a study of credit risk management practices of public and ...

A STUDY OF CREDIT RISK MANAGEMENT PRACTICES

OF PUBLIC AND PRIVATE BANKS IN INDIA

Thesis Submitted to the

GOA UNIVERSITY

For the award of the degree of

DOCTOR OF PHILOSOPHY

in

COMMERCE

by

MRS. RESHMA D. PRABHU VERLEKAR

Under the guidance of

PROF. MANOJ S. KAMAT (VVM’s Shree Damodar College Research Centre)

Principal

D.P.M.’s Shree Mallikarjun and Shri Chetan Manju Desai College

Canacona - Goa.

March 2020

I

DEDICATION

This Thesis is dedicated to

Late Shri Datta R. Prabhu Verlekar (Father)

Late Shri Tulshidas V. Kholkar (Father-in-law)

II

DECLARATION

I hereby declare that the thesis titled “A Study of Credit Risk Management

Practices of Public and Private Banks in India” submitted to the Goa

University, Goa for the award of the degree of Doctor of Philosophy in

commerce is an outcome of original and independent research work done by

me during the period 2014 to 2020 under the supervision and guidance of

Prof. Manoj S. Kamat, Principal - Shree Mallikarjun and Shri Chetan Manju

Desai College Canacona - Goa, and also that it has not formed the basis for

award of any degree, diploma, associateship, fellowship or similar title to any

candidate of this or any other University.

Date: ____________ MS. RESHMA D. PRABHU VERLEKAR

Place: Goa University

III

CERTIFICATE

This is to certify that the Ph.D. thesis titled “A Study of Credit Risk

Management Practices of Public and Private Banks in India” is a bonafide

record of the research done by “Ms. Reshma D. Prabhu Verlekar” under my

supervision and guidance, at the Goa Business School - Goa University. This

dissertation or a part thereof has not formed the basis for the award of any

degree, diploma, associateship, fellowship or similar title of this or any other

University.

Date: ______________ Prof. Manoj S. Kamat

Place: Goa University Research Guide

Principal

D.P.M.’s Shree Mallikarjun

and Shri Chetan Manju Desai College

of Arts and Commerce

Canacona - Goa.

IV

ACKNOWLEDGEMENT

It is a genuine pleasure to express my deep sense of gratitude and immeasurable

appreciation to many personalities who are directly and indirectly connected with this

research work.

I am greatly indebted to Professor Manoj S. Kamat, Principal of Shree Mallikarjun and

Shri Chetan Manju Desai College, Canacona - Goa for being my guide and contributing

immensely with his valuable suggestions, advice, motivation, encouragement and

mentoring throughout the course. It is sir’s moral support, valuable guidance, constructive

criticism and ongoing evaluation of my research work, without which it would not have

been possible for me to complete this task. He has been like a lighthouse to steer me

through all the phases of this research work and always encouraged me to present the

research findings with clarity. It was a great privilege and honour to work and study under

his guidance.

My sincere thanks goes to Dr. Sriram Padyala - Faculty of Goa Business School, Goa

University, for tirelessly witnessing my research work, periodically reviewing my progress

as an expert on the Faculty Research Committee and providing me his opinions and advice

throughout my research work.

I wish to express my gratitude to Prof. Dr. Y. V. Reddy, the Registrar of Goa University

for his wholehearted support and advice during the course of this study. I thank Prof. Dr.

K.B. Subhash, the former Dean and Head of Department of Commerce - Goa University

for providing necessary assistance and conducting the research methodology classes for the

research scholars. I am very much pleased to thank Prof. Dr. Anjana Raju – Programme

Director of Research for her valuable suggestions from time to time to improvise my

research work. I am thankful to Prof. Dr. B. Ramesh for his encouragement and

suggestions. I wish to express my gratitude to Dr. Dayanand M. S. - Vice-Dean

(Research), Goa Business School - Goa University for support and encouragement.

V

I am very thankful to the Principal of our college - Dr. Santosh Patkar, Sridora Caculo

College of Commerce & Management Studies, Mapusa for his continuous encouragement

and support. I am indebted to the Management of Saraswat Education Society for

sanctioning my study leave. I am indebted to Teaching , Administrative and Library

staff of our college for their valuable assistance and moral support throughout my research.

I am profoundly thankful to the Principal of my Research Centre, Dr. Prita Mallya -

V.V.M’s Damodar College of Commerce and Economics. I express my gratitude to the

Librarian – Ms. Manasi Rege and the other library staff of research centre for providing

me required source of secondary data. My sincere thanks to teaching and administrative

staff of the research centre for their support and help in the process of my research work.

I am thankful to the Director of Higher Education- Shri Prasad Lolayekar for timely grant

of study leave, which was of immense help in the successful completion of this research.

My special thanks and appreciation also goes to my colleague Mr. Kedar Phadke -

Faculty of National Institute of Construction Management and Research for giving a patient

hearing to my uncounted doubts and willingly helping me out with his abilities in deciding

the right approach towards selecting the statistical tools for my data analysis. His

experience and critical comments have been of immense help to me during my research

work.

My indebtedness is due to, the Librarian of Goa University Dr. Gopakumar V. and the

staff of ARPG section of Goa University for providing necessary assistance. I am thankful

to Dr. Manasvi M. Kamat for her inspiration, assistance and advice.

I am thankful to all the Respondents who contributed to the major part of my research

work by sparing their valuable time and by providing me the required information. I

express my sincere gratitude to the Resource Persons of various workshops.

VI

I am grateful to all the fellow researchers, at the Goa University – Dr. Ujwalah

Hanjunkar, Ms. P.S. Devi, Dr. Meera Mayekar, Dr. Paresh Lingadkar, Dr. Harsha

Talaulikar , Dr. Anjali Sajilal and Mr. Kavir Shirodkar for their support and timely

assistance. My sincere thanks to the faculties of Department of English – Ms. Priyanka

Pednekar and Mr. Merwyn Miranda, Purushottom Walawalkar Higher Secondary School

for the grammar check of my thesis.

I would fail in my duty, if I fall short of thanking and appreciating the valuable sacrifice of

my son Mast. Mehul Kholkar in seeing me achieve my research dream. My wholehearted

gratitude goes to my mother – Smt. Sulekha Prabhu Verlekar and my spouse Mr.

Mahesh Kholkar for supporting me in all the endeavours of my life. Also I express my

thanks to my sibling - Ms. Disha Panandikar for the assistance provided at appropriate

time. I am also indebted to my brother in law, Mr. Siddesh Kenkre for sharing his valuable

expertise in computer skills with me.

Above all, I pay my homage to the Almighty God for giving me the courage, patience and

health as well as surrounding me with people emitting positive vibes.

Reshma Prabhu Verlekar

THANK YOU.

VII

Table of Contents

Title Page

Dedication I

Declaration II

Certificate III

Acknowledgement IV-VI

Table of Contents VII-X

List of Table XI-XVI

List of Abbreviations XVII

Abstract XVIII-XX

Introduction ......................................................................................................................................... 1

1.1. Background of the Study ..................................................................................................... 1

1.2. Statement of Problem .......................................................................................................... 5

1.2.1 Research Questions ..................................................................................................... 7

1.3. Objectives of the Study ....................................................................................................... 9

1.4. Scope of the Study............................................................................................................. 10

1.5. Significance of the Study .................................................................................................. 11

1.6. Organisation of the Study .................................................................................................. 15

Theoretical Foundations and Empirical Literature ..................................................................... 17

2.1 Introduction ....................................................................................................................... 17

2.2 Theoretical Framework ..................................................................................................... 18

2.2.1 Theory Relating to CRMP and Basel III Preparedness ............................................. 18

2.2.2 Bankruptcy Models ................................................................................................... 19

2.3. Empirical Literature .......................................................................................................... 22

2.3.1. Literature on the Determinants of CRMP ................................................................. 22

2.3.2. Literature on Measurement of CRMP ....................................................................... 24

2.3.3. Literature on the Bankruptcy Models ........................................................................ 28

2.3.4. Literature on the Basel III Preparedness and Compliance ........................................ 32

2.4. Research Gap .................................................................................................................... 35

2.4.1 Gaps on Credit Risk Determinants of CRMP ........................................................... 35

2.4.2 Gaps on Measurement of CRMP............................................................................... 36

2.4.3 Gaps on Bankruptcy Models ..................................................................................... 37

2.4.4 Gaps on Basel III Preparedness and Compliance ...................................................... 37

2.5. Summary ........................................................................................................................... 38

VIII

Methodology and Techniques ........................................................................................................ 40

3.1. Introduction ....................................................................................................................... 40

3.2. Data sources ...................................................................................................................... 41

3.2.1 Primary Data ............................................................................................................. 41

3.2.2 Secondary Source ...................................................................................................... 45

3.3. Hypotheses of the study .................................................................................................... 46

3.4. Statistical Tools ................................................................................................................. 50

3.4.1 Principal Component Analysis (PCA) ...................................................................... 50

3.4.2 Reliability Test .......................................................................................................... 50

3.4.3 Descriptive Statistics ................................................................................................. 51

3.4.4 Ratio Analysis ........................................................................................................... 51

3.4.5 Pearson Correlation Coefficient ................................................................................ 51

3.4.6 Parametric Test and Non-parametric Tests ............................................................... 52

3.4.7 Regression Analysis .................................................................................................. 52

3.4.8 Robust Test ................................................................................................................ 54

3.5. Descriptions of Bankruptcy Models .................................................................................. 54

3.5.1 Altman Z-Score Model -1968 ................................................................................... 54

3.5.2 Springate Model - 1978 ............................................................................................. 56

3.5.3 Zmijewski Model (III)-1984 ..................................................................................... 57

3.5.4 Grover model (IV) -2003 .......................................................................................... 59

Determinants of Credit Risk Management Practices .................................................................. 60

4.1. Introduction ....................................................................................................................... 60

4.2. Theory and Literature ........................................................................................................ 62

4.2.1 Institutional Theory ................................................................................................... 62

4.2.2 Literature Review ...................................................................................................... 62

4.3. Data and Methods.............................................................................................................. 63

4.4. Conceptual Framework of CRMP ..................................................................................... 65

4.4.1 Risk Management Practices (RMP) .......................................................................... 66

4.4.2 Understanding Risk Management (URM) ................................................................ 66

4.4.3 Risk Identification (RI) ............................................................................................. 66

4.4.4 Risk Assessment and Analysis (RAA) ...................................................................... 67

4.4.5 Risk Monitoring and Control (RMC) ........................................................................ 67

4.4.6 Credit Risk Analysis (CRA) ...................................................................................... 67

IX

4.5. Results and Discussion ...................................................................................................... 68

4.5.1 Sample Characteristics .............................................................................................. 69

4.5.2 Identification and Classification of Determinants of CRMP .................................... 71

4.5.3 Descriptive Statistics and Correlation Analysis ........................................................ 76

4.5.4 Development of OLS Model ..................................................................................... 77

4.5.5 Comparison of the Determinants of CRMP between Public and Private Banks ....... 83

4.6. Summary ........................................................................................................................... 84

Measurement of Credit Risk Management Practices .................................................................. 86

5.1 Introduction ....................................................................................................................... 86

5.2 Literature Review .............................................................................................................. 88

5.3 Data and Methods.............................................................................................................. 90

5.4 Result and Discussion ....................................................................................................... 92

5.4.1. Measurement and Comparison of CRMP using the Lending Ratios......................... 92

5.4.2. Measurement and comparison of CRMP using Financial Ratios .............................. 99

5.4.3. Identification of Relationship between Financial Variables ................................... 106

5.5 Summary ......................................................................................................................... 109

Application and Recalibration of Bankruptcy Models in Indian Banking Sector .................. 112

6.1 Introduction ..................................................................................................................... 112

6.1.1. Operational Definitions of Bankruptcy ................................................................... 116

6.2 Review of Literature........................................................................................................ 117

6.2.1 Description of Models ............................................................................................. 120

6.3 Data and Methods............................................................................................................ 121

6.4 Results and discussion ..................................................................................................... 123

6.4.1 An Application of the Altman - 1993 Model (I) ..................................................... 124

6.4.2 An Application of the Springate Model-1978 (II) ................................................... 133

6.4.3 Application of Zmijewski Model - 1984 (III) ......................................................... 142

6.4.4 An Application of Grover Model -2003 (IV) .......................................................... 149

6.4.5 Ranking of Banks as per Bankruptcy Models ......................................................... 155

6.4.6 Comparison of Bankruptcy Score between Public and Private Banks .................... 159

6.5 Summary ......................................................................................................................... 160

Preparedness and Compliance for Basel III Norms in the Indian Banking Sector ................ 163

7.1 Introduction ..................................................................................................................... 163

7.2 Theory and Literature Review ......................................................................................... 166

X

7.2.1. Legal Theory of Finance (LTF)............................................................................... 166

7.2.2. Literature Review .................................................................................................... 166

7.3 Data and Methods............................................................................................................ 168

7.4 Conceptual framework .................................................................................................... 169

7.4.1. Conceptual Framework on Basel III Preparedness ................................................. 170

7.4.2. Operational Definitions (Basel III Compliance) ..................................................... 172

7.5 Results and Discussion .................................................................................................... 173

7.5.1 Basel III Preparedness ............................................................................................. 174

7.5.2 Compliance of Basel III Ratios by an Indian Banks ............................................... 181

7.5.3 Comparison of Capital Ratios of Public and Private Banks .................................... 185

7.6 Summary ......................................................................................................................... 191

Summary and Conclusion ............................................................................................................ 193

8.1 Introduction ..................................................................................................................... 193

8.2 Summary of Major Findings ........................................................................................... 197

8.2.1 Findings from the Determinants of Credit Risk Management Practices ................. 197

8.2.2 Findings from the Measurement of Credit Risk Management Practices ................. 198

8.2.3 Findings from the Application and Recalibration of the Bankruptcy Model .......... 199

8.2.4 Findings from the Preparations and Compliance of Basel III norms ...................... 199

8.3 Conclusion ....................................................................................................................... 201

8.4 Recommendations ........................................................................................................... 202

8.5 Implications of the study ................................................................................................. 205

8.5.1 Managerial Implications .......................................................................................... 205

8.5.2 Theoretical contributions ......................................................................................... 207

8.6 Limitations of the Study……………………………………………………………….208

8.7 Directions for future Research…………………………………………………………209

Bibliography.....................................................................................................................................211

Appendix..........................................................................................................................................223

Publications......................................................................................................................................273

XI

No. List of tables Page no.

3.1 Krejcie and Morgan Sample Size Determination 42

3.2 Information related to various sections in the questionnaire 43

3.3 CVI score calculated based on nine items from the responses of six

experts

44

3.4 Bases of discrimination of Altman Model 56

3.5 Bases of discrimination of Springate Model 57

3.6 Bases of discrimination of Zmijewski Model 58

3.7 Bases of Discrimination of Grover Model 59

4.1 Results of ranks given for different types of risk based on 116

respondents

68

4.2 The demographic profile of the 116 respondents 69

4.3 Rotated Component Matrix calculated from 31 statements from a

questionnaire

71

4.4 Extraction of the sum of Squared Loadings 72

4.5 Reliability test on 36 statements based on 116 observations 75

4.6 Descriptive Statistics of 39 respondents of private and 77 respondents

of public banks

76

4.7 The Correlation matrix calculated using 39 respondents of private and

77 respondents of public banks

77

4.8 Result of Regression calculated from 36 cases of private banks and

72 cases of public banks

79

4.9 Result of T-test of 77 observations of public banks and 39

observations private banks

83

5.1 Codes to Financial Ratios used by Bankruptcy Models 91

5.2 Result of Independent sample T-test and ANOVA of 21 public and

18 private banks for a period 2005-2017

93

5.3 Result of Independent sample T-test for liquidity analysis of time

series data from year 2005-2017and cross-sectional data of 21 public

and 18 private banks

100

5.4 Result of Independent sample T-test for profitability analysis of time

series data from year 2005-2017and cross-sectional data for 21 public

and 18 private banks

102

5.5 Result of Independent sample T-test for leverage analysis of time

series data from year 2005-2017and cross-sectional data for 21 public

and 18 private banks.

105

5.6 Result of a correlation matrix of 21 public and 18 private banks from 106

XII

2005-2017 taking into account 526 observations

6.1 Description of bankruptcy models 121

6.2 Results of the tested Altman Model on 21 public 18 private and 5

NWB

124

6.3 Altman Model Accuracy rate 125

6.4 Correlation Matrix of Recalibrated Altman Model 126

6.5 Coefficient Test of Recalibrated Altman Model 127

6.6 Results of Recalibrated Altman Model of 21 Public, 18 Private and 2

NWB

128

6.7 Recalibrated Altman Model Accuracy Rate 129

6.8 Result of Independent Sample T-test of Altman and Recalibrated

Altman Model

130

6.9 Bases of discrimination as per Robust Test 130

6.10 Results of tested Robust Test on 39 Indian banks 131

6.11 Comparison of Original and Recalibrated Altman Model with the

Robust test

132

6.12 Results of the applied Springate model on 21 public 18 private and 5

NWB

134

6.13 Model Accuracy Rate of Springate Model 135

6.14 Correlation Matrix of Recalibrated Springate Model 136

6.15 The Coefficient test of Recalibrated Springate Model 137

6.16 Results of Applied Recalibrated Model on 21 Public, 18 Private and 2

NWB

138

6.17 Model Accuracy Rate of Recalibrated Springate Model 139

6.18 Result of Independent Sample T-test of Springate and Recalibrated

Springate Model

140

6.19 Comparison of original and Recalibrated Springate Model with the

Robust test

140

6.20 Result of the tested Zmijewski model on 21 public, 18 private and 2

NWB

142

6.21 Model Accuracy rate of Zmijewski Model 144

6.22 Correlation Matrix of Zmijewski Model 145

6.23 Coefficient Test of Zmijewski Model 146

6.24 Result of the tested recalibrated Zmijewski model of 21 Public 18

Private and 2NWB

147

XIII

6.25 Model Accuracy Rate of Recalibrated Zmijewski Model 148

6.26 Result of Independent Sample T-test of Zmijewski and Recalibrated

Zmijewski Model

148

6.27 Results of the Tested Grover model on 21 Public, 18 Private and 5

NWB

149

6.28 Type I and Type II error of Grover Model 150

6.29 Correlation Matrix of Grover model 151

6.30 Results of Coefficient Test of Grover Model 152

6.31 Result of the recalibrated Grover Model of 21 Public, 18 Private and

2 NWB

153

6.32 Model Accuracy rate of Recalibrated Grover Model 154

6.33 Result of Independent Sample T-test of Grover and Recalibrated

Grover Model

155

6.34 Table showing Ranks of banks 156

6.35 Result of Independent Sample T-test in Bankruptcy score of Public

and Private Banks

159

7.1 Reliability test on 27 statements of 116 observations 174

7.2 Descriptive Statistics of 77 respondents of public and 39 respondents

of private banks

175

7.3 Correlation matrix calculated using 77 respondents of public and 39

respondents of private banks

177

7.4 Result of Regression Analysis calculated using 71 respondents of

public and 37 respondents of private banks

177

7.5 Result t-test of 77 observations of public banks and 39 observations

of private banks

180

7.6 Result of one-sample t-test of the capital ratios of 21 public and 18

private banks

181

7.7 Result of Independent sample t-testt on Cross-sectional data of 21

public and18 private banks

185

7.8 Cross-sectional data showing the result of the Paired T-test of 21

public sector banks

188

7.9 Cross sectional data showing the result of Paired T-test of 18 Private

sector banks

189

Annexure Tables

A.1 Questionnaire- Content Validity 223

A.2 Questionnaire in the Pre –PCA stage 227

A.3 Questionnaire in the Post PCA stage 228

A.4 Gross NPA to Gross Advances of 21 Public Banks for the Thirteen 230

XIV

years 2005-2017

A.5 Gross NPA to Gross Advances of 18 Private Banks for Thirteen years

(2005-2017)

231

A.6 Net NPA to Net Advances of 21 Public Banks for the Thirteen years

2005-2017

231

A.7 Net NPA to Net Advances of 18 private Banks for the Thirteen years

2005-2017

232

A.8 Total loan to total Asset of 21 public banks for 13 years for 2005-

2017

233

A.9 Total loan to total Asset of 18 Private Banks for 13 years for 2005-

2017

234

A.10 Total loan to total Deposit of 21 public banks for 13 years for 2005-

2017

234

A.11 Total loan to total Deposit of 18 private banks for 13 years for 2005-

2017

235

A.12 Total loan to Total Equity of 21 public banks for 13 years for 2005-

2017

236

A.13 Total loan to total Equity of 18 private banks for 13 years for 2005-

2017

236

A.14 Provision for NPA to Gross NPA of 21 public banks for 13 years for

2005-2017

237

A.15 Provision for NPA to Gross NPA of 18 Private Banks for 13 years for

2005-2017

238

A.16 NPA to Equity of 21 public banks for 13 years for 2005-2017 238

A.17 NPA to Equity of 21 public banks for 13 years for 2005-2017 239

A.18 CAR of 21 public banks for 13 years for 2005-2017 239

A.19 CAR of 18 private banks for 13 years for 2005-2017 240

A.20 ROA of 21 public banks for 13 years for 2005-2017 241

A.21 ROA of 18 Private Banks for 13 years for 2005-2017 242

A.22 Liquidity Analysis of Indian banks using Proxy L1 (WC/TA) for time

series data from 2005 2017

243

A.23 Liquidity Analysis of Indian banks using Proxy L1 (WC/TA) for

Cross-sectional data from 2005-2017

244

A.24 Liquidity Analysis of Indian banks using Proxy L2 (Current Ratio)

for time series data from 2005-2017

244

A.25 Liquidity Analysis of Indian banks using Proxy L2 (Current Ratio)

for Cross-sectional data from 2005-2017

245

A.26 Profitability Analysis of Indian banks using Proxy P1 (EBIT/TA) for

time series data from 2005-2017, N=273

244

A.27 Profitability Analysis of Indian banks using Proxy P1 (EBIT/TA) for


246

XV

A.28 Profitability Analysis of Indian banks using Proxy P2 (ROA) for time

series data from 2005-2017, N=273

246

A.29 Profitability Analysis of Indian banks using Proxy P2 (ROA) for


247

A.30 Profitability Analysis of Indian banks using Proxy P3 (PBIT/CL) for

time series data from 2005-2017

248

A.31 Profitability Analysis of Indian banks using Proxy P3 (PBIT/CL) for


248

A.32 Profitability Analysis of Indian banks using Proxy P4 (TI/TA) for


249

A.33 Profitability Analysis of Indian banks using Proxy P4 (TI/TA) for


249

A.34 Leverage Analysis of Indian banks using Proxy LV1 (RE/TA) for


250

A.35 Leverage Analysis of Indian banks using Proxy LV1 (RE/TA) for


251

A.36 Leverage Analysis of Indian banks using Proxy LV2 (MVE/TL) for

time series data from 2005-2017,

251

A.37 Leverage Analysis of Indian banks using Proxy LV2 (MVE/TL) for


252

A.38 Leverage Analysis of Indian banks using Proxy LV3 (TD/TA) for


253

A.39 Leverage Analysis of Indian banks using Proxy LV3 (TD/TA) for


253

A.40 Result of Altman Z-score of 21 public banks for 2005-2017 254

A.41 Result of Altman Z- score of 18 Private Banks for 2005-2017 255

A.42 Result of Altman Z-score of five NWB from 2005-2013 255

A.43 Result of Springate Model of 21 public banks for 2005-2017 255

A.44 Result of Springate Model of 18 private banks for 2005-2017 256

A.45 Result of Springate Model NWB banks from 2005-2014 257

A.46 Result of Zmijewski model for 21 public banks from 2005-2017 257

A.47 Result of Zmijewski model for18 Private banks from 2005-2017 258

A.48 Results of Zmijewski Model for NWB from 2005-2014 258

A.49 Result of Grover model of 21 Public Sector banks from 2005-2017 259

A.50 Result of Grover model for 18 Private Banks from 2005-2017 259

A.51 Result of Grover model for NWB banks from 2005-2014 260

A.52 Result of Recalibrated Altman Model of 21 Public and 18 Private

Banks

260

XVI

A.53 Result of Recalibrated Springate Model of 21 Public and 18 Private

Banks

261

A.54 Result of Recalibrated Zmijewski Model of 21 Public and 18 Private

Banks

262

A.55 Result of Recalibrated Grover Model of 21 Public and 18 Private

Banks

263

A.56 Questionnaire (Part 2): Preparedness in Basel III Implementation 264

A.57 Result of Tier I Capital Ratio from 2009-2018 267

A.58 Result of Tier II Capital Ratio from 2009-2018 268

A.59 Result of CAR from 2009-2018 269

A.60 Result of Leverage Ratio from 2009-2018 271

XVII

List of Abbreviations

AB Anticipated Benefit NPA Non-Performing Asset

AC Anticipated Cost NSFR Net Stable Funding Ratio

AI Anticipated Impact NWB Non - Working Banks

AR Availability of Resources OLS Ordinary Least Square

BP Basel III Preparedness PBIT/TA

Profit before Interest and Tax to

Total Asset (PBIT/TA)

CA/CL

Current assets / Current

Liabilities PBT/CL

Profit Before Tax to Current

Liabilities

CAR Capital Adequacy Ratio PCA Principal Component Analysis

CCB Capital Conservation Buffer RAA Risk Assessment Analysis

CCCB Counter Cyclical Capital Buffer RBI Reserve Bank of India

CRA Credit Risk Analysis RE/TA Retained Earnings to Total Asset

CRAA Credit Risk Assessment and

Analysis

RI Risk Identification

CRI Credit Risk Identification RMC Risk Monitoring Control

CRMC Credit Risk Monitoring Control RMP Risk Management Practices

CRMP Credit Risk Management

Practices ROA Return on Asset

CRU Credit Risk Understanding RWA Risk Weighted Assets

EBIT/CL

Earnings Before Interest and

Tax/Current Liability SEBI

Security Exchange Board of

India

EBIT/TA

Earnings Before Interest and Tax

to Total Asset TD/TA Total Deposit to Total Asset

EC Expected Challenge TI/TA Total Income / Total Asset,

FD Financial Distress TL Tolerance Level

LCR Liquidity Coverage Ratio TL/TA Total liabilities / Total Assets

LR Leverage Ratio URM Understanding Risk Management

MVE/TA

Market Value of Equity to Total

Asset VIF Variable Impact Factor

NI/TA Net Income / Total Assets WC/TA Working Capital to Total Asset

XVIII

ABSTRACT

Credit Risk is a significant threat for banks as the value of any organisation is measured by

its creditworthiness. Given the above, effective Credit Risk Management Practice (CRMP)

needs to be followed by banks to reduce this risk. The CRMP of public and private sector

banks in the Indian banking sector significantly differs as shown by past studies. In the

spirit of these studies, the present research assesses and compares the CRMP of the public

and the private banks in India, to check the extent and magnitude of variations. It is crucial

to study the lending and financial ratios of the banks as piling of NPAs adversely affects the

financial performance of the banks. Therefore the present study measures and compares the

credit risk management practices of public and private banks using lending ratio and

financial ratios.

Since ineffective CRMP may result in the bankruptcy of banks, the prediction of

bankruptcy by applying the bankruptcy model in the Indian banking context plays a

significant role. Due to some criticism of the models and the error rates that occurred in the

application of the model in present study, the current study aims at recalibration of the

applied models. Effective CRMP is possible due to precise control exercised by regulations

and supervision (Basel III norms). These controls pose challenges to bankers, which drives

the attention of bankers towards adequate preparedness needed for Basel III

implementation. Therefore the present study assesses the level of preparedness and

compliance of Basel III norms by public and private sector banks.

The present study used Principal Component Analysis (PCA) to determine the factors

influencing Credit Risk management Practices (CRMP) of banks. Descriptive statistics

were used to provide a summary of variables (CRMP determinants and Basel III

dimensions). In ratio analysis, lending and financial ratios were used to measure the credit

risk. Pearson’s Correlation Coefficient is used to find the relationship between financial

variables such as liquidity, profitability and leverage. An independent sample t-test is

conducted to compare the difference in credit risk determinants of CRMP, lending and

XIX

financial ratios, bankruptcy scores, Basel III preparedness and the Tier I, Tier II and CAR

between public and private banks in India. The test of ANOVA, along with a post hoc test

is conducted to find which bank reveals a statistically significant difference in the group.

One sample t-test and paired t-test was used on Basel III ratios. The robust test is conducted

to measure the financial health of the bank in the year 2017.

Regression Analysis such as, Ordinary Least Square (OLS) regression is used to determine

the statistical impact of the credit risk determinants on CRMP. Multiple linear regression is

used to measure the factors influencing the impact of the Basel III preparedness. Present

study applied Altman Z-score, Springate, Grover, and Zmijewski's model for assessing the

bankruptcy risk and also recalibrated these models by changing its coefficients through

multiple linear regression analysis.

In measuring the impact of the credit risk determinants on their respective CRMP for

public and private banks, regression results reveals that explanatory variable Credit Risk

Understanding and Credit Risk Assessment and Analysis (CRAA) among the four

components are most influential in the contribution of CRMP of private banks. The

regression model fits the data well for private banks compared to public banks in India. The

results of the Independent sample t-test show a significant difference in the credit risk

determinants between public and private banks.

In measuring the CRMP using lending ratios, it is observed that the certain lending ratios

are high in case of public banks. In measuring the CRMP using financial ratios, the result

states that both public and private banks experience low liquidity ratios and the leverage

ratios are lower for public banks and higher for private banks. The profitability ratios of

private banks show a fluctuating trend, and public banks show a decreasing trend.

The risk of bankruptcy of public and private banks was analysed by applying four

bankruptcy models. It is found that the results are not appropriate in case of two models.

The ranking results as per bankruptcy models shows that Bank of Baroda and Nainital Bank

XX

are in the first position (most efficient, and credit-risk safe) among public and private bank

respectively.

In measuring the statistical impact of factors of Basel III Preparedness, the regression

results shows that the factor ‘Anticipated Benefit’ is the most crucial followed by

‘Anticipated Impact’ in Basel III preparedness. The result of one-sample t-test shows that

private sector banks commenced disclosure towards compliance (minimum common equity

Tier I) ratio from 2018 while the public sector banks started compliance from 2014. The

private as well as public banks showed higher compliance of the Tier 1 capital ratio and

capital adequacy ratio, compared to a limit prescribed by Basel III norms.

Based on these findings, a broad conclusion can be drawn that private banks are generally

more efficient in terms of following credit risk management practices. The lending ratios

are low for private banks, indicating a better financial position. The bankruptcy study

concludes that the recalibrated Altman and the Grover model perform better than the

original model. In contrast, the original Springate and the Zmijewski model showed

improved accuracy over the recalibrated model. Further, it can be concluded that Indian

banks are well prepared to implement Basel III norms, as the Basel III ratios are maintained

by the public and private banks are at a much higher level than prescribed by RBI.

The study contributes to the literature in terms of the determination of credit risk

determinants of CRMP using the Principal Component Analysis (PCA) technique and

recalibration of bankruptcy models. The uniformity assumption of Institutional theory does

not apply in our study on account of differences in the understanding among employees on

the different aspects of credit risk and in the assessment and monitoring and control policies

of credit risk followed by the public and private banks in India. The private banks show a

negative relationship of factor, ‘Anticipated Cost’ with Basel III preparedness. These

results support the Legal Theory of Finance.

Chapter I Introduction

1

Chapter I

Introduction

1.1. Background of the Study

The health of a nation’s economy is largely dependent on a sound financial system. The

financial system provides necessary funds for the nation’s growth and development. In the

financial system, the banking sector occupies the most important position as it holds the

savings of the public provides a means of payment for goods and services and finances the

growth of business. The banks also play a major role in planning and implementing a

country’s financial policy (Itanisa 2016). Thus, a banking sector needs to be strong and

resilient for the strong economy of nation.

The word bank is derived from the French word banco or "bancus" or banc which means a

bench (Jhingan, 2001). The early Jews in Lombardy conducted the banking business by

sitting on benches. The banking is as old as the authentic history and the origins of modern

commercial banking are traceable in ancient times. According to Shekhar and Shekhar

(2008), in ancient Greece around 2000 B.C., the famous temple of Ephesus was used as

depositories for people’s surplus fund and these temples were the centres of money lending

transactions. The Priest of these temples acted as financial agent.

The period of banking reforms began in the year 1992, which provided a basis for looking

into the future of the Indian banking system (Kumar, 2014). During the last two decades,

there have been several reforms at the global and national levels that had an impact on the

operating environment of the banks. These reforms are in the form of, the introduction of

new players, new institutions, and new instruments. The introduction of new financial

products and services and the huge strides in technology and communication have brought

significant changes in the working of banks (Mallya 2012). These changes are gradual

liberalization of Indian banking, deregulation of interest rate, global competition, new

financial reforms, virtual banking, diversification of activities, retail character of banking,

liberalization of Foreign Direct Investment (FDI), active role of banks in upliftment of


2

socio-economic development of the country, implementation of new Basel requirement

etc. Given these above growing complexities in the bank’s business and the dynamic

operating environment, has led the banks to face a new or higher risk.

Risk is understood as “anything that can create hurdles in the way of achievement of certain

goals”. Therefore, the requirement of the risk management perceives a paramount

importance for the banks. According to Rao and Rentala (2012) the dynamic operating

environment of banks, have pushed the risk management to the forefront of the financial

landscape.

Risk management is understood as “adopting a practice of identifying the potential risk in

advance, analysing them and taking precautionary steps to reduce the risk”. Risk

management is significant because, today’s banks run its operation with two goals in mind

i.e. to generate profit and to stay in the business (Marision 2005), In other words, although

avoiding failure is the principal reason for managing risk but financial institution also have

broader objective of maximizing Risk Adjusted Rate of Return on Capital (RAROC).

According to Rao and Rentala (2012), the importance of risk management in banks is

because of their effect on the financial crisis and determining its role in the survival growth

and profitability of the bank. The risk management provides reasonable assurance of

achieving firm’s goals, compliance with applicable laws and regulations. It also helps in

encouragement of specific investment and decreasing the earnings volatility.

The risk management in banks witnessed a substantial change, due to emergence of

regulations evolved from the global financial crisis. As per these regulations the banks

need to retain higher standards for capital, leverage, liquidity, funding requirements and

risk reporting (Phillipe 2015). Although a variety of regulatory and supervisory authorities

have set norms for managing risk, still it remains a challenge for banks, as managing risk

depends on bank’s ability and stability. The process of risk management is complex and

difficult due to absence of risk culture in many banks. As the bank moved into a new high

powered world of financial operations and trading, with a new risk, there is a need for the

more sophisticated and versatile instrument for risk assessment, monitoring and controlling

risk exposure (Nandi and Chaudhary, 2011). The banks are expected to perform risk

management practice by designing effective risk management policies, designing a


3

procedure for identification and assessment of risk etc. Indian banking industry requires a

combination of new technologies, better processes of credit and risk appraisal, treasury

management, product diversification, internal control and external regulation for better risk

management (Adamson 2012).

Banks face three types of risk i.e. credit risk, market risk, and operational risk. Among

these risks, credit risk is the most important because it has a substantial effect on the return

on investment of the banks. According to Ngoroge and Ngahu (2017) Credit risk is a

significant threat for the banks as the value of any organization is measured by its

creditworthiness. Given the above, effective Credit Risk Management Practice (CRMP)

needs to be followed by banks to reduce this risk. The CRMP assumes establishing a

suitable credit risk management environment by identifying, measuring and managing

credit risk and covers all aspects such as identification of credit risk, understanding what

determines it and its measurement using various tools. The CRMP of public and private

sector banks in the Indian banking sector significantly differ as shown by the studies of

Thiagarajan (2011), Goel and Rekhi (2013), Kattel (2016) and Pourkeiki (2016). In the

spirit of these studies, the present research assesses and compares the CRMP of the public

and the private banks in India, to check the extent and magnitude of variations.

An ineffective CRMP may result in the bankruptcy of banks therefore, the prediction of

bankruptcy may help stakeholders to obscure themselves from the risks arising there from.

There are many models available for bank failure predictions. Literature survey shows that

the majority of international bank failure prediction employs the Altman-Z score model,

Grover model, Springate model and Zmijewski model. The present research aims to apply

all these models in the Indian banking context. Some critics of the above mentioned models

opine that when the original model is applied to a more recent sample, its predictive power

may be deficient and the risk of bankruptcy may be over predicted. Due to this fact, the

present study aims at the recalibration of the above models and compares the results from

the recalibrated and the original model, to judge its accuracy.

The effective CRMP is possible due to precise control by authorities in terms of regulations

and supervision. The concern regarding credit risk management at an international level


4

began in 1974, with the creation of the committee for regulation and supervision of banking

practices known as the Basel Committee on Banking Supervision (BCBS). Due to this

concern, the formal framework for bank’s capital structure was evolved in 1988 with an

introduction of Basel I. Basel I primarily focused on credit risk and appropriate Risk

Weighted Asset (RWA). The RBI implemented Basel I in the year 1992 in India. Basel I

was criticized for its rigidity of the "One Size Fit" approach and the absence of risk

sensitivity in capital requirements (Jayadev 2013). Due to these deficiencies in Basel I

accord, the BCBS introduced Basel II. Basel II is a much more comprehensive framework

of banking supervision when compared to Basel I (Sarma 2007). It was built on three

mutually reinforcing pillars such as Minimum Capital Requirements (Pillar1), Supervisory

Review (Pillar 2) and Market discipline (Pillar 3). Basel II was criticized for not

considering the liquidity and leverage risk in capital regulation and also failed to address

the systemic risk (Jayadev 2013). It was also criticized for its inability to prevent the

financial crisis; hence in response to the 2007-09 global financial crisis, BCBS issued Basel

III norms.

Basel III focused on four vital banking parameters viz. capital, leverage, funding, and

liquidity. Basel III introduced different aspects to control risk such as Counter Cyclical

Capital Buffers (CCCB), Capital Conservation Buffer (CCB), Leverage Ratio (LR),

Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR). Basel III poses

several challenges to the bankers; risk managers, finance managers, and Basel III program

managers are under pressure to implement Basel III. The key challenges for these managers

will be deciding how best to implement a solution that allows them to comply with Basel

III (Whitepaper). These challenges drive the attention of bankers towards the adequate

preparedness needed for Basel III implementation. This prompts the present study to assess

the level of preparedness by public and private sector banks of Basel III norms.

India should transit to Basel III because of several reasons. By far the most important

reason is that, as India integrates with the rest of the world, as increasingly Indian banks go

abroad and foreign banks come on to our shores, we cannot afford to have a regulatory

deviation from global standards. Also, as per RBI guidelines, Basel III need to be


5

implemented in phases, due to this Basel III is not implemented fully by some of the banks.

This drives the attention of the researcher, to study the compliance of Basel III.

1.2. Statement of Problem

Over the current years, there has been an increased number of credit-related problems in the

banks due to increased Non-Performing Assets (NPAs). Moreover, the piling of NPAs of

public and private banks adversely affects the profitability, liquidity and leverage position

of banks. It is a worrying fact that public sector banks report higher NPAs than the private

banks. This NPA problem may be the result of ineffective CRMP. The result of ineffective

CRMP may lead to the breakdown of the whole banking system (Ishitiq 2015).

The other problem that guides this study is that recognising of exact determinants of CRMP

is difficult given the different factors that govern CRMP in different countries. The factors

applicable in some context/nations may not apply to Indian banks due to the difference in

the economic background and the Central Bank’s policies of the country.

The credit risk management is also a significant challenge for an Indian banking system due

to the constant heat of competition that banks face from foreign banks. This fact compels

the banks to look into its profitability, liquidity and leverage position, as the financial

position is positively affected by these three variables, or one is achieved at the cost of

others. Since the available literature state an inverse relationship between liquidity and

profitability and also between profitability and leverage, an effort is made to judge the

relation in the Indian context.

In today's era, the most common problems of the Indian banking sector are bank frauds and

hike in NPAs leading the banks towards state of bankruptcy. The stakeholders are

concerned about forecasting the likelihood of financial distress to respond before the event

takes place. Thus a variety of scoring models that can predict the bank failure have been

developed. Further, although the scoring model is one of the powerful tools used for

bankruptcy prediction, it also has its loopholes. Some authors question the model’s capacity

for sound default detection and argue that the bankruptcy prediction model is less accurate


6

when it is applied to a different environment, period and industry as compared to the reason

for which it was initially devised. The problem is that the prediction models used in this

research might have been for other industries rather than banking or may be outdated. Thus

the models may not provide credible results under all conditions and may need some kind

of calibration. This may lead to another problem. There may be a difference in the

predictive ability in the original and the recalibrated model, as a result both these models

may not match with each other due to change in coefficients. This demands a need for

comparing the results of both the models with the robust test, to decide the accuracy of the

models followed.

Furthermore, due to the above loopholes in the models, the reliability of the model is

doubtful; hence using a single model may not give a perfect result. Therefore, more than

one model needs to be used to predict bankruptcy. According to Kleinert (2014), single

bankruptcy prediction model faces limitations and use of multiple bankruptcy prediction

models improved the prediction accuracy.

The Basel III implementations need proper preparations as it involves huge cost and

resources in terms of capital, manpower and technology. Further, Basel III preparations are

affected by several factors i.e. Anticipated Benefit (AB), Anticipated Cost (Anticipated

Cost), Anticipated Impact (AI), Expected Challenge (EC), Availability of Resources (AR),

Education Level (EL) etc. Thus, there is a need to find the most significant factors

influencing its preparedness. The other research problem is related to compliance of Basel

III implementation by Indian banks. RBI has given the transitional schedule for

implementation of these new norms from the year 2013-2019. In this schedule, the

minimum requirement for Basel III ratios (CAR, Tier I, Tier II, leverage and liquidity ratio)

required to be followed by individual banks are prescribed by RBI. A reference to annual

reports show that some of the banks have recently moved towards Basel guidelines and are

still following standard approaches, but still many do not follow the expected advanced

approaches. This explains the need to study the compliance of Basel III norms in the Indian

context.


7

The other research problem is related to the pre and post-implementation phase of Basel III.

Capital ratios of the banks differ in the pre and post-implementation phase of Basel III.

Capital ratios of the banks need to be enhanced in the post-implementation of Basel III in

order to meet the higher requirements. Therefore the present study assesses Basel III ratios

in these two phases.

1.2.1 Research Questions

The research question underlying this study is recognizing the exact determinants of

CRMP. These determinants are not standard and as per the literature review it is observed

that, the factors governing the CRMP differs from country to country. In the past, some of

the studies undertaken in one country have used three determinants whereas some other

studies have used five determinants in other country. Thus, determinants applicable in other

context or country may not apply to the Indian banks. Therefore, the research question

addressing the above issue states, what are the determinants of Credit Risk Management

Practices in India? These different determinants have certain impact on the CRMP as per

the reviews of past studies. Therefore, a further question arises as, what is the impact of

these determinants on the CRMP?

The primary function of public and private banks is accepting deposits for lending. In

performing a lending function, both these banks undertake a heavy credit risk. Also, these

banks follow an equivalent procedure and safety precautions before sanctioning a loan and

are on equal foot of bearing the credit risk. Considering this fact, it is understood that there

is uniformity in the risk management practices among the public and private banks.

However it is a worrying fact that, public sector banks report higher NPAs than the private

banks. This triggers the researcher to find an answer to a research question such as, whether

there is a difference in the credit risk determinants of CRMP between public and private

banks?

Financial performance indicators such as, liquidity, profitability and leverage measured

through financial ratios helps public and private banks in knowing its financial position to

manage its credit risk. The financial position of both these banks may not be uniform as the

pattern and extent of liquidity, profitability and leverage differs between public and private

banks due to their nature of operations. Therefore, the research question arises, whether


8

there is significant difference in the financial ratios between the public and private banks?

The research gap from the literature review highlights the fact that the relationship between

liquidity profitability and leverage has been studied in other countries, but very few similar

studies have been carried out in the Indian banking sector. Therefore, the research question

arises to find - Whether there exists a particular trend and relations among liquidity,

profitability and leverage of Indian banks?

A study on lending ratio helps to understand the lending and NPA position of the banks.

As per the studies of Goel and Rekhi (2013) and kattel (2016) the credit risk management

practices of public and private banks differ. This difference in the CRMP may be on

account of various factors and one amongst them may be lending ratios. Due to this, a

question arises, whether there is a significant difference in the lending ratios between public

and private banks?

The bankruptcy models applied in the present study were originally developed at a different

time period, with different samples and were affected by the country’s economic

environment. Therefore the predictive power of these models may differ. Thus the question

arises as to how accurate are the bankruptcy models? If the predictive power of the model is

affected after its application to the Indian banking sector, the next question arises as to,

whether there is a need for the recalibration of these models to suit the current economic

environment and our specific regulatory environment of the country? After recalibration

and application of these models to the Indian banking sector, the study tries to answer the

question –Which financial distress model is accurate to predict bankruptcy of banks?

The financial position of the bank is assessed using bankruptcy score. Based on these

bankruptcy scores which Indian banks could be ranked as the best, and the worst? Lastly,

based on these scores, it further tries to answer a question whether there is significant

difference in the bankruptcy score of public and private banks?

It is crucial for the Indian banks to have good preparations for the Basel III implementation

in order to meet international standards. Therefore the question arises as to verify, whether

the Indian banks are prepared for the implementation of Basel III norms? The Basel III

preparations are affected by several factors as per the studies of Al- Tamimi (2008) and


9

Kapoor and Kaur (2015), with this raises the question as to, what are the significant factors

contributing towards Basel III preparedness? The Basel III preparations of public and

private banks may differ due to their nature of operation, availability of resources, etc.

Thus, further question arises whether there is statistical difference in the Basel III

preparations of public and private banks?

Basel III implementation poses a variety of challenges in terms of enhanced capital

requirements, design of comprehensive liquidity management framework, data acquisition

and software and hardware development. Therefore, some of the banks may not follow

these norms due to cost factor thus the question arises as to Whether Indian banks comply

with the minimum requirements of Basel norms stipulated by the RBI? The post

implementation period of Basel III requires banks to maintain higher capital to meet the

norms. Therefore, the question arises as to what is the difference in the Basel III ratios of

banks in the pre and post-implementation phase of Basel III? and whether banks have

followed required capital requirements in the pre and post-implementation phase of Basel

III?

1.3. Objectives of the Study

Following are the four objectives o this study:

1. To identify the factors (determinants) affecting the credit risk management

practices, measure its individual impact and compare them between public and

private banks.

2. To measure and compare the credit risk management practices of public and private

banks using lending ratios and financial ratios.

3. To analyse the risk of bankruptcy of public and private banks using the bankruptcy

models, judge their accuracy and rank the banks based on its score.

4. To assess the preparedness and compliance of the Basel III accord of public and

private banks in India.


10

1.4. Scope of the Study

The present study has been undertaken primarily to examine the CRMP of public and

private banks in India. Banks face three types of risk – Credit risk, Market Risk and

Operational risk. Among these risks credit risk is the most important one, because it has a

substantial effect on the return on investment of the bank. Therefore, present study is

restricted to credit risk, ignoring the other risk. There are different methods to measure

CRMP, however, the present study covers two methods i.e. credit risk measurement tools

and identifying determinants of credit risk.

The current study covers banks from two categories public sector banks and private banks.

There are 27 public sector banks in India, consisting of 19 Nationalised banks , six State

Bank group, Industrial Development Bank of India (IDBI) and Bhartiya Mahila Bank. The

present study covers 19 nationalized banks, State Bank of India and IDBI bank making it

twenty one public sector banks. There are 29 private banks (old and new private sector

banks) operating in the country, however the present study covers 18 private banks. The

type and the number of banks are selected based on the availability of data and the

consequences of time constraints.

Present study assesses the risk of bankruptcy of public and private banks using bankruptcy

models. There are many models available for bank failure predictions such as Standard and

Moody’s financial ratios, Beaver model, Altman Z- score model, Ohlson’s model CAMEL

model, Grover Model, Springate Model, Neural Network and Zmijewskis Model to predict

the health of the bank. Among these models, the present study focuses on the most

frequently used model in past studies. These models are the Altman-Z score model, Grover

model, Springate model, and Zmijewski model.

The accuracy of these models could be judged based on various factors; however present

study covers Type I and Type II errors, robust test and the ranks given to the individual

banks. The secondary data for the bankruptcy study is collected for thirteen years period

from the year 2004-5 to 2016-17. The data period consists of two phases, the pre-

implementation and the post-implementation phase of Basel II and Basel III norms. This is

the period where the Basel committee put norms on the banks and the effect of all these


11

norms is reflected in the financial statements of the banks, which are more reliable and will

reduce measurement errors.

Although a study concerning Basel III norms covers many aspects such as impact,

challenges, cost- benefit analysis etc, however, due to time and resource constraints present

study is limited to the assessment of the preparedness and compliance of Basel III norms

between select public and private banks in India.

A vast and intensive study is possible with respect to full compliance of these norms by the

Indian banking sector, but based on the data availability and time factor, present study,

focuses on compliance of Basel III ratios (Minimum Common Equity Tier I capital, Tier I

capital, Tier II capital, Capital Adequacy Ratio and leverage ratios). The other ratios such

as Liquidity ratio and Net Stable Funding ratio is not covered due to lack of data in the

annual statements.

1.5. Significance of the Study

The credit risk is a significant threat for banks as it has a direct impact on the profitability

of the bank. A study by Ishitiq (2015) found a positive relationship between the

performance and risk management. This relationship throws light on the paramount

rationale of management of credit risk. A study by Rehman (2016) states that credit risk

management is getting more attention, especially after the credit crunch of 2007-2009.

According to Ngoroge and Ngahu (2017) effective risk management can bring far reaching

benefits to all organisations, whether large or small, public or private sector. Therefore

efficient CRMP is one of the vital strategy bank needs to make by diverting its resources

for the growth and survival of banking business. This strategy of CRMP can give many

advantages to the financial institution in terms of financial performance, competitive

advantage, optimum utilisation of resources, improved service delivery, growth and

survival, reduced waste and developing policies for regulatory bodies.

The information about CRMP will assist in developing policies for regulatory bodies.

According to Onyano (2010) policy makers in the financial industry will use this study in

understanding the extent to which the banking industry is exposed to credit risks. This will


12

guide them in designing the best practices for the financial institution to manage credit risk.

The findings will further enhance the managers understanding of the key systems that will

improve the banks performance. Proper CRMP is also important because Ishitiq (2015)

found in its study that, there is positive relationship between the performance and risk

management. In addition to the regulatory requirement, the CRMP is valuable and relevant

in order to increase the value of the firm (Ishitiq 2015).The value of financial institution is

judged based on its financial performance. The credit risk management is found to be one

of the determinants of returns of banks stock (Hussain and Ajmi, 2012). Thus the credit risk

management will maximize the banks risk adjusted rate of return by adjusting credit risk

exposure within acceptable limit.

It is crucial to study the lending and financial ratios of the banks as piling NPAs adversely

affects the financial performance of the banks. A study on measurement of credit risk using

lending and financial ratios is significant to managers, investors, creditors, government and

regulators. Managers can depend on these ratios to have the right choice in the decision-

making process and efficiently adopt new policies and a new management system. These

ratios help the investors to predict the future situation, earning capacity of their invested

companies and the safety of their investments. The ratio analysis assist the creditors in

knowing the ability of a company to pay off its debt and the company's potential in the

future to keep lending it. The ratios aid government to compare individual bank with the

industry ratio to provide financial support for the weaker banks and could help the bank

regulators to evaluate and compare banks performance.

The bank health needs attention because banking is a business entity that collects funds

from the public in the form of savings and Chanel’s to the community in the form of credit.

This process attaches the risk that the borrower may default on his application. This

uncertain economic environment during recent years has stressed the importance

bankruptcy prediction. In such a condition it becomes necessary to assess the financial state

of the banks using distress prediction model to estimate the business risk. This study will

help the banks to understand its financial position much in advance and to avoid financial

distress.


13

A proper prediction of bank bankruptcies is significant as the financial sector is very crucial

for a growing economy, and any variations in its performance can affect the economy either

way (Chaudhary and Nandi, 2011). The bankruptcy information is useful to the

management to undertake precautions and essential to the auditor to report on the viability

of the firm. Investors are interested in the bankruptcy model to avoid the risk of loss from

an unsuccessful investment, and the lenders are interested in taking decisions on the

granting of loans to the bank. The government agency has an interest in seeing the signs of

bankruptcy to provide support for its revival. Score value calculated using models is useful

to banks to demand loans from RBI or any other funding agency as per Pradhan (2014).

Thus the financial health of banks can help each beneficiary and it can be applied in a wide

variety of situations. Bankruptcy study will provide information on variables that will

influence the health of a bank (Stingar and Warstuti 2014). In total, if bankruptcy could be

predicted with reasonable accuracy ahead of time banks could better protect their business

and could take action to minimize risk and loss of business perhaps even prevent

bankruptcy (Ramage and Pongstal 2004).

The progress report on the implementation of Basel III regulatory framework (2013)

reflects that RBI has taken the steps for convergence of the International Basel standards

and the Indian banks need to transit to Basel III because of several reasons. The most

important reason being, the Indian banks make it presence in the world and foreign banks

come on to our shores. Hence the Indian banks cannot afford to have a regulatory deviation

from global standards. Considering this fact, preparedness of Indian banks for the

implementation of Basel III norms assumes a high level of significance. Further, it is also

significant because it poses a variety of challenges to the banks. These challenges drive

them to develop technological infrastructure and human resources to cope up with risk

environment.

A major challenge for the Indian banks is the implementation of regulations as per the

transitional schedule specified by RBI. In the post-implementation period of Basel III

norms, the banks need to infuse an additional capital, to maintain quantity, quality,

consistency, and transparency of capital as per set norms. According to Athira and Shanti

(2014), banks require additional capital of 5 lakh crores and further the public sector banks


14

require common equity of 1.4 - 1.5 trillion to meet Basel III requirements. Apart from this,

Basel III norms require banks to follow the Capital Conservation Buffer and Counter

Cyclical Capital Buffer for the better risk management framework. All these stipulations

trigger the banks to enhance their capital base by balancing the profitability aspects of

banks. These facts trigger the significance of Basel III preparations.

Due to the above challenges, RBI introduced phase in arrangement for the implementation

of Basel III norms from 2013-14 to 2018-19, which will also align full implementation of

Basel III in India closer to the internationally agreed date of January 1, 2019. Thus, all

banks are proceeding at their own pace for meeting the Basel III requirements. Therefore, a

study on the compliance of Basel III norms as per the transitional schedule specified by

RBI perceives paramount importance.

The evolution of Basel III norms has taken place due to the impact of the financial crisis in

2008. This crisis affected the U.S. economy and other economies very badly as the capital

was dried up during this crisis. However, the impact of the same was less on the Indian

economy, maybe due to the better capital position of the Indian banks in this period. This

period was known as the pre-implementation period of the Basel III norms and in this

period the capital position of the Indian banks was sufficient to face the financial crisis. The

post-implementation period of Basel III norms shows an improvement in the minimum core

capital stipulations. Thus there is difference in the capital ratios in the pre and post-

implementation period of Basel III norms. Therefore, a comparative study on the Basel III

capital ratios of the public and private banks in the pre-implementation phase and also in

the post-implementation phase achieves due importance.

The present study is also significant as it makes valuable contribution to the current

academic literature in the area of banking and also proposes methodologies as well as

practical additions to the key area. Furthermore, it will act as a source of reference and

background for other research in this area plus findings will stimulate the future research.


15

1.6. Organisation of the Study

Our study titled “A Study of Credit Risk Management Practices of Public and Private

Banks in India” is organized into eight chapters, and is arranged as follows:

Chapter I: Introduction: This chapter throws light on the background of the study,

statement of the problem, research questions, objectives of the study, scope of the study,

significance of the study, and organization of the study.

Chapter II: Theoretical Foundations and Empirical Literature: This chapter explores

exhaustive and comprehensive literature on Credit Risk management Practices (CRMP),

bankruptcy models and compliance and preparedness of Basel III norms. The review of

literature allows understanding the gaps in prior studies.

Chapter III: Methodology and Techniques: This chapter begins with specifying details

of data sources such as the primary data and secondary data. Further, it covers in detail

hypotheses of the study and the statistical tools used for data description, data validity, and

data analysis. It also covers elaborations on the recalibration of bankruptcy models and

robust tests.

Chapter IV: Determinants of CRMP: This chapter covers the identification of the credit

risk determinants of CRMP, measurement of the impact of these determinants on CRMP,

and the comparison of these determinants between public and private banks in India.

Chapter V: Measurement of CRMP: This chapter measures the CRMP using lending

ratios and financial ratios. Further, it also finds the relationship between liquidity,

profitability and leverage variables.

Chapter VI: Application and Recalibration of Bankruptcy Model: This chapter applies

the Altman Z-score, Springate, Grover, and Zmijewski's, model for assessing the

bankruptcy risk of select public and private banks in India. It also recalibrates these models


16

and further judges the accuracy of the models and compares the bankruptcy scores of public

and private banks.

Chapter VII Preparedness and Compliance of Basel III Norms: This chapter assesses

the preparedness and compliance of Basel III accord in the Indian banking industry. It

measures the impact of factors of Basel III preparedness on the relevant variables. The

compliance part covers the comparison of actual Basel III ratio of banks with minimum

ratio specified by the RBI. Additionally, it presents the comparison of the Basel III ratios

between public and private banks in the pre-implementation phase and also in the post-

implementation phase of Basel III. Further, it covers the comparison of these ratios before

and after the adoption of Basel III norms.

Chapter VIII Summary and Conclusion: In the concluding chapter, the results and

findings are summarised based on each chapter. Further, it also covers the

recommendations to the stakeholders, contributions of the study in terms of managerial and

theoretical, and finally, it includes directions for future research.

Chapter II Theoretical Foundations and Empirical Literature

17

Chapter II

Theoretical Foundations and Empirical Literature

2.1 Introduction

In 1980s literature put heavy emphasis on a conceptual study of banks and their functions.

In the year 1992 Narsimah committee introduced banking reforms and various measures

were put forth on the banks in the form of capital adequacy norms, phase out of Non-

performing Assets (NPA) etc. This has triggered the researchers and financial writers to

develop a literature on the compliance of banking reforms. Over a period of time, other

regulations came into force such as prudential norms and Basel norms which also made

ample contributions in the banking literature. The reason for the evolution of these norms is

due to inadequate CRMP followed by the banks leading them on the path of bankruptcy.

Thus present study covers CRMP, bankruptcy models and banking regulations such as

Basel III norms. Although, most of the banking literature acknowledges risk management,

bankruptcy models and Basel norms, there has been relatively little research undertaken on

comparison of CRMP between public and private banks in India, assessment of the

financial health of the banks using bankruptcy models, and preparedness and compliance of

Basel III norms.

The review of studies concerning CRMP is helped to understand the credit risk

determinants affecting CRMP and the tools used by banks to measure credit risk. The

literature on bankruptcy predictions is needed to understand the conceptual clarity and

working of bankruptcy models. Similarly, reviews on Basel III norms are required to

understand the mechanism used in other studies for Basel III preparedness and Compliance.

The objective of this chapter is to explore the available literature on CRMP, bankruptcy

models and preparedness and compliance of Basel III norms. This chapter is divided into

three parts. Part I discusses some important theories and descriptions on bankruptcy

models. Part II reviews the studies on credit risk determinants and measurement of CRMP,

bankruptcy models, preparedness and compliance of Basel III norms. Part III focuses on the

research gap and summarises the whole chapter.


18

2.2 Theoretical Framework

The study relies on the theories that may have underpinnings to the issue of CRMP and

Basel III preparedness. It further comprises a brief description of bankruptcy models such

as the Altman Z-score model, Springate model, Zmijewski model, and Grover model.

2.2.1 Theory Relating to CRMP and Basel III Preparedness

The researcher resolved the controversy on understanding the difference in the credit risk

determinants of CRMP between public and private banks, with the aid of Institutional

Theory. The other issue relating to understanding the significant contributor to the Basel

III norms preparedness was resolved with the help of Legal Theory of Finance.

2.2.1.1 Institutional Theory

The Institutional Theory (the IT) is based on understanding the theoretical framework in

context to the organizational phenomena. The IT considers the process by which structures,

schemes, rules, norms, and routines become established as authoritative guidelines for

social behaviour as per Scott (2014). This theory describes norms, ideologies, values, to be

uniformly followed by all, to run their organizations. The studies by Collier and Woods

(2011) and Hudin and Hamid (2014) used this theory in describing the concept of risk

management implementation. Their studies put forward that institutionalization exists when

the risk management activities in most of the institutions become highly equivalent. This

equality can be gained through the coercive isomorphic method by which political and

regulatory pressures are employed in the institutions.

It can deduced from this theory that regulatory pressures, enduring practices and structures

will set uniform conditions of actions, triggering highly homogeneous risk management

practices in banks. Thus according to the IT, there is some homogeneity expected with

respect to CRMP of different type of banks operating within the same environment. In

simpler terms uniform regulatory pressures, norms, and expected compliance requirements

bring uniformity in the CRMP of all the banks.


19

2.2.1.2 Legal Theory of Finance (LTF)

Legal Theory of Finance was proposed by Pistor (2013) and considered as a cornerstone for

the political economy of finance. The author argues that finance is hierarchical and the

stringent enforcement of legal obligation in the financial market which depends on one’s

hierarchy in the financial network. The theory is based on the assumption of fundamental

uncertainty. This assumption upholds the fact that, during the time of financial crisis,

stringent enforcement of legal obligations would lead to the self-destruction of the financial

system. Therefore, this theory states that the cut down of execution of the law is necessary

for the survival of the financial system. The author claims that, in the past, it is observed

that, although the law tends to give credibility through its enforceability, the actual

implementation of legal commitment without making changes in it had brought down the

financial system. LTF is an inductive theory, which argues that, there is an inverse

relationship between the obligatory nature of contractual and legal commitments on one

hand and the hierarchical nature of finance on the other, where law tends to be more elastic

at the apex and binding on the periphery of the financial system (Pistor, 2013). The

elasticity of law states that law is not equally binding throughout the system.

It is observed from the theory that if the stringent implementation of laws puts the financial

institution in a dire situation of survival, then the suspension of the full force of law is a

priority for the survival of the financial institution. This theory could be related to

understanding the contributory factors for the Basel III preparedness by the banks. In case

the cost incurred for the enforcement of norms exceeds the benefits derived, then it will be

prudent not to implement these norms for the survival of the banks.

2.2.2 Bankruptcy Models

Present study used scoring model such as Altman Z-score model, Springate model,

Zmweskis model and Grover model to predict bankruptcy of banks.


20

2.2.2.1 Altman Z-Score Model -1968

Edward Altman, Finance Professor at Stern School of Business of New York University

developed this model to predict the likelihood of bankruptcy of a manufacturing company.

He believes that the univariate prediction model served in most cases as an indicator and

not as a predictor of bankruptcy. Before the development of the model, he used 22 tentative

variables as a significant predictor of bankruptcy from the five areas such as liquidity,

profitability, leverage, solvency, and activity. He ran multiple discriminant analysis

repeatedly with different mergers of these ratios according to individual contributions,

inter-correlations, and the forecasting ability of each merger. Finally, he developed a Z-

Score model based on five variables that had the most predictive power in the multivariate

discriminant analysis model and gained the accuracy rate of 94%. In his model, he used 33

bankrupt and 33 on- bankrupt firms covering a period of 1946-1965. His model gains an

accuracy rate of 94%. His model was the most influential in corporate bankruptcy

prediction as it was a multivariate model build on the values of selected ratios.

Later this model was re-estimated for service companies in the year 1993 and made use by

many researchers in the past such as Shumway (2001), Altman (2000), Chaudhary and

Nandi (2011), Popker (2013), Chieng (2013), Sharma and Mayanka (2013), Chotalia

(2014), Pradhan (2014), Cantemir and Cristana (2014) Lin (2015), Maina and Sawka

(2017) and Khaddafi et al. (2017).

2.2.2.2 Springate Model -1978

The Springate model was the first model to be introduced by Gordon LV Springate and

used for 40 companies using four variables to determine its scores. This model is a

revolution of the Altman model and it is a strong alternative to Altman’s research as per

Cantemir (2014). In development of model, he initially used 19 financial ratios that have

been frequently used. However, after testing the model, he finally chose four financial

ratios to develop a final model. Springate model examines insufficient liquidity, excess

debt, insufficient sales and lack of profit as the factors to determine the health of firms.

Springate used 40 companies as the sample for his research and shows an accuracy rate of

92.5 percent. A study by Arasu (2013), reports that the Springate model was tested using


21

forty companies and achieved a bankruptcy prediction rate of 92.5%. The Springate model

was applied in the literature by Sajjan (2016), Husein and Pambekti (2014), Imanzadeh et

al. (2011) and Timmermans (2014).

2.2.2.3 Zmijewskis Model -1984

Finance scholar Mark E. Zmijewski created a financial distress prediction model based on

Ohlson’s (1980) work, and now is popularly known as the ‘Zmijewski model’. Zmijewski

used the probit method to predict bankruptcy by considering external factors such as

industry sector, size of the firm, economic cycle in which it operates etc. Zmijewski model

was not a pioneer in applying probit analysis in bankruptcy prediction; however he is the

first who developed a general probit model. Zmijewski argues that one need to builds a

model based on the entire population, to avoid the bias in estimated coefficients; hence he

tried to avoid the choice-based sample bias in his model. He observed that most of the early

models of predicting bankruptcy suffered from this bias. The sample used was non-

financial, non-service and non-public administration firms listed on the American and New

York stock exchange from 1972-78 comprising of 40 bankrupt and 800 non-bankrupts

firms. The original model reported an accuracy rate of 98.2 percent and was later used by

Mehrani et al. (2005), Imanzadeh et al. (2011), Timmermans (2014), Husein and Pambekti

(2014) Warstuti and Stinjak (2014), and Monousaridis (2017).

2.2.2.4 Grover’s Model - 2003

The ‘Grovers Model’ is developed by the restoration of the Altman Z model by Jeffrey S.

Grover by using a sample of 70, comprising of 35 bankrupt and 35 non-bankrupt firms. The

data period was from 1982 to 1996. During the development and testing of the model he

used the sample according to Altman Z-score and he took working capital to total asset and

EBIT to total asset variable of the Altman model and then adds profitability ratios which

are indicated by ROA. According to Prihanthini and Sari (2013) this model had a very high

accuracy rate compared to the Springate model, Zmijewski's model and Altman Z-Score to

measure bankruptcy in a food and beverage company listed in the Indonesian stock

exchange. This model has been applied by Prihanthini and Sari (2013), Aminian et al.

(2016), Qamruzzaman (2016) and Primasari (2017).


22

2.3. Empirical Literature

Past studies undertaken in India and abroad on the credit risk determinants and

measurement of CRMP, bankruptcy models and preparedness and compliance of Basel III

norms have been reviewed extensively by the researcher.

2.3.1. Literature on the Determinants of CRMP

The primary objective of the present study is to identify the factors (determinants) affecting

credit risk management practices, measure its individual impact and compare them between

public and private banks. To achieve this objective present study covers the literature on the

determinants affecting CRMP. In the beginning, studies relating to the conceptual

framework of risk and risk management practices were reviewed. Studies by Benar (2001),

Rosman (2009), Purukaystha and Raul (2014), Harie et al. (2015), and Aven (2016) provide

a historical perspective and conceptual framework of risk and risk management practices.

Their studies point out the phenomenal changes that have taken place in risk management

practices of the Indian banking sector and also explain the relationship between risk

management practice and risk management process.

A study by Diksha and Arora (2009), Bharadwaj (2013) and Subramanyam et al. (2016),

throws light on the weakness of the corporate governance structure of the banks, leading to

improper management of credit risk by banks. Bharadwaj (2013) made an empirical study

on awareness and practices of credit risk management by two banks from Ludhiana city

with a sample of 60 respondents. The data was analyzed data using descriptive statistics,

Chi-square and t-test and concludes that majority of respondents are unaware of the credit

risk management framework. Diksha and Arora (2009) critically examined the risk

management practices of three banks using primary and secondary data. The research

concludes that most of the banks do not have a risk management team, policy, procedure,

and appropriate corporate governance structure in place. Subramanyam et al. (2016)

selected seven Indian banks to study whether the concept of risk management features in


23

corporate meetings, and concludes that risk management is rarely discussed in the

meetings.

According to Nazir et al. (2012) efficient risk management practices are in need today,

because these practices will not only increase the return but improve the competitive

strength of banks. However, less efficient Risk Management Practices (RMP) could make

the situation of the bank worse. Empirical literature is available on conceptual studies of

Risk Management Practices and the factors affecting the RMP (Rosman, 2009). A study by

Das (2005), conducted a conceptual study on determinants of RMP, and presented

guidelines on various determinants of RMP. A similar study was conducted by Rosman

(2009) on Islamic banks, and stated the use of four determinants to develop risk

management model and also suggest a positive relationship between the determinants of

risk management and RMP.

A conceptual study put in practice and empirically proved has wider acceptance. Several

researchers conducted an analytical study on finding out the relationship between the RMP

and its determinants. Studies by Al-Tamimi and Al- Mazrooei (2007), Ochola (2010),

Shafiq and Nasr (2010), Nazir et al. (2012), Hussiney (2012), Gakure et.al (2012), Hussain

and Al-Ajmi (2012) and Ishtiaq (2015) have applied RMP models on different banks in

their countries.

A study by Al-Tamimi and Al-Mazoorie (2007), examined the five determinants of Risk

management Practices (RMP) of UAE national and foreign banks with the aid of Ordinary

Least Square (OLS) regression. The results indicate that there is a significant difference

between the UAE national and foreign banks in two determinants of RMP. A study by

Ochola (2010) analyzed primary data on the five determinants of credit risk management

practices used by commercial banks in Kenya with the help of descriptive statistics. Its

study concluded that one factor, i.e. credit risk monitoring score is low, indicating the

inefficiency of banks in monitoring credit risk. A study by Shafiq and Nasr (2010) explored

and compared the five determinants of RMP followed by public sector commercial banks

and local private banks in Pakistan with the aid of OLS regression. The result revealed a

significant difference in the application of determinants of RMP among public banks and

local private banks. A study by Nazir et al. (2012) compared the RMP of conventional and


24

Islamic banks in Pakistan using five determinants with the help of regression. The result

indicated that there is a significant difference in the determinants of RMP of Islamic and

conventional banks. A study by Gakure et al. (2012) investigated the effect of the four

determinants of credit risk management practice on the performance of unsecured bank

loans of commercial banks in Kenya and the result indicated a positive effect of two

determinants on the performance of banks. A study by Hussain and Al-Ajmi (2012)

examined the association of the determinants of RMP with the conventional and Islamic

banks using different econometrics and statistical method. This study used five factors to

judge its impact on the RMP using the OLS technique. The study found that the level of

risk is significantly higher in conventional banks. A study by Ishitiaq (2015) assessed the

potency of eight determinants of risk management within banks in Pakistan with the help of

OLS regression. Its study concluded that effective RMP depends upon several determinants

and is useful exercise to meet regulatory requirements. A study by Ngoroge and Ngahu

(2017) used one factor, i.e. Risk Identification (RI) to find its impact on the performance of

the banks in Kenya. Its study concludes that risk identification played a crucial role in the

credit performance of the banks. Hussiney (2012) examined the different techniques of

RMP dealt with within banks in UAE. The result indicated a narrow range of risk and

unuses of diverse range of RMP in the UAE banks.

2.3.2. Literature on Measurement of CRMP

Another aim of the present study is to measure the CRMP of public and private banks

through lending and financial ratios as credit risk measurement tools. It is found from the

literature that, the ratios for measurement of credit risk differs from bank to bank although

all the banks face the same type and extent of credit risk. A studies by Thiagarajan (2011),

Goel and Rekhi (2013), Kattel (2016), and Pourkiaei and Kumar (2016) investigated the

difference in the credit risk of public and private banks using different ratios for

measurement of credit risk.

A study by Thiagarajan (2011) measured and compared the credit risk between public and

private banks using tool such as ratio analysis and ANOVA test. The result shows a

significant difference in the ratios of public and private banks. Similarly, a study by


25

Pourkeiki (2016) measured and compared credit risk between public, private and foreign

banks using a tool i.e. ratio analysis and T-test. The result shows a significant difference in

the ratios of public, private and foreign. A study by Kattel (2016), found a significant

difference in credit risk of private and joint-venture banks in Nepal using a tool i.e. one way

ANOVA. A study by Goel and Rekhi (2013) compared the performance of public and

private banks based on indicators such as profitability and capital ratios with the help of

descriptive statistics. This study concludes that new banks are more efficient than old ones.

The present study also covers the measurement of CRMP using liquidity, profitability and

leverage variable, with the assistance of financial ratios. The liquidity, profitability and

leverage are found to be the essential determinants for the success and survival of the

banks; hence there should be optimum tradeoff between these three variables. Considering

the above present study finds the interrelationship between these three variables and

measures CRMP using these variables.

Past studies were reviewed, to understand the interrelationship between these three

variables. It is identified from the reviews that, in certain banks there is positive

relationship between liquidity and profitability, whereas other banks showed no

relationship between these two variables. This shows that, there persists a controversial

issue on the relationship of these two variables. There are studies such as Lartey et al.

(2013), Awais et al. (2016), Nabeel and Hussain (2017), which shows a negative

relationship between liquidity and profitability in banking industry.

A study by Lartey et al. (2013), found a weak relationship between liquidity and

profitability of the banks listed on the Ghana stock exchange. Similarly, Awais (2016)

developed a relationship between profitability and liquidity using the regression model. The

empirical results reveal that there is a significant negative relationship between banks

liquidity measurement and ROA ratio. Also, Nabeel and Hussain (2017) examined the

effect of liquidity management on profitability in the banking sector of Pakistan. Data were

analyzed using correlation and regression techniques. Its study concludes that cash and

current ratios show negative relations whereas quick and CAR show positive relations

towards profitability.


26

The studies which show no relationship between liquidity and profitability are Abdullah

(2014) and Ahmed (2016). A study by Abdullah (2014) analysed the impact of liquidity on

profitability in the banking sector of Bangladesh and concluded that there is no significant

relationship between liquidity and profitability. Also a study by Ahmed (2016) does not

show a significant relationship between profitability and liquidity. Its study was conducted

in Pakistan on standard chartered bank. Finally there should be an optimum tradeoff

between profitability and liquidity for the survival of the bank.

A leverage ratio measures the extent to which a bank has financial assets with equity. The

leverage ratio places a cap on the borrowings as a multiple of banks equity. Empirical

literature is available, stating the relationship between profitability and leverage. Most

studies found an inverse relationship between profitability and leverage. These studies are

Ebiringa (2012) Taani (2013) and Zafar and Fartyal (2015).

A study by Ebiringa (2012), analyzed the effect of financial leverage on Nigerian bank

performance, the study found a negative effect of leverage on the performance of the bank.

Research by Taani (2013) examined the impact of leverage on the profitability of Jordan

banks. Its study concludes that the presence of high leverage affects the return on banks. A

study by Zafar and Fartyal (2015) analyzed the leverage position of the Indian banking

industry and its impact on the profitability for a period 2001-2010. Its study concludes that

leverage has an impact on the profitability of the banks. High leverage may reduce the

profitability of the firm hence banks should make efforts to control the risk. Thus most of

the studies claim a negative relationship between leverage and profitability. However a

study by Moghaddam (2017) covered the effect of leverage and liquidity ratios on the

earnings of banks listed on the Tehran stock exchange for a period of 2010-2015 using a

multivariate regression model. The study concludes that there is a significant positive effect

of liquidity and leverage on the earnings of the firm.

A literature in the area of understanding relationship between liquidity and leverage is less,

as also the liquidity and its effect on the debt level have been a controversial issue among

scholars in finance studies. Prior studies have demonstrated that liquidity increased debt

level while other studies found that high liquidity was less leveraged and more regularly


27

financed by their capital. A study by Ghasemi and Rajak (2016) concludes that a quick ratio

has a positive effect on leverage, whereas the current ratio harms the leverage of banks.

There is a difference in the profitability, liquidity and leverage position of public and

private banks in India. Most of the studies claim that there is a difference in these variables

of public and private banks. The work by Thakarshibai (2014), Balaji and Kumar (2016),

Patel and Bhanushali (2017),Vadrale and Katti (2018), Khan (2018) and Koley (2018)

conclude that the profitability position of a private bank is better than public banks.

A study by Thakarshibai (2014), analyzed the relative profitability of public and private

banks using ANOVA and descriptive statistics. Its study concludes an improvement in the

profitability of private banks compared to public banks. A study by Balaji and Kumar

(2016) compared the financial position of selected public and private banks in India using

profitability ratios. Its study concludes that the profitability is higher for the private banks

and the public banks are lacking behind in many financial parameters and are undergoing

many challenges too. Chintala (2016) compared the financial performance between public

and private banks in India. Its study found profitability is higher for private banks. A study

by Patel and Bhanushali (2017) made a comparison of nationalized and private banks for a

data period of 2010-2015 on profitability aspects. Its study concludes that both these

sectors are profitable but it is a greatest challenge for public banks. Also, Katti and Vadrale

(2018) compared the profitability position of public and private banks in India for a period

of 2001 to 2015. Their study concludes that the profitability position of the private bank is

better than public banks. A study by Khan (2018) analyzed the financial performance of

public and private banks through the CAMEL model and made a comparison through t-test.

Its study concludes that the performance of the private bank is better in all aspects

compared to public banks. Further, a study by Koley (2019) compared the performance of

public and private banks using the CAMEL model. Its study concludes that private banks'

performance is better than public sector banks. Similarly, Sharma (2017), made a

comparison and analysis of the profitability of private banks using ANOVA. The result

shows a difference in the profitability of banks in two financial ratios out of the four ratios

used in the study.


28

2.3.3. Literature on the Bankruptcy Models

The bankruptcy is a situation where business cannot repay its outstanding debt and

improper CRMP will result in bankruptcy of banks. Past research shows different methods

available for predicting bankruptcy and one amongst them is the scoring model. The

scoring model is a linear combination of the factors (accounting variable), weighted by

coefficients which provide the relevant score. This study covers the review of scoring

models such as Altman Z-score model, Springate model, Zmweskis model and Grover

model.

There was extensive literature found on the Altman Z-score model. This model was applied

by many researchers in the past such as Chaudhary and Nandi (2011), Popker (2013),

Sharma and Mayanka (2013), Chieng (2013), Chotalia (2014), Pradhan (2014), Cantemir

(2014) Lin (2015), Maina and Sawka (2017) and Khaddafi et al. (2017).

A study by Chaudhary and Nandi (2011) developed an internal credit rating model for

banks with the help of the Altman Z-score model. Its study used the Altman Z-score model

to arrive at an equation for prediction. The results reveal the developed model is more

accurate than then Altman Z-score model. A study by Popker (2013) applied the Altman Z-

score model to assess the bankruptcy of 15 Indian commercial banks from 2002 to 2012

and concludes that the performance of all banks in the selected sample is under the safety

zone. Sharma and Mayanka (2013) applied the Altman Z-score model on 36 Indian

Commercial Banks for a period of five years from 2007 to 2012 and also ranks were given

for banks based on Z-score. Their Study concludes that financial position of sampled Indian

banks is satisfactory. A study by Chieng (2013) applied Altman Z- score model to predict

banks failure, its study suggest this model as a reliable predictor. A study by Cantemir

(2014) highlighted the evolution of this model and criticized on its clear and sound default

detection.

A study by Chotalia (2014) computed Z-score for the six Indian private banks and

concludes that sampled banks are uncertain about credit risk and considered marginal cases

to be watched with attention. Pradhan (2014) applied Altman Z-score and Neural Network


29

to predict the bankruptcy of three Indian public sector banks from 2001 to 2007. Its study

concludes that the neural network technique outperforms the Altman model. Lin (2015)

conducted study on consumer goods Industry in UK and applied original Altman Z-score

model, in which its study found that ratio (Sales/Total Asset) has little contribution to

distinguish bankrupt and non-bankrupt firm, hence its study renewed the original Altman

model and found the renewed Z- score outperforms the original Altman model. Maina and

Sawka (2017), in their paper, assessed the financial distress among 30 listed firms in the

Nairobi stock exchange in Kenya for the period from 2003 to 2007. The result indicates that

the financial health of the listed company needs to be improved. Khaddafi et al. (2017),

tested the Altman model for predicting bankruptcy of banking companies listed on the

Indonesia stock exchange and concludes that, Altman has good predictive power.

Some of the researchers applied multiple models and their comparison to identify the

accuracy and predictive power of models. These reviews are, Imanzadeh (2011), Kordlar

(2011),Vaziris and Bhuyan (2012), Avenhuis (2013), Karamzadeh (2013), , Husein (2014),

Kleinert (2014), Timmermans (2014), Kumar and Kumar (2016), Sajjan (2016), Aminian.,

et al ( 2016), Warstuti, and Stinjak, (2014), Wati (2015), Primsari (2017), Manousaridis

(2017), Syamni et al. (2018). The reviews on comparison of models found certain

researcher’s gains better results using the Altman Z- score model then the other models

such as the Springate model, Ohlson Model, Zmweskis model. However, some other

researchers have criticized the Altman Z-score model on the ground of its predictive ability.

A study by Vaziris and Bhuyan (2012) compared the predictability of failure of 100 banks

from Europe, Asia and US for a data period 2001 to 2010 using Moody's model, Standard

and Poor's, Vaziri's model, Altman-Z-score and Logit model. Their study concludes that,

Altman model makes the best prediction. A study by Karamzadeh (2013), applied and

compared the Altman and Ohlson model to predict bankruptcies of companies listed in

Tehran stock exchange. The sample composed of 45 firms in each group such as bankrupt

and Non-bankrupt group. Its study concludes Z-score model works better. A study by

Primsari (2017), made a comparison of Altman, Grover, Springate, and Zmijewski model

to find its accuracy on the 29 listed firms of the consumer goods industry of Indonesia


30

stock exchange. The study shows the Altman Z-score model as most accurate and the

Grover model as least accurate. Another study by Syamni et al. (2018) applied and

measured the impact of the score on the stock prices using Panel regression. Their study

used Altman, Grover and Zmweskis model for predicting bankruptcy of coal mining

companies in Indonesia. Its study found that Ohlson and modified Altman model were

dominant in prediction. Another study by Chieng (2013), made the verification of Altman

Z- score predictive power on Eurozone banks failure, study suggested that Altman Z-score

is a reliable predictor. All the above studies claims that Altman model accurate compared to

other models.

There are other studies propounded the renewal of the Altman Model as it gives a higher

error rate. These studies are Altman (2000), Shumway (2001), Cantemir (2014),

Timmermans (2014) Warstuti and Stinjak (2014), Kumar (2016), Manousaridis (2017), A

study by Altman (2000), discussed two models such as the Z-score model-1968 and Zeta

Model-1977 for assessing the financial distress. Its study demonstrates that the Zeta model

has improved accuracy over the existing failure of the Z-score model. Another study by

Shumway (2001) proved that the Z-score model is dead and not trustworthy anymore for

predicting corporate bankruptcy. He claims that, half of its variables have poor predictive

strength. A conceptual study by Cantemir (2014) criticized the Z-score model as it has a

series of shortcomings that cripples into prediction efficiency. A study by Timmermans

(2014), tested the accuracy of the bankruptcy model such as Altman (1968), Ohlson and

Zmijewski model after its recalibration. Its study found that the accuracy of the model

increased after recalibrations. A study by Warstuti and Stinjak (2014), determined the

variables that affect the level of health of the company by using Grover Model, Altman

Model, Springate Model, Ohlson Model, and Zmijewskis Model to predict the health of the

bank. The results of their study show that working capital has a positive influence on the

health of banks in all models of bankruptcy, except in the model of Altman (1973). Kumar

(2016) applied multiple models such as Altman Z-score, Ohlson and Zmweskis model on

Texmo industries in Coimbatore covering a period of 2005-2010. Its Study concludes that,

O-score model has better prediction performance. A study by Manousaridis (2017), applied

Altman, Springate, Zmweskis and Grover model on the firms from the emerging market. Its


31

study concludes that the Altman model is outdated and needs to be handled in cautious

way.

The Springate model is the second model used in the present study, which was introduced

by Gordon LV Springate (1978). A literature review on the Springate model shows the

predictive ability of this model. A study by Sajjan (2016) aimed at presenting a theoretical

foundation and compares the result of two models such as Zavgren and Springate. The

result indicates the adjusted Springate model was efficient than another model. Also, a

study by Warstuti and Stinjak (2014) and Manousaridis (2017), express that Springate

model is better compared to Altman model. However few other studies such as Imanzadeh

(2011) Aminian et al. (2016) and Primsari (2017) found other models more accurate than

Springate model.

The Zmijewskis model is the third model used in the present study, which was developed

by Mark E. Zmijewski in the year 1978. Many studies applied this model such as

Timmermans (2014), Warastuti and Stinjak (2014), Kumar (2016) and Manousaridis

(2017). Some of the studies used multiple models and found Zmijewski model as the best

predictor. A study by Husein (2014), analysed the accuracy of Altman, Springate,

Zmweskis and Grover Model using 132 companies listed under Drafter Efteck Syriah

(DES) in 2009-12. In the study, Binary Logistic Regression (BLR) was used for data

analysis and found that, Zmweskis is the most appropriate model to be used in financial

distress. Imanzadeh (2011) applied Springate and Zmweskis model on the firms of the

Tehran stock exchange. Its study concludes that Springate is a more conservative model

than Zmweskis model. Timmermans (2014), tested the accuracy of the bankruptcy model

such as Altman model, Ohlson and Zmijewskis model after its recalibration and found that

the accuracy of the model increased after recalibrations.

The Grover model is the fourth model used in the present study, which was developed by

the restoration of the Altman Z- model by Jeffrey S. Grover. The literature review on the

Grover model shows the following facts. A study by Aminian et al. (2016) investigated the

ability of bankruptcy prediction models such as Altman, Grover, Springate, and Zmweskis


32

of thirty-five listed firms of Tehran stock exchange using regression and correlation

analysis and showed that the Grover model shows a better bankruptcy prediction. A similar

study was conducted by Qumruzzaman (2016), on Grameen bank in Bangladesh using the

same models. Its study concludes that Grover model provides better predictions. However a

study by Primsari (2017), found Grover as the least accurate model.

There are other reviews which present a divergent view on all of these four models such as

Kordlar (2011), Avenhuis (2013) and Wati (2015). A study by Kordlar (2011), compared

the bankruptcy prediction model such as Altman, Ohlson Zmijewski, Shumway and

combined model in Tehran Stock Exchange in Iran. Its study concludes that the combined

model significantly outperforms the other model. This review summarize that all four

models have the same accuracy rate. Avenhuis (2013), tested the generalizability of

Altman, Ohlson and Zmijewski model for Dutch listed firms and re-estimated these models

using logistic regression. It is found that all three models are accurate when the same

statistical technique is used. This implies the explanatory variable of this model is the best

predictor of the likelihood of bankruptcy. Wati (2015) examined the predictive ability of

Zmweskis, Ohlson and Altman model for measuring the financial performance of listed and

delisted banks of Indonesia stock exchange. Its prediction shows that, there exists a smaller

difference in the predictive ability of these models. In other words, it means all three

models have more or less the same accuracy rate.

2.3.4. Literature on the Basel III Preparedness and Compliance

The present study covers Basel III 2013 norms as these norms were developed in the recent

period. These norms encourage banking supervisors to follow a sound CRMP, hence every

bank tries to implement Basel III norms for improving its financial health. However, in

practice, it poses a potential challenge for the banks to implement Basel III. Various studies

in the past highlighted the critique of Basel III and the challenges banks would have to face

in the implementation of Base III norms. These studies include Fry et al. (2011), Adamson

(2012), Subbarao (2012), Jain ( 2013), Jayadev (2013), Sbarcea (2014), Chkrabarti and

Rakshit (2014), Vishwanath (2015) and Barua (2016).


33

A study by Fry (2011) addressed the contemporary issues about Basel accord worldwide

and concludes that emerging economies of Pakistan poses several challenges for the

implementation of Basel III. Another conceptual study by Adamson (2012) covered the

Basel III impact on risk management and highlights the challenges posed by new reforms.

Similarly, Subbarao (2012) addressed certain questions about the implementation of Basel

III in his speech. He says it is a potential challenge for a bank to implement Basel III.

Jayadev (2013), expressed the challenges of Basel III implementation in the areas such as

capital resource, growth, financial stability, enhanced profitability, deposit pricing,

maintenance of liquidity standards, etc. A study by Sbarcea (2014) covered the evaluation

and prevention of bank risk using the Basel III approach. Its study raised a challenge on the

implementation deadline and implementation method of Basel III.

A study by Jain (2013) presented a critical view of Basel III norms in Indian banks, it

states, that RBI has delayed the final date of implementation, due to difficulty in its

implementation. Another study by Chkrabarti and Rakshi (2014) critically evaluated the

implementation of the Basel III norm and suggested a need for improvement in corporate

governance. A study by Vishwanath (2015) addressed in the national conference that Basel

III is a challenge for the Indian banking system. A study by Barua (2016) commented on

the need for huge capital requirements by Indian commercial banks to comply with the

minimum requirements of Basel III.

All these above studies focus on the implementation of Basel III norms, a potential

challenge for the Indian banks. This triggers heavy preparations on the part of Indian banks

for the successful implementations of the Basel III accord. Some of the studies have

explored the preparations made by banks such as Ernst and Young (2003), Hussein and Al-

Tamami (2008), KPMG (2008), Mirchandani and Rathore (2013), Ernst and Young (2013),

Roy (2014), Hussein and Hassan (2015), Al-Tamimi (2015), Kapoor and Kaur (2015) and

Boora and Jangra (2018).

A study by Ernst and Young (2003) assessed the readiness of the South African banking

industry for Basel II implementation. Its study found banks data collection is the key

challenge for banks and banks are in part implementation of Basel III. A study by Hussein


34

and Al-Tamimi et al. (2008) investigated the Basel II preparations of UAE banks using four

factors affecting preparations such as anticipated benefit, cost, impact, and challenges in

regression. The result shows that anticipate benefit and impact significantly contributes

towards the Basel II preparations. A study by KPMG (2008) examined the preparedness of

thirty-five Asia Pacific banks using simple percentage analysis. Its study concludes that

there is a need to devote more time and resources for the full implementation of

approaches. A study by Mirchandani and Rathore (2013) explored the readiness to comply

with the regulations of Basel III of Indian public sector banks. The results conclude that

implementation will have a significant impact on the profitability and lending activities of

the banks. Also, Ernst & Young (2013) measured the readiness of implementation of Basel

II, Basel II.5 and Basel III in the Russian banking sector. The study concluded that the

Russian banking Sector should plan strategically and conduct various Basel III awareness

programs and workshops for managers.

A study by Roy (2014) analyzed the preparedness of Basel III capital adequacy taking

different elements of capital, its study found that, banks follow a huge challenge to deal

with this capital aspect. Studies by Al- Tamimi et al. (2015) investigated the readiness of

UAE banks for Basel III implementation using three factors for contribution such as

expected benefits, awareness, and the availability of resources. The regression result

indicates, the availability of resources is the most important factor for Basel III

implementation. A study by Kapoor and Kaur (2016) evaluated the Basel II preparations of

Indian public and the private Banks based on the four factors such as perceived benefit,

anticipated cost, anticipated impact, and anticipated challenges using regression analysis,

its results shows that banks anticipated benefit is a significant factor in Basel III

preparations. A study by Boora and Jangra (2018) explored the preparation level of Indian

public banks towards Basel III using six factors in regression analysis. The result shows

bank preparedness has a significant relationship and highly affected by the anticipated

benefit and expected cost provided for implementation. Above studies shows, the

preparations of Basel III are conducted in other countries, but very little literature is

contributed to our country.


35

According to Swamy (2013) banking is one of the most heavily regulated businesses since

it is heavily leveraged, hence a need felt to check the compliance of regulations. Kumar

(2014) compared the level of compliance of Basel II norms for selected nationalized,

foreign and private banks. The study used trend analysis, paired t-test, one way ANOVA,

and t-test for analyzing bank compliance for Tier I, Tier II capital, and CAR. The analysis

indicates that foreign banks are more efficient in compliance with new norms as compared

to nationalized and private banks. Datey and Tiwari (2014), studied the effect of Basel III

norms on the stability of Indian banks and concludes that the Basel III accord is expected to

generate a positive response to the economy. Balsubramaniam (2010), in his study, states

the compliance process of the Indian banks to Basel III standards and concludes that, Indian

banks have to plan for more capital in years ahead. Al-Hares et al. (2013) conducted a study

on the compliance of Basel III capital standard in conventional and Islamic banks. Their

study concludes that implementation process will be completed by 2019. Sharma (2017)

conducted a study on the compliance of Basel III norms of selected national foreign and

private banks. Its study found a significant difference in the Tier 1 capital ratio under Basel

II and Basel III and also found a significant difference in the CAR of public and private

banks.

2.4. Research Gap The above reviews present a research gap on credit risk determinants of CRMP,

measurement of CRMP, bankruptcy models and Basel III preparedness and compliance.

2.4.1 Gaps on Credit Risk Determinants of CRMP

Past studies on determinants of RMP reveals the use of three or five determinants while

other studies used six or seven determinants of RMP. These studies are conducted in

different countries such as Pakistan, Kenya, UAE. The determinants applicable in some

context/nations may not apply to Indian banks due to the difference in the economic

background and the Central Bank's policies of the country. Thus recognizing of exact

determinants of RMP in the Indian banking sector persist a research gap. Therefore present

study follows a scientific procedure (Principal Component Analysis) for the identification

of determinants, rather than determining them a priori. Similarly, studies are available

covering the determinants of RMP in general, but studies are very few on determinants of


36

Credit RMP. RMP is a broad term covering all types of risks such as credit risk, market

risk, interest risk, and liquidity risk. Among these risks, credit risk is the most important

risk because it has a substantial effect on the return on investment of the bank. Therefore,

the present study will focus on the determinants of Credit Risk Management Practices

(CRMP) and contribute to the literature of the credit risk determinants of CRMP. This

study is adding new value to literature in terms of identifying the determinants of CRMP

using PCA technique.

In Indian banking system the two major sectors are public and private banks. Recent

statistics on NPA reveals that NPA of public sector banks are higher than private banks.

One of the reasons for the same is ineffective CRMP. Considering this fact, the novel

approach of the present study is to compare the CRMP of public and private banks.

2.4.2 Gaps on Measurement of CRMP

Though a great deal of literature is available on credit risk measuring tools with other

parameters, studies measuring the credit risk based on lending ratios and financial ratios as

a parameter are lacking. Studies that investigate the difference in the lending and financial

ratios of public and private banks are also limited in nature. The novelty of the present

study highlights the trend analysis through cross-sectional and time-series data analysis.

This study tries to seek, the year in which the banks liquidity, profitability and leverage

position was poor through a time series data analysis and it also put focus on finding out

the bank from public and private sector group, which has a strong and weak liquidity,

profitability and leverage position through a cross-sectional data analysis. The proxy to the

variable used in the present study is different from those used in past studies as this study

used proxy to a variable based on bankruptcy models used in this study. The relationship

between liquidity profitability and leverage has been studied in other countries, but no

similar studies have been carried out in the Indian banking sector. Most of the studies

focused on the profitability aspect as a base for comparison between public and private

banks, however, the present study covers the liquidity, profitability, and leverage aspects as

a base for making a comparison between public and private banks.


37

2.4.3 Gaps on Bankruptcy Models

Many researchers use one or two bankruptcy models in their studies, which may not give

relevant results according to Kleinert (2014). Thus there is a need to use more than two

models to decide the accuracy of the model. Some studies have used limited sample of

banks that may not give an appropriate results, this directs to do a study on larger sample

size. Though ample literature is available on bankruptcy prediction covering only one

sector of banking either public or private sector, but the work on comparative analysis on

public and private banks on this count are lacking. Ample literature is available covering a

data period of three to five years. Literature needs to be developed covering a data period of

more than five years. Studies are available covering a data period of 2001- 2012, studies on

recent period left to be examined and studies on recent periods need to be examined. In

previous studies, bankruptcy models were applied for nonbanking companies, but very few

cover the application of bankruptcy models on banks. The original models may not be

relevant in the current economic environment, thus literature need to be developed on

recalibrations of original model.

The novel approach of present study focuses on seeking the accuracy and ability of

bankruptcy models, following dual approach i.e. ranking the banks based on the score and

detecting Type I and Type II error.

2.4.4 Gaps on Basel III Preparedness and Compliance

Many studies are available on identifying the factors affecting the Basel II preparations,

those concerning Basel III preparations found to be lacking. Even though some of the

studies used six factors affecting Basel III preparedness, but some of these factors were not

significant towards Basel III preparedness. This persist a gap in identifying the significant

factors affecting Basel III preparedness for banks in Indian context. Studies on comparison

of the factors of Basel III preparedness between public and private banks are also lacking.

The empirical literature is available on studies such as factors affecting capital adequacy,

however, very few studies are available on understanding the capital adequacy before and

after adoption of Basel III norms. Previous studies examined the impact of Basel III norms;


38

whereas literature is limited on compliance of Basel III norms as per the requirements of

RBI in Indian banking sector. Though some studies do available on comparing the CAR,

Tier I and Tier II capital ratio before and after the adoption of Basel II Norms, but the same

concerning Basel III norms left to be examined.

2.5. Summary The extensive literature review helps in understanding the concepts and assists in finding

the research gaps. Literature review on the historical perspective and conceptual framework

of risk and risk management practices points out the phenomenal changes that have taken

place in risk management practices of the Indian banking sector. The conceptual studies on

risk management states that, banks are unaware of the credit risk management framework

and they do not have a risk management team, procedure and policies in place. Empirical

literature on the determinants of RMP, shows that some studies used three or five

determinants while other studies used six or seven determinants. Similarly these studies are

conducted in different countries, with difference in the economic background and the

Central Bank's policies of the country. This shows that these determinants are not standard

and varies from country to country. Therefore the factors applicable in some

context/nations may not apply to Indian banks. Further, empirical analysis of many studies

found a positive relationship between the determinants of RMP and RMP of banks.

It is found from the literature that, the ratios for measurement of credit risk differs from

bank to bank although all the banks face the same type and extent of credit risk. Some

banks have used lending ratios as well as financial ratios as a credit risk measurement tools.

In this context, certain studies found a significant difference in the ratios of public and

private banks, indicating the difference in the CRMP of banks.

Empirical literature is available on the liquidity, profitability and leverage of banks. The

literature on the profitability and liquidity states, positive and negative relationship between

these two variables. Also few other studies claim no relationship between profitability and

liquidity. This shows the controversy exists in the relationship of profitability and liquidity

of banks. Further, many studies on the relationship between leverage and profitability,

claims negative relationship between them. A literature on liquidity and leverage claims a


39

controversial issue among scholars in finance studies. Prior studies have demonstrated that

liquidity increased debt level while other studies found, high liquidity was less leveraged

and more regularly financed by their capital. Finally, the interrelationship between

liquidity, profitability, and leverage is a controversial issue.

An extensive literature is reviewed on the bankruptcy study and it shows different methods

available for predicting bankruptcy. Amongst them, one of the popular method is scoring

model. There was ample literature found on scoring models such as the Altman Z-score

model, Springate model, Zmijewski model, and Grover Model. Certain reviews applied

single model, while others used multiple model in their studies. Similarly, some reviews

applied and compared these models to find the accuracy and predictive power of these

models. These reviews found better results using the Altman Z- score model, while other

researchers found better results using Zmijewski’s Model. Similarly, some other

researchers found the Springate model and Grover model has better predictive ability

compared to other models. Further, some other researchers have criticized these models on

the ground of its predictive ability and propounded for the renewal of these models as it

gives a higher error rate. The reviews assisted the researcher in identifying models,

understanding the critiques of the model and focusing on the recalibration of these models.

The present study covers Basel III 2013 norms as these norms were developed in the recent

period. Various studies in the past highlighted the critique of Basel III and the challenges

banks would have to face in the implementation of Base III norms. This triggers heavy

preparations on the part of Indian banks for the successful implementation of the Basel III

accord. Some of the studies have explored the preparations made by banks in terms of

understanding the factors affecting the Basel III preparedness of the banks. These reviews

highlight the significance of four factors in Basel III preparedness.

The banks are the most heavily regulated businesses as it is heavily leveraged and this

demand to check the compliance of regulations. Past research evidenced the use of different

methods and techniques followed by the researchers for the Basel III compliance. Most of

the studies highlighted the use of capital ratios to check the compliance of Basel III.

Chapter III Methodology and Techniques Literature

40

Chapter III

Methodology and Techniques

3.1. Introduction

The present study entitled “Credit Risk Management Practices of Public and Private Banks

in India" is undertaken to identify the determinants affecting credit risk management

practices, through principal component analysis and measure its impact using OLS

regression. This study also measures credit risk using lending and financial ratios. It

compares the lending ratios between public and private banks and also within these banks

by utilizing independent sample T-test and ANOVA respectively. Further, with the aid of

cross-sectional and time-series data, this study measures liquidity, profitability and leverage

position of banks using financial ratios. It also identifies the relationship between liquidity

profitability and leverage with the aid of Pearson’s correlation coefficient.

This study also aims at analysing the risk of bankruptcy of public and private banks using

the bankruptcy models and judges the accuracy of these models. This aim is achieved by

applying Altman Z-score, Springate, Grover, and Zmijewski's model for assessing the

bankruptcy risk of select public and private banks in India. Further, a study also recalibrates

these models by changing its coefficients through multiple linear regression analysis and

compares the original and recalibrated model to judge the accuracy of these models. The

ranks are given to individual banks by accurate models. Towards the end, a study compares

the bankruptcy score of public and private banks with the help of the t-test.

Finally, the present study assesses the preparedness and the compliance of Basel III accord

in the Indian Banking Industry. A study measures the impact of factors affecting Basel III

preparedness with the aid of multiple linear regressions. Additionally, it covers the

comparison of the implemented Basel III ratios of public and private banks with minimum

requirements specified by RBI using one-sample t-test. Moreover, it covers a comparison of

the Basel III ratios between public and private banks in the pre-implementation phase and

also in the post-implementation phase of Basel III with the aid of independent sample t-test.


41

At last, the study makes a comparison of these ratios before and after the adoption of Basel

III norms using paired sample t-test.

The data for the present study is collected from primary and secondary sources. Primary

data is used to evaluate the impact of credit risk determinants on CRMP and to evaluate

factors affecting the Basel III preparedness of banks. Secondary data is used to measure

credit risk using lending and financial ratio and to assess the financial health of banks by

application and recalibrations of bankruptcy models. This data is further used to check the

compliance of Basel III ratios.

A variety of statistical tools and techniques are used for data validity, data description, and

data analysis. The analyzed data is helpful to draw a valid conclusion and present findings

of the study. These findings are useful for the policy and strategic decision making to the

individual banks, regulatory authorities, government, prospective investors and the other

stakeholders of banks. This chapter covers the elaborations on data sources, statistical tools

and econometric approach and it specifies the descriptions of the hypothesis.

3.2. Data sources

The data required for the present study is collected from primary and secondary sources.

Primary data is used to fulfill the requirements of objective one and part of objective four.

The secondary data sources have been employed for the remaining objectives of the study.

3.2.1 Primary Source

The primary data was collected through a series of interview schedules and a predesigned

structured questionnaire run on credit risk officers of banks. Before designing the

questionnaire, desk research was conducted to study literature on the available subject and

to have a thorough understanding of various parameters to be included in the questionnaire.

The questionnaire was tested with a pilot study and a content validity test. The details of the

sampling design used by the researcher are explained in the forthcoming section.

3.2.1.1 Sampling Design

The sampling design of the study includes a study area, sample size, data collection

instrument, content validity of the questionnaire, and the pilot study.


42

Study area

The study area selected were the Head offices, Regional offices and the Zonal offices of the

banks located in Mumbai, Hyderabad, Chennai, Poona, and Goa. The area selected for the

pilot study is the capital city of Goa (Panjim), wherein the regional, zonal and the main

branches of the banks were visited.

Sample Size

For the present study, the sample size has been scientifically determined following the

Krejcie and Morgan (1970) sample selection rule. The Krejcie and Morgan sample size

calculator was based on P = 0.05, where the probability of committing Type I error is less

than 5% (P<0.05). A brief description of the method is given in Table 3.1.

Table 3.1: Krejcie and Morgan Sample Size Determination

N S N S N S N S N S

10 10 110 86 210 136 320 175 550 226

20 19 120 92 220 140 340 181 600 234

30 28 130 97 230 144 360 186 650 242

40 36 140 103 240 148 380 191 700 248

50 44 150 108 250 152 400 196 750 254

60 52 160 113 260 155 420 201 800 260

70 59 170 118 270 159 440 205 850 265

80 66 180 123 280 162 460 210 900 269

90 73 190 127 290 165 480 214 950 274

100 80 200 132 300 169 500 217 1000 278

Source: Adapted from Krejcie and Morgan 1970

S= χ2 NP (1-P)/d2 (N-1) + χ2P (1-P)…………… (3.1)

Where, S = Required sample size

χ2 = Table value of Chi-square for 1 degree of freedom at the desired confidence level

(3.841)

N = Population size

P = Population proportion (assumed to be .50 since this would provide the maximum

sample size

d= Degree of accuracy expressed as a proportion (.05)

Note: As the population increases, the sample size increases at a diminishing rate and

remains constant at more than 380 cases.

Based on the pilot study, it is found that there are 3 to 5 key risk officers in the Credit Risk

Management Department at the Head Office-level, hereafter referred to as the ‘Credit-Risk

Officers.' The survey was conducted following the Stratified Random Sampling method,


43

with two strata of public and private banks. The present study has undertaken 21 public

sector banks and 18 private banks in a sample. Therefore the population of our study is

(21x5) = 105 Credit Officers in case of Public banks, and (18x3) = 54 Credit Risk Officers

in case of private banks. Thus the total population of the present study comprises 159

Credit-Risk Officers. Table 3.1 shows that 113 Credit-Risk Officers would be an

appropriate sample considering a population size of a maximum of 160 Credit-Risk

Officers.

In order to collect data, around 140 questionnaires were distributed through Google Forms

and as well as in-person to the Credit-Risk Officers of the banks. As many as 24

respondents did not reply to the questionnaire due to their busy schedule, providing us with

an 83 percent response rate. Based on the 116 responses generated, the generalisations have

been formulated. The proportion of the sample between public and private banks is 2:1

based on the number of credit risk officers in public and private banks comprising 77 and

39 Credit-Risk Officers of Public and private banks.

The Data Collection Instrument

The data collection instrument was designed based on previous studies, who perform

similar studies but in a different environment. This instrument is similar but not identical,

having been revised after taking into account the environment and opinions of experts. The

questionnaire was sent in electronic and physical form based on the location of the

respondent. The respondents were asked scale based questions on a five-point Likert scale

ranging from 1-5. The questionnaire consists of three sections mentioned in table 3.2

Table 3.2: Information related to various sections in the questionnaire

Section I Demographic Profile

Section II Determinants of Credit Risk management Practice

Section III Preparedness in Basel III Implementation Source: Researchers Compilation based on a questionnaire

3.2.1.2 Content Validity Index (CVI) and Reliability

The content validity index was computed to provide evidence of the validity of contents in

the questionnaire and to assess the necessity of questions. The ratings of items were done

by the six experts on three factors, such as relevance, clarity and simplicity. These experts

were selected from the banking and the academic sectors. The criteria outlined by Yun and


44

Ulrich (2002) were used as a reference point in selecting the panel experts. In this study,

four experts were taken from banking sectors. These four experts are the regulators who

visit the local RBI office and have more than ten years of experience, while two experts

were taken from the academics holding a doctoral degree in the banking area and have

published papers in the same area. The expert’s opinion was analyzed using a quantitative

method such as Content Validity Index (CVI) to decide the quality of items. The minimum

CVR for each item to be considered as acceptable was 0.75 for a one-tailed test at the 95%

confidence level, assuming that a minimum of 8 judges was used for the study

(Lawshe,1975).

Table 3.3: CVI score calculated based on nine items from the responses of six experts

Q. N

o

Co

nstru

ct

no

.

Relev

ancy

(Sco

re)

Clarity

(Sco

re)

Sim

plicity

(Sco

re)

Resu

lts

II - 6 4 33.33 66.67 33.33 Dropped

5 50 66.67 33.33 Dropped

6 83.33 33.33 33.33 Changed

II - 8 1 33.33 66.67 33.33 Dropped

4 83.33 33.33 33.33 Changed

5 50 66.67 33.33 Dropped

6 33.33 33.33 66.67 Dropped

II - 10 33.33 50 33.33 Dropped

IV - 4 50 33.33 33.33 Dropped

Source: Authors calculations based on responses from appendix table A.1

Table 3.3 shows the CVI for nine items from the data collection instrument. The seven

items [Construct no. II-6(4), II-6(5), II-8(1)1, II-8(5), II-8 (6), II-10 and II-4] from the

instrument do not meet the criteria fixed for the study; therefore based on the CVI index

these items were dropped. Four experts suggested minor revisions regarding the clarity or

wording of two items [Constructs no. II – 6 (6) and II-8 (4)] which were incorporated into

the data collection instrument.

3.2.1.3 Pilot Study

The questionnaire was first tested through a pilot study by taking the responses from ten

public banks and eight private banks from the capital city of Goa (Panjim), wherein the

regional, zonal and the main branches of the banks were visited. The pilot study helped the


45

researcher to understand the overview of the Credit Risk Management Department

(CRMD), level of preparedness for implementation of Basel III norms, location of the

zonal, regional and head offices. Further, necessary modifications were made in the

questionnaire based on the responses of respondents from the pilot study. Finally, the

questionnaire was revised and restructured to meet the objectives of the study.

3.2.2 Secondary Source

The secondary data is collected from various sources such as audited statements of

individual banks and RBI websites, various publications of RBI like statistical tables, and

reports. The data is also collected from reputed data sources such as Centre for Monitoring

Indian Economy (CMIE) and Indiastats.com. The present study analysed data for thirteen

years between 2004-05 and 2016-17. The data period consists of two phases, the pre-

implementation and the post-implementation phase of Basel II and Basel III norms. This is

the period where the Basel committee put norms on the banks in the form of minimum

capital requirement, the supervisory review process, market discipline, capital buffers, etc.

The effect of all these norms is reflected in the financial statements of the banks, which are

more reliable and will reduce measurement errors.

In the present study, banks are selected as sample, because the financial data of banks are

taken from publicly available financial statements, which are more reliable and will reduce

measurement errors. Another reason is, banks are subject to many regulations and financial

statements are monitored by outsiders such as creditors, depositors, RBI (Reserve Bank of

India), SEBI (Securities Exchange Board of India), Tax Authorities, etc. The type and the

number of banks are selected based on the availability of data and the consequences of time

limitations. The secondary data is collected for the second objective on 44 Indian banks,

consist of 21 public sector banks, 18 are the private banks and five are the non-working

banks. However, for the other objectives, data about nonworking banks was not relevant;

hence for these objectives, the sample taken is 39 commercial banks (21 public banks and

18 private banks).


46

3.3. Hypotheses of the study

Based on the objectives of the study and the comprehensive review of the literature the

following hypotheses have been framed. The first objective of the study tests the significant

impact of the determinants affecting CRMP and compares them between public and private

banks in India. The Ordinary Least Square (OLS) regression model is used to test the

following null hypothesis:

H01: There is no significant statistical impact of credit risk determinants on their respective

Credit Risk Management Practices (CRMP), of /between public and private banks in India.

A study by Al-Tamimi (2007) showed that understanding risk and risk management, risk

monitoring and reporting and credit risk analysis has a positive relationship with risk

management practices. A conceptual study by Rosman (2009) found a positive relationship

between the RU, RI, RAA and RMC on the RMP of banks. Another study conducted by

Nazir et al. (2012) revealed that risk analysis, risk monitoring and understanding risk and

risk management are positively significantly contributing to risk management practices of

Islamic and conventional banks operating in Pakistan. A study by Jorge and Ngahu (2017)

shows that risk identification is positively significant to influence the CRMP. These studies

are conducted other than India and the above hypothesis is framed in context to Indian

banks. The Indian banks reflect unhealthy scenarios of NPA’s, which may be due to

improper CRMP. Therefore, the hypothesis states no statistical impact on the determinants

in improving CRMP.

The above hypothesis also states, no difference in the determinants of CRMP between

public and private banks in India. This hypothesis supports the institutional theory, as the

theory claims that due to the presence of uniform rules and regulations by all the banks, it

will set uniform conditions of actions triggering risk management practices highly

homogeneous in banks. The identical regulatory pressures, norms, and practices will bring

uniformity in the CRMP of all the banks; hence there may not be a statistical difference in

the determinants of CRMP between public and private banks.


47

The present study also compares the lending ratios between and amongst the public and

private banks based on the following null hypothesis of independent sample t-test and

ANOVA.

H02: There is no statistical difference in the lending ratios between and amongst the public

and private banks in India.

The above hypothesis states that the lending ratios mentioned above are the same for public

and private banks in India, on the ground that both these banks perform a similar function

of accepting deposits for lending; therefore, they undertake a huge credit risk. This will lead

to uniformities in the ratios of public and private banks.

The lending ratios of all the public banks will be in par with the other banks in the public

sector group, as also the case of the private sector group. Due to this, all the public sector

banks and private banks will follow a uniform procedure before sanctioning the loan and

comes under a consistent regulatory regime. Therefore the mean lending ratios of all the

public and private banks will be at the same level; hence the above null hypothesis of

ANOVA states that all the public sector banks and private bank group means are equal.

This study also covers a comparison of financial ratios between public and private banks

based on the following null hypothesis of Independent t-test:

H03: There is no statistical difference in the financial ratio (liquidity, profitability and

leverage) between the public and private banks in India.

The difference in these variables may not arise because both the banks belong to the same

industry and performing similar functions, and comes under the uniform regulatory regime

of RBI. Therefore there may not be a difference in the financial ratios between public and

private banks.

This study tries to seek the difference in the bankruptcy scores between public and private

banks in India and the difference in the original and the recalibrated model, with the aid of

Independent sample t-test. Following is the null hypothesis for the same.

H04: There is no statistical difference in the bankruptcy scores using the original and the

recalibrated bankruptcy models for banks, and between public and private banks in India.


48

The difference in these models may not arise, as both models are using the same accounting

variable to arrive at a score. Similarly, there may not arise, a difference in the bankruptcy

score between public and private banks, due to the similarity in the lending function

performed by both the banks. Both these banks are carrying the risk of default by the

borrower and therefore have an equal chance of becoming bankrupt due to non-payment by

the borrower.

This study explains the Basel III preparedness (BP) of public and private sector banks in

India, in terms of understanding the factors influencing BP. These factors are Anticipated

Benefit (AB), Anticipated Cost (AC), Anticipated Impact (AI) and Expected Challenge

(EC). The Multiple linear regression model is used to test the following null hypothesis:

H05: There is no statistical impact of factors such as Anticipated Benefit, Anticipated Cost,

Anticipated Impact and Expected Challenge on the Basel III Preparedness for and between

the public and private banks in India.

The hypothesis states that the factor AB, AC, AI and EC are not the influential factors for

Basel III preparedness. This hypothesis is framed based on the assumption of the

fundamental uncertainty of the Legal Theory of Finance (LTF). The theory states that, if the

cost of enforcement of norms exceeds the benefit it gains, then the cut down of these norms

will be a priority for the survival of banks. This theory is related to understanding the Non-

contributory factors for the Basel III preparedness by the banks. The factors will be non-

contributory to BP when there is no impact of these factors on BP.

The above hypothesis also states the uniformity in the preparations of Basel III norms by

public and private banks in India. This hypothesis is framed based on the uniform schedule

or the phase-in arrangement of the Basel III norms given by the RBI to the Indian banks.

This schedule makes the uniform enforcement of the Basel III norms by public and private

banks. Therefore both these banks are undertaking similar steps for the preparations of

Basel III norms; hence there may not be any difference in the preparations of Basel III

norms by public and private banks in India.


49

This study explains the compliance of Basel III ratios of public and private banks with the

minimum required ratio prescribed by RBI in Basel III, based on the following hypothesis

of one sample T-test.

H06: The mean actual Basel III compliance ratios of public and private banks in India are

lower or equal to the minimum Basel III ratio prescribed by RBI.

The actual Basel III requirement ratios of public banks and private banks could be lower

because, historically banks were maintaining a low provision coverage ratio, as the Indian

banks have a high proportion of NPAs that are not provided for the capital.

Further, this study comprises of comparison of the Basel III capital ratios between public

and private banks in the pre-implementation phase and also in the post-implementation

phase of the Basel III period using an Independent sample T-test.

H07: There is no statistical difference in the mean Tier I, Tier II and Capital Adequacy

Ratio between public and private banks in India in the pre-implementation and post-

implementation phase of Basel III norms.

The pre-implementation phase covers the period from 2009-2013 and in this phase, banks

were following Basel II norms. Similarly, the post-implementation phase covers the period

from 2014 -2018 and in this period, banks were following Basel III norms. Due to the

implementation of these norms by the banks, the difference in the above ratios may not be

there in this period between public and private banks.

Lastly, this study covers a comparison of Basel III capital ratios before and after the

adoption of Basel III norms, with the help of the following hypothesis of paired T-test

H08: There is no statistical difference in the mean Tier I, Tier II, Capital Adequacy Ratio

among the public and private banks in India before and after the adoption of the Basel III

norms.


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The significant difference in these ratios may not arise for public banks, because

historically, these banks were maintaining adequate capital and controls the majority share

in the market, possessing good capital health. Therefore there may not be any difference in

the above ratios of public banks before and after the adoption of Basel III norms.

Similarly, the difference in capital ratios may not arise in the case of the private banks, as

these banks were always playing the precautionary role before granting a loan to the

business community and therefore, the NPA’s of these banks were always less compared to

NPA’s of public banks. Further, these banks were maintaining higher capital ratios, even

before the adoption of Basel III norms as per observation to appendix table A.60 (CAR).

3.4. Statistical Tools

The present study used a variety of statistical tools for data validity, data description, and

data analysis. These tools are the following:

3.4.1 Principal Component Analysis (PCA)

PCA is a data compression tool that is utilized to decrease huge sections of variables into

small sections, that still contains most of the common information in the larger set. In the

present study, PCA is used to determine the factors influencing Credit Risk management

Practices (CRMP) of banks using the data generated from section II of the questionnaire

(Appendix table A.2). It is used on 31 statements of the questionnaire. In this study, the

selection of the variable is made based upon Eigenvalues, visualization of the Scree plot,

and Rotated Component Matrix.

3.4.2 Reliability Test

The reliability of the data collected on the Likert five-point scales in the questionnaire was

checked using Cronbach's alpha reliability coefficients, which measure the consistency with

which respondents answer the question within a scale. In the present study, for all the

constructs, the reliability coefficients are greater than 0.7, which concludes that the data

collected by the researcher is reliable and can be used for further analysis.


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3.4.3 Descriptive Statistics

The descriptive statistics provides summary of variables in terms of percentage, frequency,

mean, median, standard deviation, kurtosis, skewness, minimum, and maximum value, to

organize and summarize data and to describe the characteristics of the sample. The data

with respect to the determinants of CRMP and dimensions of Basel III preparedness of

public and private banks in India is described in terms of mean, median, standard deviation,

kurtosis, skewness and range, minimum and maximum values. This helps us to provide a

summary of all variables concerning the respondent. The data concerning financial ratios is

described in terms of mean, maximum, minimum and standard deviation in the appendix

table (A.22-A.39) to understand the trend analysis of cross-sectional and time-series data.

3.4.4 Ratio Analysis

The ‘Lending Ratios’ such as Gross NPA to Gross Advance Ratio, Net NPA to Net

Advance Ratio, Total Loan to Total Asset, Total Loan to Total Deposit, Total Loan to Total

Equity, Provision for NPA to NPA, Capital Adequacy Ratio and Return on Asset are used

in the present study. Further, 'Financial Ratios' are used to measure the profitability,

liquidity and leverage position of the public and private banks in India. Profitability is

measured using Earnings before Interest and Tax to Total Assets, Earnings Before Interest

and Tax to Current Liability, ROA and Total Income to Total Asset. The Liquidity is

measured using Working Capital to Total Asset, Current Asset to Current Liabilities, while

the Leverage is measured using Retained Earnings to Total Asset, Market Value of Equity

to Total Liability and Total Deposit to Total Asset. The need for measurement of the above

variables arises as they act as a basis to determine the bankruptcy score.

3.4.5 Pearson Correlation Coefficient

Pearson’s Correlation Coefficient is used to find the relationship between financial ratios

used to measure liquidity, profitability and leverage. It is also used to meet the assumption

of multiple linear regression and OLS regression.


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3.4.6 Parametric Test and Non-parametric Tests

An independent sample t-test is conducted to compare the difference in credit risk

determinants of CRMP, lending and financial ratios, bankruptcy scores, Basel III

preparedness and the Tier I, Tier II and CAR between public and private banks in India.

This test is also used to compare the difference in the bankruptcy scores between the

original and the recalibrated bankruptcy models. The test of ANOVA is used on lending

ratios to find a significant difference amongst the banks. Further, a post hoc test is

conducted to find which bank reveals a statistically significant difference in the group. One

sample t-test is used to compares the actual Basel III ratios of public and private banks with

minimum ratios prescribed by RBI while paired t-test is run to determine the significant

mean difference in the mean tier I cap ratio, tier II cap ratio, and the capital adequacy ratio

in the pre-implementation and post-implementation phase of Basel III norms. Non-

parametric test such as Mann-Whitney U-test is used for comparing the financial ratios, as

the data was found to be following the non-normal distribution

3.4.7 Regression Analysis

Regression Analysis is a powerful statistical method that allows the examination of the

relationship between two or more variables. In the present study, two types of regressions

are used, such as Ordinary Least Square (OLS) regression and Multiple Linear Regression.

The Ordinary Least Square (OLS) regression is used to determine the statistical impact of

the credit risk determinants on CRMP. The result of the PCA shows that CRMP is

dependent on four credit risk determinants.

Thus the multiple regression equation of CRMP is as follows:

CRMP = f (CRU+CRI+CRAA+CRMC) ----------------- (3.1)

Where the independent variable Credit Risk Management Practices (CRMP) are a function

of Credit Risk Understanding (CRU), Credit Risk Identification (CRI), Credit Risk

Assessment and Analysis (CRAA) and Credit Risk Monitoring and Control (CRMC). All

the assumptions required to run the regression, such as testing of linearity, testing of

independence of observation, testing for outliers, testing for Homoscedasticity, testing of

normality and multicollinearity test were checked before running a regression.


53

The second type of regression used in the present study is the Multiple Linear Regression.

This regression allows the researcher to determine the overall fit of the model and the

relative contribution of each of the total variance explained. This regression is used to

recalibrate the bankruptcy models. These equations are:

1. Recalibrated Equation of Altman Model

Z-scores = β+βX1+βX2+βX3+βX4......................................... (3.2)

Z-scores = 2.22 + 0.754X1 + 0.036X2 + 0.199X3 + 0.239X4

Where X1= Working capital/Total assets, X2= Retained earnings/Total assets, X3=

Earnings before interest and taxes/total assets, X4= Market value equity/Book value of

Total liabilities and Z= overall index.

2. Recalibrated Equation of Springate Model

S-scores = β +β1X1+β2X2+β3X3+β4X4........................ (3.3)

S-scores = 0.093 + 1.37X1 + 7.124X2 + 0.585X3 - 1.988X4

Where X1= Working capital/Total assets, X2= Earnings before Interest and Tax/Total

assets, X3= Earnings before interest and taxes/Current Liability, X4= Total Income/Total

Asset, and S= overall index.

3. Recalibrated Equation of Zmijewski Model

X-scores =β +β1X1+β2X2-β3X3.............................. (3.4)

X-scores = -2.534 - 3.79X1 + 4.246X2 - 0.081X3

Where X1 = Return on Asset (ROA), X2 = Total deposit to Total Asset, X3 = Current Asset

to Current Liabilities and X-Score = overall index.

4. Recalibrated Equation of Grover Model

G-scores =β +β1X1+β2X2-β3X3 ............................ (3.5)

G-scores = 0.074 + 1.65X1 + 3.09X2 - 0.014X3


assets, X3= Return on Asset (ROA) and G-Score = overall index.


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The recalibrated model consists of the same variable as the original model, but the

coefficients differ. To develop a recalibrated model, all the assumptions required for the

regression were checked. These assumptions are correlation coefficients, independence of

errors, multicollinearity, homoscedasticity, normality, normality of residuals, presence of

outliers and data fit of the model.

The multiple linear regression is also used to measure the factors impacting the impact of

the Basel III preparedness. The conceptual framework states that Basel III Preparedness

(BP) is dependent on four factors, such as AB, AC, AI, and EC. Thus the regression

equation of Basel III Preparedness is as follows:

BP= f (AB+AC+AI+EC).............. (3.6)

Where BP= Basel III Preparedness, AB = Anticipated Benefit, AC = Anticipated Cost, AI

= Anticipated Impact and EC = Expected Challenges.

3.4.8 Robust Test

The robust test is conducted to measure the financial health of the bank in the year 2017.

This test was conducted, taking into account profitability and asset quality as a variable.

These variables are measured using a proxy to a variable such as Return on Asset (ROA)

and Net NPA to Net Advances ratio, respectively, for the year 2017. Thus for the robust

test ROA and Net NPA to Net Advance ratio for the current year 2017 is considered.

3.5. Descriptions of Bankruptcy Models

3.5.1 Altman Z-Score Model -1968

In the year 1967, Edward Altman - Finance Professor of Leonard N. Stern School of

Business of New York University developed the financial model in the year 1967 to predict

the likelihood of bankruptcy of a company. His work was based on the notion that the

univariate prediction model served in most cases as an indicator and not as a predictor of

bankruptcy. Before the development of the Altman -1968 model, he tested 22 tentative

variables as a significant predictor of bankruptcy from the five areas such as liquidity,

profitability, leverage, solvency and activity. Finally, based on five variables, he developed

a Z-score model. In his model, he used 33 bankrupt and 33 non - bankrupt firms covering a


55

period from 1946 -1965. His model gains an accuracy rate of 94%. This model was applied

by Nandi and Chaudhary (2011), Popker (2013), Sharma and Mayanka (2013), Chotalia

(2014), Pradhan (2014), Vaziris and Bhuyan (2012), Lin (2015) and Maina and Sawka

(2017). The model developed a following equation with zones of discrimination

Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 1.0X5 ………..(3.7)

Where X1 = Working capital/Total assets, X2 = Retained earnings/Total assets, X3=

Earnings before interest and taxes/Total assets, X4 = Market value equity/Book value of

total liabilities and Z= overall index.

Zones of discrimination

Z > 2.99 = “Safe” Zone

1.81 < Z < 2.99 = “Gray” Zone

Z < 1.81 = “Distress” Zone

Extension of Z-score Model -1993

Z-score was re-estimated based on the other databases for private manufacturing

companies, non-manufacturing companies, and service companies. The Z-score model for

the service companies uses four variables to discriminate between obligors. These variables

are – liquidity, leverage, profitability, and solvency. These variables are measured with the

help of the following ratios:

An equation for service industries as per Z-score Model -1993

Z = 6.56X1+ 3.26 X2+6.72 X3+1.05 X4 …………………………………………… (3.8)

Where X1= Working capital/Total assets, X2 = Retained earnings/Total assets, X3=

Earnings before interest and taxes/total assets, X4 = Market value equity/Book value of

Total liabilities and Z= overall index.

The ‘Working capital/Total assets (WC/TA)’ ratio measures the liquidity of the firm’s

assets. Altman considers that a firm with heavy operational losses will have diminishing

current assets compared to the total assets. The second ratio ‘Retained earnings/Total assets

(RE/TA)’indicates the ability of the firm to earn profit through retained earnings and

measure of the cumulative profitability in time. Altman (2000) notes that the retained


56

earnings account can be influenced by certain corporate strategies like a reorganization or

dividend declarations. The third ratio ‘Earnings before interest and taxes/Total assets’

measures the profitability of the business and is an indicator of effectiveness of banks in

using its assets to generate earnings before the payment of contractual obligations. The last

ratio ‘Market value equity/book value of total liabilities’ indicates the proportion of owners

fund to the long term debt. A high value of ratio depicts a firm’s aggressiveness in

financing its growth with debt. This rate shows the amount by which the value of the assets

of the firm can fall before the liabilities surmount the assets, and thus the firm is insolvent.

Based on the results of Z- score, banks may be classified into different zones shown in

Table 3.4: Bases of discrimination of Altman Model

Situation Z - score Zones

I 1.1 or less Distress zone

II 1.1 to 2.6 Grey area

III 2.6 or more Safe zone

Source: Secondary data

3.5.2 Springate Model - 1978

The Springate model introduced by Gordon LV Springate in the year1978 and is a

revolution of the Altman model. According to Cantemir (2014) it is a strong alternative to

Altman's research. This models initially tested 19 financial ratios that have been frequently

used by firms and finally chose four financial ratios to be used to determine whether the

company is said to be either a healthy company or potentially insolvent. The Springate

model examines insufficient liquidity, excess debt, insufficient sales and lack of profit as

the factors to determine the health of firms. Springate used 40 companies as the sample for

this research. Springate test shows that the model has an accuracy rate of 92.5%. A study

by Arasu (2013), reports that the Springate model was tested using forty companies and

achieved a bankruptcy prediction rate of 92.5%. The Springate -1978 study formulated a

equation as follows:

S= 1.3X1 + 3.07X2 + 0.66X3 + 0.4X4 …………………….. (3.9)


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Where X1 = Working capital/Total assets, X2 = Earnings before Interest and Tax/Total

assets, X3 = Earnings before interest and taxes/Current Liability, X4 = Total Income/Total


In the Springate S-score, the first ratio which is the ‘working capital divided by total asset’

measures the liquid asset concerning the firm’s size. The second ratio ‘profit before interest

and taxes divided by total assets’ estimates the cash supply available for allocation to

creditors, the government and shareholders. This ratio is a measure of an organization's

operating efficiency separated from any leverage effects and it is a true depiction of asset

production (Pandey 2011). The third ratio ‘profit before taxes divided by current liabilities’

estimates the cash supply available from the operation, for honoring the short-term

obligations of the firm. Last ratio ‘sales divided by total assets’, measures the capital

turnover. Financial analysts find the Springate model useful as it can increase the power of

decision for investors and suppliers of financial resources to sustain financial markets

(Security Exchange) for ensuring the allocation of optimal financial resources (Arasu et al.

2013). Table 3.5 shows the zones of discrimination for the Springate model.

Table 3.5: Bases of discrimination of Springate Model

Outcome (S-score) S = ≤ 0.862 “Financial Distress”

≥ 0.862 "safe Zone"

Source: Secondary Data

3.5.3 Zmijewski Model - 1984

A study by Mark E. Zmijewski (1984), created a financial distress prediction model based

on Ohlson (1980) work popularly known as Zmijewski model. Zmijewski used the Probit

method to predict bankruptcy. The Probit regression is suitable when the dependent

variable is qualitative and the independent variables are quantitative. During the

development of model Zmijewski’s took external factors, such as industry sector, size of

the company, economic cycle, etc. Therefore, he used all non-financial, non-service and

non-public administration firms listed on American and New York Stock Exchange from

1972-78. His sample consists of 40 bankrupt and 800 non-bankrupts firms. The original


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models reported an accuracy rate of 98.2%. This model uses variables such as profitability,

leverage and liquidity. This model arrived the following equation:

X - Score = - 4.3 - 4.5X1 + 5.7X2 - 0.004X3 ………………….. (3.10)


to Current Liabilities and X-Score = overall index.

Based on the results of model, X-Score values obtained are divided into two classes. If the

X-score is negative (X - Score < 0), then the company is classified in a healthy condition.

Conversely, if the X-score is positive (X-Score ≥0) then the company is classified under

financial distress. It is noted that X-score can be converted to bankruptcy probability using

the expression - 1/1+ε-1.8138Zm

Probit models use a latent variable Y* that ranges from negative infinity to positive infinity.

The cumulative standard normal function G transforms the latent variable Y* into a

predicted Y value between 0 and 1.

Pr(𝑌)=𝐺(𝑌∗)=𝐺(𝛽0+𝛽1𝑥1+𝛽2𝑥2+⋯+𝛽𝑘𝑥𝑘 )……….(3.11)

The estimated coefficients from the Probit regression are difficult to interpret because they

measure the change in the latent variable, not Y itself. The marginal effects are a more

useful way to measure the magnitude of the estimated coefficients.

Table 3.6: Bases of discrimination of Zmijewski Model

Outcome (X-Score)

X-score < 0 = ‘Safe’

>0 = ‘Financial Distress’

Probability (X-score) = ≤ 0.5 ‘Safe’

> 0.5 ‘Financial distress’

Source: Secondary data

The Probit model of Zmijewski is beneficial in comparison with MDA because the Probit

function maps the value to a probability bounded between 0 and 1, which is easy to

interpret. In statistical matters, the Zmijewski model defines bases of discrimination as in

case of a p-value or X - score that is equal or greater than 0.5 is classified as ‘Financial

distress’ and companies having a p-value that is lower than 0.5 are classified as ‘Safe’


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Zmijewski was not a pioneer in applying probit analysis in bankruptcy prediction; however,

he is the first who developed a general probit model. Zmijewski model argues that unless

one builds a model based on the entire population, the estimated coefficients will be biased,

and the resulting predictions will over-estimate the proportion of bankrupt firms that are

correctly classified. Mehrani et al. (2005) conclude in its study that, the Zmijewski model

can divide the firm into bankrupt and non-bankrupt, more efficiently.

3.5.4 Grover Model - 2003

The Grover model is developed by the restoration of the Altman Z - model by Jeffrey S.

Grover. According to Primasari (2017), during the development of the model he used the

sample according to Altman Z-score by adding 13 financial ratios. He used a sample of 70

companies with 35 bankrupt and 35 non-bankrupt companies and considered a data period

from 1982 to 1996. The result, Grover models are the most appropriate predictive models

applied to companies in the food and beverage sector.

G-score = 1.650X1+3.404X2 - 0.016 ROA+0.057…… (3.12)


assets, X3= Return on Asset (ROA) and G-Score = Overall index.

The ‘Working capital/Total assets’ ratio measures the liquidity of the firm’s assets and

‘Earnings before interest and taxes/Total assets’ ratio is an indicator of understanding the

effectiveness of bank’s assets to generate earnings before payment of contractual

obligations. Whereas, ROA measures the relationship between net profit and total asset.

Table 3.7: Bases of Discrimination of Grover Model

Outcome (G-score) G≤ -0.02---Bankrupt

G≥ 0.01---- Non-bankrupt

Source: Secondary Data

A study shows that the model Grover has the highest degree of accuracy that is equal to

100% (Grover 2003). On the contrary, the Altman model of Z-Score has an accuracy rate of

94%, Springate model has an accuracy rate of 92.5%, Zmijewski’s model has an accuracy

rate of 98.2%.

Chapter IV Determinants of Credit Risk Management Practices

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Chapter IV

Determinants of Credit Risk Management Practices

4.1. Introduction

The banking sector is the mainstream of capital inflows and outflows of the economic

system of the country. The banking sector in an Indian economy is undergoing a process of

change, driving them to pose a higher risk. Therefore, there is a critical need for risk

management in this sector. Risk management is understood as a set of departments or

bodies concerned with identifying, assessing, mitigating and reporting risk. The risk

management is an ongoing process, which is highly dependent on the volatility of the

internal and external environment of the banks. Some of the risk is at the micro level, such

as the negative effects of the recession, political interference, natural disasters, and some

are at the macro level such as credit risk, liquidity risk, foreign exchange risk and

operational risk.

The banks face four types of risk such as credit risk, market risk, liquidity risk, and

operational risk. These four risks have a manifold effect on the performance of the banks

however, the magnitude and the extent of loss caused by credit risks are more severe and

can lead to bank failures. Thus, from amongst the above mentioned risk, credit risk is the

most significant risk, as it has a substantial impact on the financial health of the banks.

According to Afande (2014) the credit risk problem often begins right at the loan

application stage and increases further at the loan approval, monitoring and controlling

stages, especially when CRM guidelines in terms of policy an strategies/procedures for

credit processing do not exist are weak or incomplete. Due to this reason, the management

of credit risk is the most prominent strategy banks need to adopt for survival. Credit risk

management is a broad term, which includes principles, processes, and practices adopted by

the banks. The principles and processes guide the adoption of effective Credit Risk

Management Practices (CRMP). Considering this fact, the present study focuses on

effective CRMP. It is understood that “CRMP means establishing a suitable credit risk

management environment by understanding, identifying, assessment and monitoring the

credit risk.”


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The significance of effective credit risk management practices has increased with time

basically due to prior global meltdown. Further, the credit risk is not only triggered in the

banks due to micro factors but also due to macro factors. It is a problem not just restricted

to national boundary, but the effect of improper credit risk management is spread all over

the world. Thus, not only the country’s economy is affected due to credit risk, but there is a

world economy meltdown due to credit risk. The proliferation of 2008 crisis worldwide

triggered the attention of the researcher towards better credit risk management practices of

the banks, as the major cause of the financial crisis is ineffective credit risk management

practice. In recent years, risk management in banking institutions has got substantial

importance and has become the center debate after the global financial crisis (Ishitiq 2015).

Besides, a study by Rehman (2016), states that risk management is getting more attention,

especially after the credit crunch in 2007 to 2009. Thus, owing to the increasing NPA’s,

CRMP has jumped to the forefront of credit risk management activities carried out by the

banks.

The present study covers the CRMP of the public and the private banks in India to check

the extent and magnitude of variation. It is essential to acknowledge the literature, which

states that CRMP of the public and the private banks differ. This difference may be on

account of an exceptional challenge public sector banks need to face in managing credit

risk due to government control and interference. This interference the government is to

improve the scenario of the Indian economy. As per Kamat (2019), the situation of the

Indian economy reflects that the government needs to address issues such as Job creation,

NPA laden banking, falling investments, poor performance of exports, and burgeoning

deficits. This shows the need for the speedy recovery of the Indian economy along with the

revival of the automobile, banking, finance, service telecom etc. The above issues drive the

government to depend on the public sector banks for financial assistance.

The present chapter is an empirical study which summarizes the findings from the primary

data collected through a questionnaire. It aims to identify the factors (determinants)

affecting credit risk management practices, measure its individual impact and compare

them between public and private banks. This chapter consisting results and discussion is

divided into four parts. Part I explains the sample characteristics such as demographic


62

profile, content validity, pilot study. Part II comprises of reliability analysis and Principal

Component Analysis (PCA). Part III consists of descriptive statistics on all the

determinants of CRMP and Pearson’s correlation coefficient. Part IV covers the regression

analysis to measure the credit risk determinants impact on CRMP. It also includes the

results of the Independent sample T-test to compare the credit risk determinants between

public and private banks in India.

4.2. Theory and Literature

4.2.1 Institutional Theory

The present study applies the Institutional Theory to find the extent of uniformity in the

credit risk determinants of CRMP between public and private banks. The Institutional

Theory describes norms, ideologies, values, which need to be uniformly followed by all to

run their organizations. The institutionalization exists when the risk management activities

in most of the institutions become highly equivalent. This equality can be attained through

the coercive isomorphic method by which political and lawfulness pressures are employed

on institutions in the forms of persuasion, direction or invitation. It is understood from the

theory that, due to regulatory pressure, enduring practices and structure will set uniform

conditions of actions triggering risk management practices highly homogeneous in banks.

According to this theory, there is a homogeneity of CRMP between public and private

banks.

4.2.2 Literature Review

Studies by Benar (2001), Rosman (2009), Purukaystha and Raul (2014), Harrie (2015),

Aven (2016) studied the historical perspectives and conceptual framework of risk and risk

management practices over a period of time. A study by Diksha and Arora (2009),

Bharadwaj (2013) and Subramanyam, et al. (2016), throws light on the weakness of

corporate governance structure of the banks and improper management of credit risk by

banks. The credit risk management policies of private banks are different from public banks

as per the studies of Thiagarajan (2011), Goel and Rekhi (2013), Kattel (2016) and

Pourkeiki (2016).


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The empirical literature is available on Risk Management Practices (RMP) and the factors

affecting the RMP. A conceptual study by Das (2005) and Rosman (2009), figures out the

theoretical model of RMP. Das (2005), in its study presented guidelines on determinants of

RMP. A study by Rosman (2009) stated the use of four determinants in understanding the

RMP. Ample literature is available specifying the empirical evidence on determinants of

RMP. A studies by Al-Tamimi & Al-Mazoorie (2002), Al-Tamimi and Al- Mazrooei

(2007), Onyano (2010), Shafiq and Nasr (2010), Nazir (2012), Hussiney (2012), Gakure

(2012), Hussain (2015) and Ishtiaq (2015), Ngoroge and Ngahu (2017), applied RMP

model on different banks in their countries.

Literature on the above studies shows that some studies used three determinants, while

other studies used seven determinants. Ishitiaq (2015) conducted a study in Pakistan and

used seven determinants. Similarly, Al Tamimi (2007), Al-Tamimi & Al-Mazoorie (2002)

conducted a study in UAE, also Hussain & Al-Ajmi, (2012) conducted a study in Bahrain

and Shafiq & Nasr (2010) conducted a study in Pakistan using five determinants. A study

conducted by Rehman (2016) in Pakistan used six determinants. A study by Ngoroge and

Ngahu (2017) used one factor i.e. Risk Identification (RI), to find its impact on the

performance of the bank. This shows that these determinants are not standard. Thus, the

present study is an extension to the above studies with a difference in terms of focusing on

credit risk, identification of the determinants using Principal Component Analysis (PCA),

and a study about Indian banks.

4.3. Data and Methods

This chapter used primary data collected through a predesigned structured questionnaire.

Before designing the questionnaire, desk research was conducted to study literature on the

available subject and to have a complete understanding of various parameters to be

included in the questionnaire. The questionnaire was tested with a content validity test

(Annexure 1) and a pilot study. In the content validity, the ratings of items were done by six

experts on three factors such as relevance, clarity, and simplicity. The result of the content

validity Index shows minor revision regarding the clarity or wording of the items and those

revisions were incorporated into the instrument. The questionnaire was answered by Credit


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Risk Officers of Banks. Most of these officers possessed an experience of more than five

years and a designation of managership as per table 4.2.

Thirty-one questions were used to find the credit risk determinants of CRMP as per

annexure table II. These thirty one statements are undertaken based on the questionnaire of

past studies such as Al-Tamimi and Al-Mazoorie (2002), Al-Tamimi and Al- Mazrooei

(2007), Onyano (2010), Shafiq and Nasr (2010), Nazir (2012), Hussiney (2012), Gakure

(2012), Hussain (2015) and Ishtiaq (2015), Ngoroge and Ngahu (2017). These thirty-one

statements were overlapping and conveyed the same meaning. These thirty one statements

were used by the above studies in their countries to find the determinants of CRMP. The

present study poses a research problem in recognising of exact determinants of CRMP

given the different factors that govern CRMP in different countries. The factors applicable

in some context/nations may not apply to Indian banks due to the difference in the

economic background and the Central Bank's policies of the country. Also, certain studies

used many determinants resulting in the problem of collinearity. Therefore a data

compression tool called PCA was used in the study to identify the credit risk determinants.

This technique could reduce the huge sections of variables into small sections that still

contain most of the common information in the larger set.

The PCA technique reduced the thirty-one statements into twenty two statements.

Assumption such as the suitability of data is measured through the Kaiser-Meyer-Olkin

(KMO) test. The KMO values range from 0 to 1. High values indicate usefulness. Both

William et al. (2010) and Parinet et al. (2004) state the data is adequate when the value is

greater than 0.5. The second assumption of PCA is used to check the Eigen value. An Eigen

value of 1 represents the variance of one variable. Another method to decide the retention

of the component is a visual inspection of the Scree plot. In the Scree plot, only those

components are retained before the last inflection point of the graph. Finally, the retention

of components is made based on Eigen value, visual inspection of Scree plot and rotated

component matrix. Based on PCA, twenty two statements were used to identify the

determinants of CRMP. Reliability analysis was used on these statements to assess the

chances of random error entered.


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Descriptive statistics (mean, median, variance in data, Kurtosis and the maximum and

minimum value) presented in Table 4.6 were used to summarize and describe the

characteristics of the sample, while the Pearson correlation coefficient was used to check

the relationship between the independent variables. Regression analysis was used in the

present study to measure the relationship between several independent variables and a

single dependent variable. The present study used Ordinary Least Square (OLS) to test the

significant impact of the dependent variable on the independent variable. OLS method is

applied in the current study because this is a well-accepted method and commonly used in

studies such as Al Tamimi and Mazoorie (2007), Shafiq and Nasr (2010) etc.

Any Assumptions required to run the ordinary least square regression such as linearity,

independence of observation, outliers, homoscedasticity, normality, normal PP-plot etc.

were checked before running a regression. An independent sample T-test was conducted to

compare the differences in CRMP of public and private banks.

.

4.4. Conceptual Framework of CRMP

Risk management is a wide term, which includes principles, process and practices. Risk

management principles are the rule of action guiding the effective implementation of the

risk management process and also a guiding force for adopting effective risk management

practices. The risk management process is a mechanism for the implementation of the

practice. Considering this fact, it is understood that the principles and processes guide the

adoption of effective Risk Management Practices (RMP). This study focuses on the

adoption of effective RMP. RMP is a broad term that includes determinants governing

RMP and the tools for measurement of credit risk.

RMP is a model comprises of four determinants such as Risk Identification, Risk

Understanding, Risk Assessment and Analysis and Risk Monitoring Control. These are

called determinants or factors because it contributes to effective RMP..


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Based on literature review, it was found that most of the studies in the past have covered all

types of risk under the Risk Management Practices (RMP). These studies are Al-Tamimi &

Al-Mazoorie (2002), Al-Tamimi and Al- Mazrooei (2007), Onyano (2010), Shafiq and

Nasr (2010), Nazir (2012), Hussiney (2012), Gakure (2012), Hussain (2015) and Ishtiaq

(2015), Ngoroge and Ngahu (2017). Further, most of the above studies show the use of five

determinants as an independent variable to arrive at a RMP model. These determinants are

Understanding Risk Management (URM), Risk Identification (RI), Risk Assessment and

Analysis (RAA), Risk Monitoring (RMC) and Credit Risk Analysis (CRA). Following is

the equation used by these studies to understand the impact of the independent variable on

the dependent variable.

RMP = f (URM+RI+RAA+RMC+CRA)…………….. (4.1)

In the above equation, RMP is a response variable that is measured using five explanatory

variables such as URM, RI, RAA, RMC, and CRA.

4.4.1 Risk Management Practices (RMP)

The RMP means establishing an appropriate risk management environment, this can be

created by developing sound policies to identify, manage and measure the risk. Thus RMP

covers the framing of policies on different aspects of risk, identification of risk and

measurement of the risk using tools and techniques.

4.4.2 Understanding Risk Management (URM)

The URM is significant for bank employees to understand the factors causing risk, to make

them vigilant about the threats of banking operations and the risks that are inherent and

exposed in their business operations. A better understanding of risk management is also

important in the bank’s activities where managing risk is one of the most important

objectives of banks. A change in the attitude and mindset of the employees will drive them

to understand that managing risk is crucial for the bank’s success.

4.4.3 Risk Identification (RI)

This variable involves the procedure of identifying threats that confront to banks and

forecasting the happening of such an event. It means deciding in advance the positive and


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negative effects of each threat and also deciding the procedure to control threat. There are

several techniques that can be used in risk identification, such as crucial observation areas

inside and outside the banks, assigning responsibilities to the employees of banks to

identify specific risks. The risk identification process is normally conducted by the risk

analyst, who estimates the likelihood of the threats and identifies the procedure to control it.

4.4.4 Risk Assessment and Analysis (RAA)

RAA classifies the different risk according to the amount of damage it possibly causes.

This classification enables the management to divide risks that are enabling threat to the

existence of the corporation from those which can only cause slight damages (Rosman

2009). In other words in RAA the bank classifies the credit risks that are threatening the

existence of firm from those, which can cause a loss to the banks. A success of the banks

depends on its ability to assess risk strategically.

4.4.5 Risk Monitoring and Control (RMC)

The main task of the risk manager is to monitor, measure and control credit risk. The risk

manager's duty includes the identification of possible events or future changes that could

have a negative impact on the bank's credit portfolio and the bank's ability to withstand the

changes. In case of risk monitoring, the key areas to examine critically is counterparty's

financial position. Therefore, risk monitoring can be used to make sure that risk

management practices are in line which will help bank management to discover mistake at

an early stage.

4.4.6 Credit Risk Analysis (CRA)

This variable focuses on credit risk, as it is the most important risk which may drive the

corporate in the state of insolvency. Therefore, a detailed analysis of this risk will improve

the RMP of banks.

The present study focuses on credit risk. The reason being, credit risk is the most important

risk as it has a direct impact on the financial health, growth and survival of a bank. Also,


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the survey results show that credit risk is ranked one by the majority of respondents as seen

in table 4.1.

Table 4.1: Results of ranks given for different types of risk based on 116 respondents

Source: Authors calculations based on data collection instrument from appendix table A.3

In the data collection instrument, the respondents were asked to give a rank from 1 to 5 to

the different types of risk mentioned in table 4.1. The above table shows that the majority

of the respondents have given 1st rank for credit risk highlighting the significance of credit

risk faced by them amongst the other types of risk. This is due to the fact that lending is the

primary business of banks, which involves huge risk in case of contractual break of the

agreement by obligors. Therefore, the present study focuses on Credit Risk Management

Practices (CRMP) rather than the Risk Management Practices (RMP).

The CRMP involves designing an environment that includes creating awareness among the

bank staff on understanding the concept of credit risk, identification of sources and areas of

credit risk, analysis and assessment of credit risk, finally, monitoring and controlling credit

risk. Based on the above aspects, the data collection instrument (Appendix A.2) covers the

statements on the same.

4.5. Results and Discussion

This chapter consists of results and discussion into four parts. Part I (Sample

Characteristics), it explains the sample characteristics such as demographic profile, pilot

study, content validity, Part II (Identification and Classification of Determinants) comprises

of reliability analysis and Principal Component Analysis (PCA). Part III (Descriptive

analysis correlation analysis) consists of descriptive statistics on all the determinants of

S. N. Types of Risk Frequency Percentage

(%)

Rank

1 Credit Risk 91 84 1

2 Operational Risk 80 74 2

3 Market Risk 74 69 3

4 Exchange rate risk 63 58 5

5 Liquidity risk 85 79 4


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CRMP and the Pearson’s correlation coefficient. Part IV (Development of OLS model)

covers the regression analysis and the results of the Independent sample T-test.

4.5.1 Sample Characteristics

The sample characteristics is explained in terms of the demographic profile of the

respondents, description of the small scale preliminary study (pilot study) and an

explanation on the quality of items on the data collection instrument.

4.5.1.1. Demographic Profile

This data was collected through a predesigned structured questionnaire run on Credit Risk

Officers of Banks. Our sample size has been scientifically determined following the Krejcie

and Morgan (1970) sample selection rule. This rule shows that 113 Credit Risk Officers

would be an appropriate sample considering a population size of maximum of 160 Credit-

Risk Officers. In order to collect data, around 140 questionnaires were distributed through

Google Forms and as well as in-person to the Credit-Risk Officers of the banks. Twenty-

four respondents did not reply to the questionnaire due to their busy schedule, providing us

with an 83 percent response rate. The proportion of the sample between public and private

banks is 2:1 based on the number of credit risk officers in public and private banks

comprising 77 and 39 Credit-Risk Officers of public and private banks. Based on the 116

responses generated, the generalizations have been formulated.

Table 4.2: The demographic profile of the 116 respondents

Attributes Frequency Percentage

Type of Bank

Public 77 66 %

Private 39 34 %

Gender

Male 70 60%

Females 46 40%

Designation

Executive management 15 12%


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Middle level management 101 88%

Length of Experience

Less than five years 40 35%

Five years and Less than ten years 71 61%

Ten Years and longer 5 4%

Source: Authors Compilation based on the data collection instrument from appendix table A.3

Table 4.2 shows that gender composition of the sample is strongly influenced by males and

the majority of the risk officers belong to middle level management. The quality of the

responses will be more reliable as most of the respondents belong to the category of

possessing more than five years of experience.

4.5.1.2. Pilot Study

The questionnaire was first tested with a pilot study by taking responses from branches,

zonal and regional offices of public banks and private banks. The area selected for the pilot

study is the capital city of Goa (Panjim), wherein the regional, zonal and the main branches

of the banks were visited. The pilot study helped the researcher to understand the overview

of the Credit Risk Management Department (CRMD), the profile of the credit risk officers,

the location of the zonal, regional and the head offices etc. Thereafter, modifications were

made in the questionnaire based on the responses of respondents from the pilot study.

4.5.1.3. Test of Content validity

The Content Validity Index (CVI) was computed to provide evidence of content validity

and to assess the necessity of questions. The ratings of items were done by experts on three

factors, such as relevance, clarity and simplicity. A panel of six content experts was asked

to rate each scale item in terms of its relevance, simplicity and clarity to the underlying

constructs. These experts were selected from the banking and academic sectors. The

expert's opinion was analyzed using a quantitative method such as Content Validity Index

(CVI) to decide the quality of items. The minimum CVR for each item to be considered as

acceptable was 0.75 for a one-tailed test at the 95% confidence level, assuming that a

minimum of 8 judges was used for the study (Lawshe, 1975). The result of the content

validity Index showed a minor revision regarding the clarity and wording of items and


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those revisions were incorporated into the instrument. The detail discussion about content

validity is done in the research methodology chapter.

4.5.2 Identification and Classification of Determinants of CRMP

The result and discussion in this section include details on Principal Component Analysis

(PCA) and reliability analysis.

4.5.2.1 Principal Component Analysis (PCA)

In order to identify the determinants, Principal Component Analysis (PCA) is used. The use

of PCA in this study allows transforming and reducing the thirty one statements into a

smaller number of components and yet retaining a significant amount of information about

variance. The preliminary step for PCA is sample adequacy. The sample adequacy is

checked with the help of the Kaiser-Meyer-Olkin (KMO) test. In the present study, the

KMO values are above 0.5, the data for the models are found adequate in their collinearity.

The Rotated Component Matrix and Eigen value methods are used to decide the retention

of components.

Table 4.3: Rotated Component Matrix calculated from 31 statements from a questionnaire

Statements

Rotated Component matrix

Component 1

CRAA

Component 2

CRMC

Component 3

CRU

Component 4

CRI

Stat.26 .984 .102 .105 .038

Stat.23 .841 .109 .158 .123

Stat.13 .783 .025 .138 .102

Stat.2 .650 .166 -.053 .114

Stat.17 .612 .102 .091 .021

Stat.15 .510 .112 .103 .031

Stat.6 .113 .743 -.023 .135

Stat.29 .123 .683 .152 .082

Stat.18 .023 .612 .123 .131

Stat.24 .012 .523 .134 .026

Stat.10 .153 .514. .152 .023

Stat.21 .153 .450 .121 .052

Stat.27 .226 -.007 .780 .142


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Stat.22 .126 .113 .674 .014

Stat.11 .111 .081 .567 .091

Stat.1 .021 .112 .468 .233

Stat.4 .056 .176 .414 .145

Stat.12 .542

Stat.8 .503

Stat.16 .433

Stat.31 .413

Stat.19 .409

Source: Authors working based on data collection instrument from appendix table 4.2

The Rotated component matrix shows the retained rotated component loads on each

variable. The result of the rotated component matrix is given in Table 4.3 indicates the

inclusion of twenty-two statements and the exclusion of nine statements.

Table 4.4: Extraction of the sum of Squared Loadings

Component Eigen

values

% of

variance

Cumulative

%

Component 1 6.23 28.31 28.31

Component 2 4.08 18.54 46.85

Component 3 3.33 15.13 61.98

Component 4 2.41 10.95 72.93

Source: Authors Computation based on appendix table 4.2

The result in the above table indicates the retention of the four components, whose Kaiser -

Eigen value obtained is above one. In other words, twenty two statements are reduced to

four components. These four components put together explained 73% of variations in the

variables. The first component captured around 28% of variations. The second component

captured around 18% of variations, while the third and fourth component captured around

15 and 10 percent variations respectively.

Application of cattle Scree plot is yet another method used to determine the components to

retain in regression. A visual inspection of the Scree plot signifies the retention of four

components to be used in the regression based on the inflection point.


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Studies such as Al-Tamimi & Al-Mazoorie (2002), Al-Tamimi and Al- Mazrooei (2007),

Onyano (2010), Shafiq and Nasr (2010), Nazir (2012), Hussiney (2012), Gakure (2012),

Hussain (2015) and Ishtiaq (2015), Ngoroge and Ngahu (2017) were used as a guidance for

labeling the components (determinants). The data in table 4.5 led to the final conclusion of

grouping and labeling of factors as follows:

1. Determinant 1 has good correlation with the statement number 26, 23, 13, 2, 17 and 15

and will be labeled as CRAA (Credit Risk Assessment and Analysis).

2. Determinant 2 has good correlation with the statement number 6, 29,18,24,10 and 21

and will be labeled as CRMC (Credit Risk monitoring and Control).

3. Determinant 3 has good correlation with statement number 27, 22,11,1 and 4 and will be

labeled as CRU (Credit Risk Understanding).

4 Determinant 4 has good correlation with the statement number 12, 8, 16, 31 and 19 and

will be labeled as CRI (Credit Risk Identification).

The CRMP involves stages in a sequence such as Credit Risk Understanding (CRU), Credit

Risk Identification (CRI), Credit Risk Assessment and Analysis (CRAA) and Credit Risk

Monitoring and Control (CRMC), hence the regression equation of CRMP is as follows:

CRMP= f (CRU+CRI+CRAA+CRMC)…………… (4.2)

The original equation no 4.1 is modified as PCA has subsumed CRA in CRAA. Thus

modified equation consist of CRMP as a response variable which is measured using four

explanatory variables such as CRU CRI, CRAA, and CRMC,

Credit Risk Management Practices (CRMP)

This dependent variable is measured in the present study using six statements mentioned in

Annexure table A.3. These statements are based on the past studies such as Al-Tamimi &

Al-Mazoorie (2002), Al-Tamimi and Al- Mazrooei (2007), Onyano (2010), Shafiq and

Nasr (2010), Nazir (2012), Hussiney (2012), Gakure (2012), Hussain (2015) and Ishtiaq

(2015), Ngoroge and Ngahu (2017), who perform similar studies but in a different

environment. These statements are similar but not identical, having been revised after


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taking into account the environment and opinions of the experts. CRMP is evaluated based

on an appropriateness of banks credit risk management department, periodical preparing of

credit risk policy by bank, well documented credit risk management process that provides

guidance to the staff, assessment whether, the bank’s main objective is efficient credit risk

management, emphasis of the bank’s on recruitment of highly qualified people in credit risk

department, and whether application of Basel capital has improved the credit risk

management effectiveness in bank.

Credit Risk Understanding (CRU)

The CRU is the first independent variable used in the equation 4.2. This variable is

measured using five statements given in appendix table 4.2. These statements are related to

a common understanding among the employees of the bank and about the formal credit risk

management system, importance of managing the credit risk, understanding on different

types of risk and the risk evaluation techniques, and finally a clear understanding of bank

employees about the setup and understanding on responsibilities by the bank's staff. Every

banker, in the beginning, has to understand the portfolio of risk it has to face and basically

the credit risk it plans to take in the future. Al-Tamimi and Al- Mazoorie (2007) concludes

that a good understanding of different risk and risk management among banking employees

improves the potential of banks to manage risk. Thus, understanding credit risk is an

important determinant that can have a positive impact on the Credit Risk Management

Practices of the banks.

Credit Risk Identification (CRI)

In the present study, CRI is assessed by considering the following aspects such as

systematic procedure followed for identification of credit risk, presence of committee for

identification of credit risk, the frequency at which the changes in the credit risk is

identified by banks, a technique used for identifying credit risk. These aspects are measured

using statements from annexure table A.3.


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Credit Risk Assessment and Analysis (CRAA)

In equation 4.2, CRAA is the third independent variable used to measure impact on

CRMP.. The CRAA is measured by considering the aspects such as quantitative and

qualitative techniques followed for assessment, measurement of cost and benefit of

addressing risk, provision of well defined training for assessment of credit risk, presence of

computer based support system for assessment of credit risk to analyse the risks and the use

of standardized approach for assessment of credit risk.

Credit Risk Monitoring and Control (CRMC)

The CRMC is the last variable used in equation 4.1. This variable is evaluated by

considering six statements from annexure table A.3, such as evaluation of risk management

and control system within the bank, level of control in the bank, monitoring the

effectiveness of credit risk, evaluation of standard reporting system, monitoring of credit

limit, reporting of internal auditor, communication and reporting lines within a bank.

4.5.2.2 Reliability Analysis

The reliability of the data collected on Likert five point scales in a questionnaire from

annexure table A.3 was checked using Cronbach’s Alpha reliability coefficients, which

measure the consistency with which respondents answer the question within a scale.

Table 4.5: Reliability test on 36 statements based on 116 observations

Constructs Results

Credit Risk Understanding (CRU) 0.78

Credit Risk Identification (CRI) 0.73

Credit Risk Assessment and Analysis (CRAA) 0.81

Credit Risk Monitoring and Reporting (CRMR) 0.79

Credit Risk Management Practices (CRMP) 0.85

Source: Authors calculations based on primary data from appendix table 4.3


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Table 4.5 reports Cronbach’s alpha coefficient of all the variables is higher than the

prescribed level (0.70), indicating excellent reliability of constructs. The Coefficient of

Cronbach’s alpha for the study variables is also accordant with the results from several past

research studies, such as Al Tamimi and Mazoorie (2007) and Hussain and Al-Ajmi (2012)

4.5.3 Descriptive Statistics and Correlation Analysis

This part consists of the results and discussion on descriptive statistics of the determinants

of CRMP and correlation analysis of the independent determinants of CRMP. The

descriptive statistics describe the data in terms of mean, median, variance in data, Kurtosis

and the maximum and minimum value. The correlation analysis shows the relationship

between the dependent variable such as CRMP and the independent variable such as CRU,

CRI, CRAA and CRMC.

Table 4.6: Descriptive Statistics of 39 respondents of private and 77 respondents of public banks

Public sector banks Private banks

CRU CRI CRA CRMC CRMP CRU CRI CRAA CRMC CRMP

Mean 3.9 4.01 4.00 4.21 4.18 4.57 4.49 4.74 4.77 4.79

Median 4.4 4.2 4.16 4.33 4.66 4.8 4.6 4.6 4.83 4.83

Std. Dev 1.14 0.69 0.88 0.61 1.06 0.43 0.45 0.20 0.26 0.20

Kurtosis -0.01 -0.41 -0.11 -0.76 0.06 1.7 -0.03 1.7 0.42 2.8

Minimum 1.8 2.8 2.33 3.1 1.8 3.2 3.6 4.16 4 4.16

maximum 5 5 5 5 5 5 5 5 5 5

Count 77 77 77 77 77 39 39 39 39 39

Source: Authors calculations based on data from appendix table A.2 and A.3

The mean score of all the determinants affecting the CRMP in the case of the public sector

bank is lower than the private banks. This signifies that the contribution of the determinants

to CRMP in the case of private banks is more than the public sector banks indicating the

efficient ability of the private bank in managing the credit risk. The mean response on the

determinant CRU in the case of the private banks is higher compared to the public banks,

indicating the private banks have a good understanding of risk and risk management. The

mean score of CRAA and CRMC is also higher for the private banks signaling efficient and

effective assessment and monitoring of the credit risk. The standard deviation in the above

table shows that the variance in data in the case of private banks is lower than the public

banks. The value of kurtosis less than 3 in the above table indicates the normality of data.


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Table 4.7: The Correlation matrix calculated using 39 respondents of private and 77 respondents of public banks

Public Banks Private Banks

CRU CRI CRAA CRMC CRU CRI CRAA CRMC

CRU 1 0.403* -.063 .432* CRU 1 -0.12 .432* .253

CRI 0.403* 1 .147 .493* CRI -0.12 1 .094 .208

CRAA -.063 .147 1 .383* CRAA .432* .094 1 .490*

CRMC .432* .493* .383* 1 CRMC .253 .208 .490* 1

Source: Authors Calculation based on appendix table 2 *Correlation at 5% level of significance

The correlation analysis is conducted to meet the assumption of OLS regression. Table 4.7

shows the relationship between the independent variable, such as CRU, CRI, CRAA, and

CRMC. The relationship among the Independent variable of the banks shows the

correlation coefficient value less than 0.50. This shows that there is no problem of

multicollinearity.

4.5.4 Development of OLS Model

An OLS model is constructed to test the significant impact of credit risk determinants of

CRMP on CRMP of banks. The Model uses CRMP as the response variable and four

variables such as CRU, CRI, CRAA and CRMP as an explanatory variable. The

explanatory variable is derived through PCA. Each of these variables is important to

measure and examine the risk management practices of banks. According to Rosman

(2009), these variables have a positive relationship with risk management practices. A

study by Al-Tamimi and Al-Mazrooei (2007) supports the use of such an approach and

shows a positive relationship between RMP and aspects of the risk management process.

The (OLS) regression model is developed to test the following null hypothesis.

H01: There is no significant statistical impact of credit risk determinants on their respective

Credit Risk Management Practices (CRMP), of the public and private banks in India.

A study by Al-Tamimi (2007) showed that understanding risk and risk management, risk

monitoring and reporting and credit risk analysis has a positive relationship with risk

management practices. A conceptual study by Rosman (2009) found a positive relationship

between the RU, RI, RAA and RMC on the RMP of banks. Another study conducted by

Nazir et al. (2012) revealed that risk analysis, risk monitoring and understanding risk and


78

risk management are positively significantly contributing to risk management practices of

Islamic and conventional banks operating in Pakistan. A study by Jorge and Ngahu (2017)

shows that risk identification is positively significant to influence the CRMP. These studies

are conducted other than India and the above hypothesis is framed in context to Indian

banks. The Indian banks reflect unhealthy scenarios of NPA’s, which may be due to

improper CRMP. Therefore, the hypothesis states no statistical impact on the determinants

in improving CRMP.

This hypothesis also supports the Institutional Theory, as the theory claims that the

presence of uniform rules and regulations will lead to uniformity in the credit risk

determinants of CRMP between public and private banks. In other words, the statistical

impact of the credit risk determinants of CRMP between public and private banks will be

uniform.

The basic aim behind the development of the model is to test the above hypothesis and to

determine the significance of the independent variable on the dependent variable. The

model will answer the question as how much of the dependent variable changes for one unit

of change in an independent variable. Research in the past has used an OLS technique in

order to develop a model; hence present study used this regression to determine the

significance of independent variables on the dependent variable.

There were 77 cases of public sector banks and 39 cases of private banks considered to run

the linear regression. All the assumptions required to run the regression were checked. The

first assumption on linearity was checked with the help of the scatter plot. A scatter plot

showed the linearity of data. The second assumption of independence of observation was

met with the help of Durbin Watson Statistics. There was no problem with autocorrelation

as the Durbin Watson Statistics laid in the range of 0 to 4. The third assumption on testing

of outliers was checked with the help of casewise diagnostics. After removing the outliers

which were not representing the target population, 108 observations are used to develop the

model. The fourth assumption on testing for Homoscedasticity was made by observing the

scatter plot. The homoscedasticity test shows that residuals are randomly scattered. The

fifth assumption on the Normality test was satisfied by observation to histogram and the


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sixth assumption on multicollinearity was checked by observing the variance inflation

factor and tolerance value. There was no problem with multicollinearity as the variance

inflation factor was less than five, and the tolerance value was less than 0.2.

Table 4.8: Result of Regression calculated from 36 cases of private banks and 72 cases of public banks

Public banks Private Banks

Unstandardised

Coefficient

Unstandardised Coefficient

Determin

ants

β Std.

error

Beta Determina

nts

β Std.

error

Beta

Constant 18.67 6.387 Constant 8.175 3.684

CRU -.032 .211 -0.063 CRU 0.244 0.098 0.321*

CRI .042 .315 0.074 CRI 0.095 0.118 0.130

CRAA .029 .216 0.037 CRAA 0.320 0.208 0.380*

CRMC .214 .267 0.280 CRMC 0.101 0.134 0.141*

Source: Computed from primary data from appendix table 2 and 3 *Significance level 5% and 10%**

Public banks: Durbin Watson Statistics 1.967, ANOVA value F (4, 68 ) = .342, Sig = .342 Model Fitness:

R= 0.32, R2 = 0.16, AdjR2 = 0.11

Private Banks: Durbin Watson Statistics 1.847, ANOVA value-F (4, 32) = 8.069, Sig = 0.00 Model Fitness:

R= 0.709, R2 = 0.502, AdjR2 = 0.432.

4.5.4.1 CRU and CRMP

The impact of CRU on CRMP is tested with the help of Ordinary Least Square (OLS)

regression. The results of public sector banks in Table 4.8 shows that there is an

insignificant and negative impact of CRU on CRMP. In the case of private banks, the

regression results show a statistical significant positive impact of CRU towards the

improvement of the CRMP of these banks. Therefore, an inference can be drawn that there

is a lack of awareness among the staff members of public banks about the aspects of CRU,

whereas in the case of private banks, CRMP is significantly influenced by CRU. These

results are in line with Al-Tamimi and Al Mazoorie (2007).

It is essential to mention here that a clear understanding of credit risk and credit risk

management by employees of banks tends to add value in effective credit risk management

perspectives, particularly in dealing with different aspects of credit risk inherent and

exposed in the banking operations in India. Appreciation of banks' risk exposure, by board


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members, executive management, and other employees are significant for effective risk

management (Hussain and Azmi, 2012). The result of a public and private bank does not

support the Institutional theory, because the theory claims that CRU among public and

private banks is uniform, however the results show that CRU has an insignificant impact on

CRMP in case of public banks, whereas CRU has a significant impact on CRMP in case of

private banks. This violates the assumption of the homogeneity of institutional theory. The

theory is not working concerning independent variable CRU on account of difference in the

common understanding among the employees on the different aspects of CRU followed by

public and private banks in India.

4.5.4.2 CRI and CRMP

The impact of CRI on CRMP of banks in India highlights that CRI has an insignificant

impact on CRMP. These findings are contradicting with the results of relevant studies such

as Al-Tamimi and Al-Mazrooei (2007), Hussain and Al-Ajmi (2012), Ishitiq (2015). The

results are contradicting because these studies are conducted in different countries having

different economic background. The studies by Al-Tamimi and Al-Mazrooei (2007) and

Hussain and Al-Ajmi (2012) are conducted in the UAE and Ishitiq (2015) study is

conducted in Pakistan. Furthermore, these findings do not support the policy implications

of the Reserve Bank of India (RBI) guidelines. The RBI’s guidelines in its notification

stated that top management of banks should attach considerable importance to improve the

ability to identify the overall level of risk undertaken. However, in practice these banks are

not procuring full information from borrowers before granting a loan to them. Therefore,

the NPA's of these banks have shown an increasing trend over some time. As per the RBI’s

report, a fact from Business Standard (2019), reveals that total NPA's of banks has

increased by more than 300% in ten years from 2006-07 to 2016-17. This could be on

account of improper CRI by banks.

4.5.4.3 CRAA and CRMP

The result of public sector banks shows that there is an insignificant impact of CRAA on

the CRMP. This result shows that there is a lack of credit risk analysis and assessment

process in public sector banks. However, the results of private banks in India show a


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significant positive impact of CRAA on the CRMP. Therefore, an inference can be drawn

that CRMP in the private banking industry is significantly influenced by CRAA. These

findings are also compliable with the results of Al-Tamimi and Al-Mazrooei (2007) and

Ishitiq (2015), indicating a significant relationship between CRAA and CRMP in case of

Indian private banks.

The private banks are applying their skills before granting a loan to the customer.

Therefore, the NPA's of private banks are less compared to public sector banks. As per

RBI’s report, a fact from Business Standard (2019) reveals that public sector bank's Gross

NPA to Gross Advance ratio was 2.8% in 2006-07 and reached 11.8% in 2016-17.

Similarly, private sector banks exhibit statistics of the same ratio as 2.2% in 2006-07 to

4.3% in 2016-17. Rosman (2009), in its study believes that CRAA, particularly on

measuring risk in a banking institution, is vital for CRMP. Therefore, public sector banks

are expected to make a CRAA policy for having an effective CRMP. As per the guidance

note of RBI, all the local banks must implement a system for risk assessment and analysis

to fulfill the regulatory requirement. Guidance note on credit risk management direct all the

banks to make compact approaches for the risk assessment and analysis in order to provide

a robust framework for risk management in these financial institutions. In total, the results

of the public and the private banks are paradoxical, hence the findings are contradicting the

uniformity assumption of Institutional Theory. The public and the private banks are not

following the uniform norms, rules, practices for managing credit risk, hence CRAA of

public and private banks differs.

4.5.4.4 CRMC and CRMP

The relationship between CRMC and CRMP of public and private banks in India is tested

and shown in Table 4.7. According to the results, the public sector banks show an

insignificant impact of CRMC on the CRMP. This result shows that there is a lack of credit

risk monitoring and control processes in public sector banks. The results of the present

study concerning public banks throw light on the partial compatibility of the guidance note

on CRAA given by RBI. Finally, the results of the present study shows that public sector

banks are not following proper procedures for CRMC to improve the CRMP of banks.


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However, private banks show a significant positive impact of CRMC on the CRMP of

banks in India. Based on these empirical facts, it indicates that CRMC is an essential

determinant of CRMP in the case of private banks in India. These findings are compatible

with the results of relevant studies Al-Tamimi and Al-Mazrooei (2007), Hussain and Al-

Azmi (2012) and Ishitiq (2015).

Overall the contribution of CRMC to CRMP differs concerning public and private banks as

per the results are shown in Table 4.7. The findings are contradicting to the homogeneity

assumption of Institutional Theory. Finally, RBI guidelines given in the guidance note

states that top management of banks should attach considerable importance to improve the

ability to identify the overall level of risk undertaken. Considering this fact, guidelines of

RBI it is expected that all the banking institutions are required to implement a

comprehensive and rigorous mechanism of risk monitoring and control in India to have an

effective CRMP.

The result of regression demonstrates that CRU, CRI, CRAA and CRMC were found to

have statistical insignificant influence on the CRMP of public banks. This result helps us to

accept the null hypothesis (H01). The influence of CRU, CRAA and CRMC were

significant on the CRMP of private banks, which fails to accept the null hypothesis (H01).

The regression results of private banks are in line with Al - Tamimi (2007), Rosman

(2009), Nazir et al. (2012) and Jorge Naghu (2017).

4.5.4.5 Model Fit

An OLS regression was run to understand the impact of CRU, CRI, CRAA and CRMC on

the CRMP of public and private banks. Table 4.8 shows the results of regression analysis

on public and private sector bank's data. The Results of public sector banks show that

adjusted R2 is 0.11, which means that 11% of the variation in the dependent variable that is

CRMP is due to the explanatory variables (CRU, CRI, CRAA, and CRMC) and remaining

89% variation is due to other factors. F value is not significant at 1%; hence, we can say

that the overall Model is not a good fit. The value of beta (β) explains the contribution of

the independent variable. CRMC shows the highest beta value, which means its


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contribution is more than other independent variables in CRMP, whereas CRU contributes

negatively to CRMP.

In the case of private banks, results show that adjusted R2 is 0.43 which means that 43% of

the variation in the dependent variable that is CRMP is due to the explanatory variables

(CRU, CRI, CRAA, and CRMC) and remaining 57% variation is due to other factors. F

value is significant at 1%; hence, we can say that the overall model is a good fit. The value

of beta (β) explains the contribution of the independent variable. CRU and CRAA show the

most substantial beta value, which means their contribution is more than other independent

variables in CRMP. The results also reveal that CRU, CRI, CRAA, and CRMC have a

positive relationship with CRMP.

4.5.5 Comparison of the Determinants of CRMP between Public and

Private Banks

The present study also tries to seek the difference in the credit risk determinants of CRMP

between public and private banks in India. Thus, if there is a difference in the credit risk

determinants of CRMP between public and private banks in India, it means that the CRMP

of these banks differs. An independent sample T-test was conducted based on the

hypothesis that the mean score of credit risk determinants of CRMP of public banks is

equal to the mean score of private banks. This hypothesis supports the institutional theory,

as the theory claims that due to the presence of uniform rules and regulations by all the

banks, it will set uniform conditions of actions triggering risk management practices highly

homogeneous in banks. The identical regulatory pressures, norms, and practices will bring

uniformity in the CRMP of all the banks. Further, the data was satisfying the assumption

normality, hence Independent sample T-test was conducted.

Table 4.9: Result of T-test of 77 observations of public banks and 39 observations private banks

Hypotheses P-value Decision

Mean CRU of public banks is equal to mean CRU of private banks 0.000 Not supported

Mean CRI of public banks is equal to mean CRI of private banks 0.004 Not supported


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Mean CRAA of public banks is equal to mean CRAA of private banks 0.000 Not supported

Mean CRMC of public banks is equal to mean CRMC of private banks 0.000 Not Supported

Source: Authors calculations from appendix table 4.3, Note: Significance level at 5%

The result of the independent sample t-test fails to accept the null hypothesis. The result

shows a significant difference in the credit risk determinants of CRMP of public and private

banks with respect to all the four determinants. This result is supported by the result of the

regression analysis present study. The result of the regression analysis of public and private

banks differs. The result of the Independent sample T-test is contradicting to the uniformity

assumption of Institutional theory, on the ground that, CRMP of public and private banks

differs as per the result of T-test and regression analysis. The difference in the result of

public and private banks may be on account of weak determinants of CRMP of public

sector banks as the mean of public sector banks is low compared to private banks. Thus,

public sector banks need improvement in determinants of CRMP such as CRU, CRI,

CRAA and CRMC.

4.6. Summary

The aim of this study was to investigate empirically the influence of credit risk

determinants on CRMP of the public and the private banks operating in India. To achieve

this objective, the credit risk determinants of CRMP were identified with the help of data

collection tool such as questionnaire and data analysis tool such as Principal Component

analysis. Based on the literature review, it was found that most of the studies in the past

have used five determinants and all types of risk under the Risk Management Practices

(RMP). The present study found four determinants using a rotated component matrix of

Principal component analysis as it has subsumed the additional variable into an existing

variable. These four determinants do not present a good fit for the model in the case of

public sector banks, hence there is a need to add some more statements and find more

determinants.

The results of the descriptive analysis show that the mean score of private banks is better

than public banks. It means that the process followed for credit risk management in terms

of CRU, CRI, CRAA, and CRMC is superior for the private banks compared to public

banks.


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In measuring the impact of the credit risk determinants on their respective CRMP for public

and private banks, regression results reveal that explanatory variable Credit Risk

Understanding and Credit Risk Assessment and Analysis (CRAA) among the four

components are most influential in the contribution of CRMP of private banks.

The result of regression demonstrates that credit risk determinants were found to have

statistical insignificant influence on the CRMP of public banks. This result helps us to

accept the null hypothesis (H01). However, the same determinants were significant on the

CRMP of private banks, which fails to accept the null hypothesis (H01). The regression

results of private banks are in line with Al - Tamimi (2007), Rosman (2009), Nazir et al.

(2012) and Jorge Naghu (2017). Thus, the regression model fits the data well for the private

banks compared to public banks in India and the results for private banks are consistent

with previous studies like that by Al-Tamimi and Al-Mazrooei (2007), and Hussain and Al-

Ajmi (2012).

Further, these regression results are also supported by the result of the independent sample

t-test. The results of the t-test show a significant difference in the credit risk determinants of

CRMP between public and private banks. These contradictory results between the public

and the private banks violate the assumption of uniformity of the Institutional theory.

Finally, the results of the present study provided empirical confirmations that private

banks are generally better in understanding the credit risk, using techniques for

identification of credit risk, following proper process for credit risk assessment and

analysis, and adopting the right approach for credit risk monitoring and control. This

indicates the ability of these banks to manage risks efficiently in the future.

Chapter V Measurement of Credit Risk Management Practices

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Chapter V

Measurement of Credit Risk Management Practices

5.1 Introduction

The growing complexities of the bank’s business due to liberalization, deregulations,

technical up-gradation, and the dynamic operating environment, gave rise to credit risk.

Credit risk refers to the probability of counterparty failing to repay a debt to an

organization, arises due to lending activities. The credit risk is a curse for banks for

decades as it has a devastating effect in terms of financial loss to the banks. This demands

banks to deploy powerful CRMP, which aids in the measurement of credit risk.

Measurement of credit risk means quantification of risk from credit operations using

quantitative variables such as lending and financial ratios. Measurement of credit risk is an

essential mechanism for preventing financial losses and maintaining continuity of the

banking business. It provides a mechanism for monitoring credit risk and reliable reporting.

Today the banks are facing an increasing trend of Non-Performing Assets (NPAs)

triggering insight into credit risk measurement. Thus credit risk measurement perceives

higher significance due to the higher NPA level of Indian banks compared to the global

benchmark. As per the RBI report, the facts state that Gross NPA to Gross Advance ratio of

a public bank is 11.59 % (March 2019), as against it is 2.99% for foreign banks. For the last

few years, (NPAs) have emerged as an inevitable burden on the government and RBI.

Several steps have been undertaken by both to curb the increasing NPA of Indian banks,

but these steps proved to be unsuccessful. This situation drives the attention of the banker

towards understanding the financial position of its banks. Assessment of financial position

can serve the specific needs through reforms in financial data.

There are several ways to make reforms in the financial data of the banks, and one of the

conventional methods used is ratio analysis. Ratio analysis is a more sophisticated method

as it provides a more detailed explanation since it considers correlations between two

variables. Therefore, the present study used a credit risk related ratio analysis as a tool for


87

the measurement of credit risk. This tool helps in internal risk monitoring for banks. A

trend in specific ratios will help to draw generalizations about credit risk in public and

private banks.

In the present study, credit risk related ratios such as lending and financial ratios are used.

Lending ratios are loan related ratios, which convey data about asset quality and loan

account position of the banks. These ratios evaluate the position of the performing and

Non-performing assets, and the assessment of the loan account plays a vital role in meeting

the financial obligations of the business. This study measures credit risk using eight lending

ratios.

A measurement of CRMP with the aid of financial ratios assists in understanding financial

position in terms of liquidity, profitability, and leverage position of banks. The regulators

from time to time are in search of different methods to compare the financial position of

banks. Thus, measuring credit risk using financial ratios helps the bank regulators to

evaluate the comparative performance of banks. Data regarding the financial position of the

banks shows the effectiveness of CRMP adopted by banks. The present study measures

CRMP using liquidity, profitability, and leverage variable, with the assistance of financial

ratios. The reason for using these variables is, in subsequent chapter four models are used

to predict the bankruptcy of banks, and these four models used liquidity, leverage, and

profitability variable to predict the bankruptcy of banks. Therefore, financial ratios

measuring these variables are considered in the present study.

An analysis of profitability, liquidity, and leverage position, helps to understand the

financial position of banks as it is positively affected by these three variables, or one is

achieved at the cost of others. The liquidity, profitability, and leverage are found to be the

essential determinants for the success and survival of the banks; hence there should be an

optimum tradeoff between these three variables. Considering the above, the present study

finds the interrelationship between these three variables and measures CRMP using these

three variables. Thus study on correlation analysis will help the researcher to understand the

strength and the direction of the relationship. Further, the available literature states an


88

inverse relationship between liquidity and profitability and also between profitability and

leverage. An effort is made to judge the same concerning the Indian context.

These above issues drive the researcher to measure and compare the credit risk

management practices of public and private banks using lending ratio and financial ratio.

The banks need to divert their scarce resources towards the effective management of credit

risk based on the analysis of lending and financial ratios. A study on measuring and

comparing credit risk will help to understand the asset quality position of a bank. Similarly,

a study of financial ratios will help to analyze the trend in liquidity, profitability, and

leverage variables with the help of time-series and cross-sectional data.

The present chapter is an empirical chapter that summarizes the findings from the

secondary data. This chapter consists of results and discussion into three parts. Part I

explains the measurement and comparison of CRMP using the lending ratio of public and

private banks in India. Part II comprises of measurement and comparison of liquidity,

profitability, and leverage position the banks using financial ratios. Part III covers the

identification of the relationship between liquidity, profitability, and leverage.

5.2 Literature Review

The recent history of financial crisis 2008 induced financial analysts to develop literature

on the CRMP. It is found from the literature that the ratios for measurement of credit risk

differ from bank to bank, although all the banks face the same extent of credit risk. A study

by Thiagarajan (2011) found a significant difference between public and private banks

concerning specific key ratios. A study of Kattel (2016), found a significant difference in

the mean value of credit risk ratios of private and joint-venture banks in Nepal using a tool

one way ANOVA. A study by Kumar and Pourkiaei (2016) shows a significant difference

between public, private, and foreign banks with regards to specific ratios. A study by Rekhi

and Goel (2013) compared the performance of public and private banks, conclude that new

banks are more efficient than old ones.

The liquidity, profitability, and leverage are important determinants for the success and

survival of the bank. There should be an optimum tradeoff between all these three

variables. Some of the past studies developed a relationship between liquidity and


89

profitability in the case of the banking industry. These studies are Lartey et al. (2013),

Abdullah (2014), Malik and Khursheed (2016), Awais (2016), Nabeel and Hussain (2017),

and Sharma (2017).

Some studies in the past found a weak and insignificant relationship between profitability

and liquidity. A study by Lartey et al. (2013), found a weak relationship between liquidity

and profitability of the banks listed on the Ghana stock exchange. Similarly, a study by

Malik and Khursheed (2016) found e negative relationship profitability and liquidity. Also,

a study by Nabeel and Hussain (2017) concludes that cash and current ratios show negative

relations, whereas quick ratio and CAR show positive relations towards profitability.

Abdullah (2014) concludes that there is no significant relationship between liquidity and

profitability. However, a study by Awais (2016) found a significant relationship between

the liquidity and profitability of banks in Pakistan whereas, a study by Ahmed (2016)

reveals that there is a positive as well as an inverse relationship between profitability and

liquidity.

The leverage ratio measures the extent to which a bank has financial assets with equity.

Some of the studies found a positive relationship between profitability and leverage

variable. However, some other studies conclude the inverse relationship between

profitability and leverage. A study by Zafar and Fartyal (2015) concludes that leverage has

an inverse impact on the profitability of the banks. A study by Ebiringa (2012) found a

negative effect of leverage on the performance of the bank. A study by Taani (2013)

concludes that the presence of high leverage affects the return on banks. However,

Moghaddam (2017) study concludes that there is a significant positive effect of liquidity

and leverage on the earnings of the firm. Thus most of the studies claim a negative

relationship between leverage and profitability.

Literature in the area of understanding the relationship between liquidity and leverage is

less, as also the liquidity and its effect on the debt level have been a controversial issue

among scholars in finance studies. Prior studies have demonstrated that liquidity increased

debt level while other studies found that high liquidity was less leveraged and more

regularly financed by their capital. A study by Ghasemi and Rajak (2016) concludes that a

quick ratio has a positive effect on leverage, whereas the current ratio harms the leverage.


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There is a difference in the profitability, liquidity, and leverage position of public and

private banks in India. Thakarshibai (2014) concludes that there is an improvement in the

profitability of private banks compared to public banks. Balaji and Kumar (2016) study

concluded that the growth rate is higher for private banks, whereas public banks are lagging

in many financial parameters and are facing many challenges too. A study by Chintala

(2016), found that profitability is higher for private banks. Also, Katti and Vadrale (2018)

study concluded that the profitability position of the private bank is better than public

banks. A study by Khan (2018) and Koley (2019) concludes that the performance of private

banks is better as compared to public banks. Similarly, a study by Sharma (2017) found a

difference in the profitability of public and private banks. Patel and Bhanushali (2017)

study concluded that both sectors are profitable, and profitability is the greatest challenge

for banks. This literature shows that most of the studies focused on the profitability aspect

as a base for comparison between public and private banks. However, studies on banks'

liquidity and leverage as a base for comparison in the Indian context are very few.

5.3 Data and Methods

The present study is conducted to measure and compare the CRMP of public and private

banks in India using lending and financial ratio. In this study, the required data have been

sourced from audited statements of individual banks and RBI. Part of data is collected from

reputed data sources such as Centre for Monitoring Indian Economy (CMIE) and Indian

stats.com. The data was taken for thirteen years period from the year 2004-05 to 2016-17.

A total of 39 commercial banks (21 one Public banks and 18 private banks) were chosen as

sample based on the availability of data and the consequences of time limitations.

The ratio analysis technique is used to measure credit risk in select public and private banks

in India. Ratio analysis is the most powerful tool of financial analysis, and it is relevant in

assessing the performance in respect of liquidity position, operating efficiency, asset

quality, etc. In the present study, eight lending ratios are used to measure the credit risk of

public and private banks. These ratios are Gross NPA to Gross Advance Ratio, Net NPA to

Net Advance ratio, Total loan to Total Asset, Total loan to Total Deposit, Total Loan to

Total Equity, Provision for NPA to NPA, Capital Adequacy Ratio (CAR) and Return on

Asset (ROA). This study compares lending ratios between public and private banks using a


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parametric test such as an Independent samples t-test. The assumptions required to run the

independent sample T-test, such as normal distributions and homogeneity of variance were

checked. The sample data is consistent with these assumptions.

The standard One Way ANOVA test was conducted to test the difference is statistically

significant amongst the 21 public banks group and 18 private banks group. The

assumptions required to run ANOVA, such as continuous dependent variable,

independence of observation, normal distribution, and homogeneity of variance were

checked before the application of ANOVA. The sample data were made in line with these

assumptions. Further, a post hoc test was conducted to find, the bank which contributes to a

statistically significant difference in the group.

This study also used ‘Financial Ratios’ to measure the liquidity, profitability, and leverage

position of the public and private banks in India. In this study, nine financial ratios are used

to measure the above variables. These ratios are taken from the bankruptcy model used in

the further part of the present study. For the sake of simplicity, codes are given to financial

ratios, which are mentioned in the following table.

Table 5.1: Codes to Financial Ratios used by Bankruptcy Models

Liquidity Profitability Leverage

Financial Ratio Bankruptcy

Model

Financial Ratio Bankruptcy Model Financial Ratio Bankruptcy Model

L1- Working

Capital to Total

Asset (WC/TA)

Altman Z-score

Springate Model

Grover Model

P1 – Earnings

Before Interest and

Tax/Total Asset

(EBIT/TA)

Altman Z-score

Springate Model

Grover Model

LV1-Retained

Earnings/Total

Asset (RE/TA)

Altman Z-score

L2 - Current Asset/

Current liability

(CA/CL)

Zmijewski model P2 – Return on

Asset (ROA)

Grover Model

Zmijewski model

LV2-Market Value

of Equity /Total

Liability (MVE/TL)

Altman Z-score

P3 –Profit Before

Interest and

Tax/Current

Liability

(PBIT/CL)

Springate Model

LV3 – Total

Deposit /Total

Asset (TD/TA)

Zmijewski model

P4- Total Income

(TI)/Total Asset

(TA)

Springate Model

Source: Authors compilations


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Table 5.1 shows that the variable Liquidity is measured using Working Capital to Total

Asset (L1), and Current Asset to Current Liabilities (L2). The variable Profitability is

measured using Earnings before Interest and Tax to Total Assets (P1), Return on Asset

(P2), Earnings Before Interest and Tax to Current Liability (P3), and Total Income to Total

Asset (P4). While the Leverage is measured using Retained Earnings to Total Asset (LV1),

Market Value of Equity to Total Liability (LV2), and Total Deposit to Total Asset(LV3).

Table 5.1 shows that ratio L1 and P1 is used by most of the bankruptcy model.

Time-series data for a period from 2004-05 to 2016-17 and cross-sectional data of 21 public

sector banks and 18 private banks was used to compare the liquidity, profitability, and

leverage position of public and private banks in India. The parametric test, such as the

Independent sample t-test, was used for normal data, and the non-parametric test such as

Mann Whitney U-test was used for non-normal data. This test will help the researcher to

generalize and compare the liquidity, profitability, and leverage position of public and

private banks in India. Further, Pearson’s correlation coefficient is used to find the

correlation between liquidity and profitability, profitability and leverage, liquidity, and

leverage that will state the existence, direction, and strength of the relationship.

5.4 Result and Discussion

This chapter consists of results and discussion into three parts. Part I comprises

measurement and comparison of CRMP using lending ratios. Part II comprises of

measurement and comparison of CRMP using financial ratios, and part III consists of

identification of the relationship between the financial variables such as liquidity,

profitability, and leverage.

5.4.1. Measurement and Comparison of CRMP using the Lending Ratios

This part measures and compares the CRMP using eight lending ratios such as Gross NPA

to Gross Advance Ratio, Net NPA to Net Advance ratio, Total loan to Total Asset, Total

loan to Total Deposit, Total loan to Total Equity, Provision for NPA to NPA, CAR, and

ROA. Independent sample T-test and ANOVA is used to test the following null hypothesis.

H01: There is no statistical difference in the lending ratios between and amongst the public

and private banks in India.


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The above hypothesis states that the lending ratios mentioned above are the same for public

and private banks in India on the ground that both these banks perform a similar function,

i.e., accepting deposits for lending; therefore, they undertake a huge credit risk. This will

lead to uniformities in the ratios of public and private banks.

The lending ratios of all the public banks will be in par with the other banks in the public

sector group, as also the case of the private sector group. Due to this, all the public sector

banks and private banks will follow a uniform procedure before sanctioning the loan and

comes under a consistent regulatory regime. Therefore the mean lending ratios of all the

public and private banks will be at the same level; hence the above null hypothesis of one

way ANOVA states that all the public sector banks and private bank group means are

equal. The result of the independent sample-test and ANOVA are given in the following

table.

Table 5.2: Result of Independent sample T-test and ANOVA of 21 public and 18 private banks for a period 2005-2017

Tool Banks Independent Sample T-test One way ANOVA

S.D. Mean F-

value

P-

value

Remark

Gross NPA to

Gross Advances

Gross NPA =

Total Amount of

outstanding NPA

in borrower

account

Public 1.02 4.5

3.75

0.001

SD in ratio

among Public

and private

banks

F(15,192)=0.897,

P<0.001--NSD

Private 1.40 3.0 F(21,251)=5.986,

P<0.001----SD

Net NPA to Net

Advance Ratio

Net NPA =

Gross NPA –

Provisions on

Gross Advances

Public 0.66 2.46 5.56 0.000

SD in ratio

among Public

and private

banks

F(21,251)=0.875

P<0.001—NSD

Private F(15,192)=5.408,

P<0.001—SD

Total Loan to

Total Asset Ratio

Public 0.22 23.69

2.22

0.03 SD in ratio

among Public

and private

banks

F(21,251)= 2.53

P≤0.05—NSD

Private 0.46 15.69 F(15,192)=7.217, P

≤0.05—NSD

Total Loan to

Total Deposit

Ratio

Public 0.12 18.10 1.130 0.25

NSD in the

ratio of public

and private

banks

F(21,251)= 5.63

P≤0.05--SD

Private 0.99 F(15,192)=18.08


94

≤0.05--SD

Total Loan to

Total Equity

Ratio

Public 269 288 -1.2 0.2

NSD in a ratio

of Public and

private banks

F(21,251)= 27.43

P≤0.05--SD

Private 160 193 F(15,192)=18.90, P

≤0.05--SD

Provision to

NPA to NPA

ratio

Public 0.70 16.81 -1.8 0.06

SD in a ratio

of Public and

private banks

F(21,251)= 44.77

P≤0.05--SD

Private 0.5 F(15,192)=6.088, P

≤0.05--SD

Return on Asset Public .22 15.60

-2.60

0.009 SD in a ratio

of Public and

private banks

F(21,251)= 2.368,

P≤0.05--SD

Private .98 25.1 F(15,192)= 2.20 P

≤0.05--SD

Capital

Adequacy Ratio

Public 0.74 15.7 -2.60 0.009

SD in the ratio

of public and

private banks

F(21,251)= 4.144,

P≤0.05--SD

Private 4.3 25 F(15,192)= 7.53 P

≤0.05--SD Notes: Significant Difference (SD), Non-Significant Difference (NSD), Source: Authors Calculation using data from appendix table A.4

to A.21

5.4.1.1 Gross NPA to Gross Advance ratio

Gross NPAs comprises of all the non-standard assets like as sub-standard, doubtful, and

loss assets. The Gross NPA consists of (Balance in Interest Suspense account+ Claims

received from Deposit Insurance Credit Guarantee Corporation Export Credit Guarantee

Corporation and pending for adjustment + Part-payment received and kept in suspense

account + Total provisions held). The lower the ratio is, the better it is. The gross NPA is

always shown as a percentage of advances.

A time-series data presented in annexure table A.4 and A.5 shows all the public banks in

the decreasing trend of this ratio from the year 2005 to 2012. The ratio has gradually

increased from 2013 to 2017. This increase may be due to improper CRMP resulting in a

higher ratio. Similarly, all the private banks also show the decreasing trend of this ratio

from the year 2005 to 2013, and it shows a gradual increase in the ratio from 2014 to 2017.

This rise could be on account of an increase in the ratio of Catholic Syrian banks, City

Union Bank, ICICI Bank, and Nainital bank, as these banks ratio shows an increasing trend

continuously for a period of four years (2014 – 2017).

There is a statistically significant difference in the Gross NPA to the Gross Advance ratio

in the group of private banks as per table 5.2. A post hoc test result shows the statistical


95

difference lies in the Yes bank in the private bank group. However, One Way-ANOVA

result for public sector banks shows that there was no statistical difference in the mean

Gross NPA to Gross Advance ratio between different banks in the public sector group.

The annexure table A.4 and A.5 shows the gross NPA to gross advance ratio of 13% and

4% for public and private banks respectively in the latest years. This trend shows a

difference in this ratio between public and private banks, supported by the results of the

Independent sample T-test (Table 5.2), and this difference depicts a difference in the

CRMP of public and private banks.

5.4.1.2 Net NPA to Net Advance ratio

The Net NPA’s are calculated by deducting provisions from Gross NPA. Lower the ratio

better it is for the bank’s prosperity. Time series data presented in annexure table A.6

shows a decreasing trend in this ratio for public sector banks from 2005-2009; however, it

started rising from 2010-2017. The private banks, however, showed a fluctuating trend in

this ratio over thirteen years and was consistently low for these banks, as per annexure table

A.7. This shows the difference in the trend of public and private banks, and also, this ratio

is high for public banks. The average ratio reached 8.10 and 2.69 for public and private

banks, respectively, in the year 2017. All these facts reveal a difference in this ratio of

public and private banks supported by the results of the Independent sample t-test in table

5.2.

The result of one Way-ANOVA shows no statistically significant difference in this ratio

amongst different banks in the public sector group. However, private banks show a

significant difference amongst different banks. A post hoc test result shows the statistical

difference lies in the Catholic Syrian Bank and Yes Bank in the private bank group, as also

the ratio was highest for Catholic Syrian bank and lowest for the Yes bank as per annexed

Table A.7.

5.4.1.3 Total Loan to Total Asset Ratio

Loan to Asset ratio measures the total loan outstanding as a percentage of total assets.

Banks that have a relatively higher loan to asset ratio derive more of their income from loan

and investment.


96

Time series data presented in annexure A.8 and A.9 showed an increasing trend from 2005

to 2017 in the case of public and private banks, as both the sectors have crossed the ratio

over 0.5. This ratio 0.5 indicates that the loan sanctioned by the banks is more than half of

the assets present with the bank. This depicts, banks have increased their lending business

over some time, maybe to face fierce competition with each other and with the foreign

players.

The one way-ANOVA result shows a statistically significant difference amongst the

different banks in the public sector group. The post hoc test shows a statistical difference

lies due to the mean ratio of Syndicate and the United Bank of India. There was also a

statistically significant difference in the Total loan to Total Asset ratio of private banks. A

post hoc test result shows the statistical difference lies in the City Union Bank in the private

bank group.

The variance in the mean ratio among public banks is low, whereas among private banks is

high. This shows a difference in the ratio of public and private banks supported by the

results of the Independent sample t-test in table 5.2.

5.4.1.4 Total Loan to Total Deposit Ratio

This ratio helps to assess the liquidity and aggressiveness of bank management. This ratio

specifies how much a bank lends out of deposits it has mobilized. A higher the ratio

indicates, riskier a bank may be at higher defaults and more dependent on deposits for

lending and vice versa. A meager ratio indicates banks are not making full utilization of

their resources, and if the ratio is above a certain level, it indicates pressure on the

resources.

A time-series data presented in annexure A.10 and A.11 shows an increasing trend in this

ratio throughout 2005-2017. This signifies that banks have increased their loan business for

over thirteen years. The pattern of increasing trend also shows no difference in this ratio

between public and private banks. As a shred of evidence for the same, are the results of the

Independent sample t-test in table 5.2, which indicates that there is no statistically

significant difference in this ratio between public and private banks.

The one way ANOVA table shows a statistically significant difference amongst different

banks in the public sector group and private banks group. A post hoc test reveals the


97

difference lies due to the mean ratio of IDBI and ICICI banks from public and private

groups, respectively.

5.4.1.5 Total Loan to Total Equity Ratio

This ratio expresses the relationship between total loan outstanding and the total equity of

the bank. This ratio helps the investor to analyze the equity position of the bank to make an

investment decision. It also explains the extent to which shareholders' equity can fulfill the

loan obligations of banks. Lower the ratio better it is for the stakeholders.

Time series data presented in annexure A.11 and A.12 show an increasing trend over

thirteen years from 2005-2017. An increasing trend signifies that banks have expanded

their lending business. This hike could be on account of policies of RBI or may be due to

market factors such as the entry of new players. The trend also shows no difference in this

ratio of public and private banks, supported by the results of the Independent sample t-test

in table 5.2.

There was a statistically significant difference in this ratio amongst the different banks in

public and private sector groups as per one way ANOVA table. A post hoc test reveals a

statistical difference lies due to the difference in the ratio of State Bank of India and Bank

of Maharashtra in the public sector group and HDFC and DCB in private bank group.

Overall Total loan to total equity ratio shows that all the banks from the sample have high

loans outstanding compared to its equity capital. This shows that banks have financed its

loan through deposits and other borrowed funds. Although this strategy is favorable to

equity holders in terms of getting the benefit of "Trading on Equity", however, it may

endanger the bank’s financial position if the loan turns into NPA. Thus banks in a sample

have expanded their lending business at the cost of equity, which may endanger the

financial position of the banks in the long term due to financial instability.

5.4.1.6 Provision for NPA to NPA ratio

This ratio highlights the extent of provisioning done on the existing NPA, thereby

indicating future provisioning requirements in the event of no recovery from the stock of

NPA. It indicates the degree of safety measures adopted by banks and has a direct bearing

on the profitability, dividend, and safety of shareholders. Higher the ratio better it is for the

banks from a safety point.


98

Time series data presented in the annexed table A.14 shows a public sector banks had a

decreasing trend for the last thirteen years except for the year 2013. On an average

provision, this ratio is low private banks, and the trend of this ratio differs among public

and private banks, this fact is supported by the results of the Independent sample t-test in

table 5.2.

The One way ANOVA table shows a statistically significant difference in the mean of this

ratio amongst the banks from the private group and the banks from the public sector group.

A post hoc test was conducted to find which bank reveals a statistical difference. The test

result shows the statistical difference lies in the Nainital and Laxmi Vials bank in the

private bank group and Union Bank of India and State Bank of India from the public sector

group.

5.4.1.7 Return on Asset (ROA)

It is the profitability ratio measured in terms of the relationship between net profit and total

assets. The ROA is frequently applied to banks because cash flow analysis is more

challenging to construct accurately.

This ratio for public sector banks showed minor fluctuations in the trend from 2005-2013;

however, it has decreased drastically in the year 2014 to 2017. This may be due to negative

returns of the United Bank of India and Indian Overseas Bank, Corporation Bank, Dena,

IDBI, and Central bank of India. One way-ANOVA table shows that there was a

statistically significant difference in the mean ROA ratio of different banks in the public

sector group. A post hoc test result shows a statistical difference lies due to the mean ratio

of Indian banks and the Central bank of India. The ‘ROA’ of the private bank has increased

over some time. One way ANOVA table shows a statistically significant difference in the

ROA of these banks. A post hoc test result reveals that the difference is on account of

Kotak Mahindra Bank, HDFC, Catholic Syrian bank, in the private bank group. During the

year 2017, the ‘ROA’ of private banks is almost 200% higher than public banks.

Overall, ‘ROA’ of a private bank is better than public sector banks, indicating a better

profitability position and credit risk management strategy of private banks. This difference

is supported by the results of the Independent Sample t-test in table 5.2.


99

5.4.1.8 Capital Adequacy Ratio (CAR)

It is a ratio that measures the amount of bank capital expressed as a percentage of its risk-

weighted credit exposure. ‘CAR’ is precautionary ratio banks need to maintain to face their

NPA's The Basel III norms stipulated a capital to risk-weighted assets of 8%. However, as

per RBI norms, Indian scheduled commercial banks are required to maintain a CAR of 9%

while Indian public sector banks are emphasized to maintain a ‘CAR’ of 12%. The

annexed table A.18 and A.19 show all the public and private banks have consistently

maintained the CRAR norm of 8% as prescribed by BCBS and 9% prescribed by RBI. This

shows the precautionary role played by all banks to avoid financial distress.

Overall, the private sector banks ‘CAR’ is better than Public banks. This shows the

difference in the ‘CAR’ of public and private banks. This difference is statistically

supported by the results of the Independent sample t-test. One way-ANOVA table shows

the statistically significant difference in the ‘CAR’ ratio amongst the different banks in the

public sector group and private sector group. Further, a post hoc test reveals a statistical

difference in the public sector group is due to the ratio of Bank of Baroda and private sector

group is due to Yes Bank, Catholic Syrian Bank, and the Karur Vysa Bank.

The overall results reveal a statistically significant difference in the majority of lending

ratios selected, and this result fails to accept the null hypothesis. In other words, it means

that there is a significant difference in the extent of credit risk between public and private

banks. The extent of credit risk is less in private banks may be due to low and manageable

NPAs with these banks.

These above findings state that public sector banks are facing unfavorable scenarios of

these lending ratios compared to private banks. These findings are in line with Velling

(2010), Thiagarajan et al. (2011), and Kattel (2016).

5.4.2. Measurement and comparison of CRMP using Financial Ratios

The financial ratios are used to analyze the liquidity, profitability, and leverage position of

the banks. Liquidity explains the availability of cash for immediate use at any point in time.

Profitability explains the degree to which the bank yields financial gain, and the leverage

variable measures the bank’s vulnerability to downturns. The measurement of these

variables is done with the help of a financial ratio. Liquidity is analyzed using two financial


100

ratios; Profitability is analysed using four financial ratios, and leverage is analysed using

three financial ratios. This analysis is carried with the help of the following hypothesis.

H02: There is no statistical difference in the financial ratio (liquidity, profitability, and

leverage) between the public and private banks in India.

The difference in these variables may not arise because both the banks belong to the same

industry and performing similar functions, and comes under the uniform regulatory regime

of RBI. Therefore there may not be a difference in the financial ratios between public and

private banks.

5.4.2.1 Liquidity Analysis

Liquidity at banks is a measure of its ability to readily find the cash to meet the demand

upon it. The banks need to maintain adequate liquidity levels as low safety margins may

endanger the bank's financial position. Basel III regulations stipulated 3% as the LCR.

Before these regulations, the Indian banking sector was lacking the regulations on

quantitative minimum regulatory requirements. In the present study, liquidity is measured

using ‘WC/TA (L1)’ and the Current Ratio (L2). The bankruptcy model applied in the

present study, such as Altman Z-score, Springate model, and Grover model used L1 while

the Zmijewski model, used L2 as a proxy to liquidity variable.

Table 5.3: Result of Independent sample T-test for liquidity analysis of time series data from year 2005-

2017and cross-sectional data of 21 public and 18 private banks

Proxy to variable Time Series

data

Mean

Results of Independent

Sample T-test/Mann-

Whitney U-test

Cross

Sectional Data

(Mean)

Results of

Independent

Sample T-test

Public WC/TA

(L1)

0.087 T(24 ) = -117,P=0.908

(NSD)

0.089 T(37 ) = -0.028,

P=0.978 (NSD) Private 0.088 0.089

Public CR (L2)- 3.912 T(24 ) = -0.128, P=0.898

(NSD) Mann Whitney U-

test

3.91 T(37 ) = -0.345,

P=0.732 (NSD)

Mann Whitney U-test

Private 3.754 3.75

Source: Authors calculations based on Appendix table from A.22-A.25, Notes Significant Difference (SD), Non-

Significant Difference (NSD),


101

The ‘WC/TA’ ratio is used as a measure to indicate the liquidity, and the current ratio

measures the banks short term solvency. The higher the ratio, the more is the bank's ability

to meet current obligations and higher is the safety for the funds of creditors.

Conventionally, a current ratio of 2:1 is considered satisfactory. The current ratio of public

as well as private banks is better as both these sectors have crossed the standard ratio 2:1 as

per annexed table A.24.

Time series data on ‘WC/TA’ ratio presented in annexure A.22 for public banks showed a

lower value of this ratio in the year 2005 and was negative for private banks in the same

year. This shows the poor liquidity position of private banks fifteen years back. Up to the

year 2009, ‘WC/TA’ of both sectors of banks was improving; however, it decreased in the

year 2010 by almost 22% and 23% in the case of public and private banks, respectively.

Since both the sectors ‘WC/TA’ ratio was low in the year 2010, it may be due to the impact

of macroeconomic factors. According to Elliott (2014), a bank’s liquidity situations,

particularly in a crisis, will be affected much more than just these reserves of cash and

highly liquid securities. Time series data relating to the current ratio annexed in table A.24

shows an increasing trend over thirteen years for public sector banks, whereas private banks

depict minor fluctuations.

Due to such a weak liquidity position of banks, Basel III norms introduced a type of buffer

known as Liquidity Coverage Ratio (LCR) to cover unexpected cash flows. Overall the

liquidity positions of both categories of banks show an improving trend for the last fifteen

years. This shows a similarity in the trend of the liquidity position of public and private

banks, supported by the results of independent sample t-test result in table 5.3.

The cross-sectional data on descriptive statistics presented in the annexure A.23 and A.25

shows that Bank of Baroda and Nainital bank has the highest mean ratio as well as obtained

maximum value from public and private category respectively, depicting the best liquidity

position of these banks. This fact is supported by the results of the Altman Z- score and

recalibrated Altman model, which shows the highest score for Bank of Baroda and Nainital

bank. The mean and minimum ‘WC/TA’ ratio was low for IDBI bank (public sector banks)

and Kotak Mahindra Ltd. (a private bank) during the last fifteen years. This depicts the

weak liquidity position of these banks supported by the results of Altman Z-score, which


102

shows IDBI in the distress position, maybe due to liquidity factor. The average mean

current ratio was maximum in the year 2017 for public banks and 2015 for private banks.

This is the post-implementation period of Basel III, whereby due to stringent rules and

regulations on liquidity coverage ratio by Basel III, all the banks may have improved its

liquidity position.

Finally, both these ratios showed the same results. Using both the ratios, Bank of Baroda

and Nainital bank from the public and private category show a better liquidity position.

Similarly, IDBI and Kotak Mahindra bank from the public and private category depict a

poor liquidity position. This fact is supported by the results of the Independent sample T-

test. These results help to accept the null hypothesis. Thus the liquidity position of both

public and private banks reflects the same situation.

5.4.2.2 Profitability Analysis

In the present study, profitability is measured using first ratio, ‘EBIT/TA’ ratio (P1), which

explains the amount of pre-interest and tax profit available to discharge short term

obligations. The second ratio, Return on Asset (P2), explains the ability of the company to

use its assets to create its profit. The third ratio ‘PBIT to CL (P3)’ explains the amount of

pre-interest and tax profit available to discharge short term obligations and the fourth one

‘TI/TA (P4)’ indicates that a high ratio is a good indicator. However, a too high ratio is not

necessarily a good indicator as it may be at the cost of high-risk loans subject to default.

The Altman Z-score model used P1, the Springate model used P1, P2, and P3, the Grover

model used P1 and P2, whereas the Zmijewski model used P2 as a proxy to profitability

variable in their respective models.

Table 5.4: Result of Independent sample T-test for profitability analysis of time series data from year 2005-

2017and cross-sectional data for 21 public and 18 private banks

Proxy to

variable

Time Series data

Mean

Results of

Independent Sample

T-test/ Mann Whitney

U-test

Cross

Sectional Data

(Mean)


Sample T-test/ Mann

Whitney U-test

EBIT/TA-

Public

0.071 T(24 ) = 2.275, P = 0.084

(SD)

Mann Whiteny U-test

0.077 T(37 ) = -1.6, P = 0.0105

(SD)

Mann Whiteny U test


103

Private 0.072 0.079

ROA 0.666 T(24 ) = -1.9, P = 0.044

(SD)

Mann Whiteny U-test

0.667 T(37 ) = -2.5, P = 0.010

(SD)

Mann Whitney U-test

0.856 0.855

PBIT/CL 2.21 T(24 ) = -0.521, P =0.607

(NSD)

2.21 T(37 ) = -0.929, P = 0.359

(NSD)

2.05 2.5

TI/TA 0.085 T(24 ) = -3.01, P = 0.006

(SD)

0.084 T(37 ) = -4.4, P = 0.00

(SD)

0.093 0.092

Source: Authors calculations based on appendix table A.26-A.33. Notes Significant Difference (SD), Non-Significant

Difference (NSD).

Table 5.4 shows, majority of the profitability ratios reveals a significant difference in the

profitability ratios of public and private banks. These results fail to accept the null

hypothesis. This difference is supported by the fact that time-series data from the year

2005-2017 of annexure A.26, shows an increasing trend of ‘EBIT/ TA’ ratio of a public

sector bank. In contrast, in the case of private banks, it shows fluctuations. This ratio of

public and private banks was high in the year 2014 and low in the year 2005. Similarly, as

per annexure A.28, ‘ROA’ of public banks shows a decreasing trend from 2005-2017

signaling banks to improve their performance, whereas, in the case of private banks, it

shows fluctuations. During 2017 the ROA of a private bank was almost 200% higher than

public banks. Overall the ‘ROA’ of a private bank is better than public sector banks,

indicating a better profitability position and credit risk management strategy of this bank.

The ‘ROA’ of public banks has worsened over thirteen years, may due to increasing NPA.

The ratio of TI/TA ratio also supports the difference in the profitability position of public

and private banks. This ratio is consistently low for public banks in comparison to private

banks from 2005-2017.

The time-series data on ‘PBIT/CL’ ratio shows minor fluctuations and an increasing trend

from 2005-2017 for public and private banks. The mean ratio was lowest for both the

sectors in the year 2008. This may be on account of the marginal effect of the financial

crisis of 2008. This fact shows no difference in this ratio between public and private banks,

supported by the results of Independent sample t-test in table 5.4


104

The cross-sectional data on profitability ratios is presented in annexed table A.27, A.29,

A.31, and A.33. The cross-sectional data on ‘EBIT/TA’ shows Corporation bank (public

Bank) and City Union banks (private sector) show the highest mean ‘EBIT/TA’ ratio.

Similarly, the lowest mean ‘EBIT/TA’ ratio was for IDBI and Yes bank from public and

private banks, respectively. The cross-sectional data on ‘ROA’ shows Indian bank and

HDFC has the highest ‘ROA’ in case public banks and private banks, respectively. The

cross-sectional data on ‘PBIT/CL shows the highest values for Punjab and Sind Bank and

lowest for SBI. Similarly, in the case of the private category, it was highest for Karnataka

Bank Ltd and the lowest for HDFC. The minimum ratio of all the banks is below 1 in the

thirteen years of data period, and this signifies the insufficiency of profit to meet short term

obligations. The cross-sectional data on Total Income to Total Asset is highest for Indian

Bank and Kotak Mahindra bank among the public and private banks, respectively.

The trends in time series data cross-sectional data and the results of the t-test show a

significant difference in the profitability aspects of public and private banks in India. These

results are supported by the studies of Thakarshibai (2014), Balaji and Kumar (2016), Katti

and Vadrale (2018), Khan (2018), Koley (2019).

5.4.2.3 Leverage Analysis

The leverage variable measures the bank’s vulnerability to downturns. In the present study,

leverage is measured using ‘RE/TA (LV1)’, which indicates the ability of the firm to earn

profit and thereby securing retained earnings. The second leverage ratio ‘MVE/TL (LV2)’,

indicates the proportion of owners fund to the long term debt. A high value of ratio depicts

a firm's aggressiveness in financing its growth with debt. This rate shows the amount by

which the value of the assets of the firm can fall before the liabilities surmount the assets,

and thus the firm is insolvent. The third leverage ratio ‘TD/TA (LV3)’ measures the

relationship between total deposit and total asset.

Altman Z-score model used LV1 and LV2 as a proxy to leverage variable, and the

Zmijewski model used LV3 as a proxy to leverage variable in its models.


105

Table 5.5: Result of Independent sample T-test for leverage analysis of time series data from year 2005-

2017and cross-sectional data for 21 public and 18 private banks.

Proxy to

variable

Time Series data

Mean

Results of

Independent Sample

T-test

Cross

Sectional Data

(Mean)


Sample T-test

RE/TA

Public

0.049 T(24 ) = -4.4, P=0.000

(SD)

Mann Whiteny U-Test

0.050 T(24 ) = -4.8, P=0.000

(SD)

Mann Whiteny U-test

Private 0.111 0.110

MVE/TL 0.052 T(24)= -0.4.313, P=0.00

(SD)

Mann Whiteny U-test

0.053 T(24 ) = -3.954, P=0.00

(SD)

Mann Whiteny U-test

0.162 0.162

TD/TA 0.944 T(24 ) = -5.38, P=0.000

(SD)

0.943 T(37 ) = -1.4, P=0.000

(SD)

0.960 0.960

Source: Authors calculations based on appendix table A.34 to A.39. Notes Significant Difference (SD), Non-

Significant Difference (NSD),

Table 5.5 shows all the leverage ratio and reveals a significant difference in the leverage

position of public and private banks. This result is supported by the fact that the RE/TA

ratio of public banks shows a constant or stable position, whereas the private banks show

fluctuations from 2005-2017. Also, the ‘MVE/TA’ ratio is consistently low for public

banks in comparison to private banks from 2005-2017. The ‘TD/TA’ ratio as per annexure

A.38, shows minor fluctuations and an increasing trend for the thirteen years, in the case of

public and private banks.

The cross-sectional data on leverage ratio is presented in A.35, A.37, and A.39. The cross-

sectional data on ‘RE/TA’ shows the highest ratio for Punjab and Sind bank and the lowest

to the United Bank of India from the public sector categories. The highest mean ‘RE/TA’

ratio belongs to the Nainital bank and the lowest to Laxmi Vilas bank in the case of private

banks. The cross-sectional on ‘MVE/TL’ shows that this ratio is lower for public banks and

higher for private banks. State bank of India (Public bank) and Kotak Mahindra Bank

(Private bank) shows the highest mean ratio. This ratio shows the lowest mean for Punjab

and Sind Bank and Nainital banks from public and private banks, respectively. The cross-


106

sectional data on ‘TD/TA’ shows the highest ratio for Punjab and Sind bank and the lowest

to the State Bank of India from the public sector categories. The highest ‘TD/TA’ ratio

belongs to the Axis bank and the lowest to Kotak Mahindra Bank in the case of private

banks

5.4.3. Identification of Relationship between Financial Variables

The present study used Pearson’s coefficient of correlation to understand the relationship

between liquidity, leverage, and profitability variables. This study investigated the strength

and directions of the relationship that exists between two variables. A Pearson's correlation

generates a coefficient known as "r" that indicates the magnitude of relationship, which can

range from -1 to + 1. This matrix develops a relationship between profitability and

liquidity, profitability and leverage, and liquidity and leverage.

Table 5.6: Result of a correlation matrix of 21 public and 18 private banks from 2005-2017 taking into account 526

observations

Relationship between

Liquidity and

profitability

Type of

relation

Relationship between

profitability with

leverage

Type of relation Relationship

between

Liquidity and

Leverage

Type of

relation

Corr ( L1, P1)= -1.56

Weak

Negative

Relation

Corr ( P1, LV1)= -0.097 No correlation Corr ( L1,

LV1)=0.067 No correlation

Corr ( L1, P2)= -.165

Weak

negative

relation

Corr ( P2, LV1)= 0.021 No correlation Corr ( L1,

LV2)=-.028 No correlation

Corr ( L1, P3)= -.005 No correlation Corr ( P3, LV1)= -0.079 No correlation Corr ( L1,

LV3)=.219

Weak positive

correlation

Corr ( L1, P4)= -.188

Weak

Negative

Relation

Corr ( P4, LV1)=-0.023 No correlation Corr ( L2,


Corr ( L2, P1)= -0.233

Weak

negative

relation

Corr ( P1, LV2)=.087 No correlation Corr ( L2,


Corr ( L2, P2)= -0.137

Weak

Negative

relation

Corr (P2, LV2)=.022 No correlation

Corr

(L2,LV3)=

0.495

Moderate

Positive

correlation

Corr ( L2, P3)= 0.372 Weak positive

relation Corr ( P3, LV2)=.075 No correlation

Result: 60% of the cases shows

no correlation

40% of cases show a positive

correlation between liquidity

Corr ( L2, P4)= -.010 No relation Corr (P4, LV4)=.080 No correlation

Result: 63% result shows a negative Corr ( P1, LV3)=.192 Weak Positive


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correlation between

25% shows no correlation

12% shows a positive correlation

correlation and leverage

Corr (P2, LV3)=-.155 Weak Negative

correlation

Corr ( P3, LV3)=.677 Strong Positive

correlation

Corr (P4, LV3)=.012 No correlation

Result: 75% of the cases shows no correlation

16% shows a positive correlation, 9% shows a

negative correlation

* Sources: Calculated by Author using data from appendix table A.22-A.39 Notes: Annual Report (-0.1 to-0.3 weak negative relation,

0.1to0.30 weak positive correlation; -0.3 to -0.5 Average negative correlation, 0.3-0.5 average positive correlation; -0.5 to -1.0 Strong negative correlation, 0.5 -1.0 strong positive correlation)

5.4.3.1 Correlation between Profitability and Liquidity

Liquidity is needed for short term survival and profitability for the long term survival of the

entity. The literature review claims that there should be a tradeoff between liquidity and

profitability. Thus, this is a difficult situation for bankers to demonstrate, as both variables

have an inverse relationship with each other as per past studies. Hiriyogan (1985), argues

that in long-run relationship between profitability and liquidity could become positive. In

the study conducted by Abdullah, (2014), there is a short-run tradeoff between profitability

and liquidity.

In the present study, profitability is measured using ratios such as P1, P2, P3, and P4.

Liquidity is measured using two ratios L1 and L2. Thus the relationship of eight ratios is

being found using coefficient correlations. The coefficient table shows that there is a

significant weak negative relationship between three ratios, such as L1 and P1, L1 and P2,

L1, and P4. However, there is no relationship between L1 and P3. Similarly, the second

liquidity variable L2 shows a weak negative relation with two variables, i.e., P1 and P2, but

the same variable shows no relationship with P1 and P4.

The overall results show five variables out of eight variables, account for 63% of the

observations shows an inverse relationship between liquidity and profitability. This may be

the case of those banks, which satisfy the depositors, at the cost of profitability as

depositors ask for maximum liquidity as a guarantee for safety. Also, it may be the case of

other banks that satisfy their shareholders at the cost of liquidity. These findings of inverse

relationships are contradicting to the findings of Ahmed (2016). These variations in result

may be on account sample selected in its study is an international bank (Standard Chartered


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Bank) from Pakistan. Similarly, the variations in results are also found in a study by Lartey

and Samuel (2013). These contradictions may be on account of country differences as the

sample for the study is from Europe, North America, and Australia. Also, another reason

for the contradictory results may be due to the time gap, as the above studies were

conducted from 1972 to 1981, a period when investment management was not a serious

issue. However, findings of Awais and Khursheed (2016), is in line with the findings of the

present study. Thus, most of the time, to achieve the objective of better liquidity,

profitability is lowered as the cash is withheld and not invested to reap benefits. Therefore,

liquidity management should be done in such a way so that it has to develop a positive

relationship with profitability.

5.4.3.2 Correlation between Profitability and Leverage variable

Some of the studies found that there is a positive relationship between profitability and

leverage variable. However, some other studies conclude the inverse relationship between

profitability and leverage. Whereas some other finance researchers claimed that financial

leverage does not have an impact on profitability. Thus the issue of capital structure and the

performance of the banks remain controversial and puzzle issues around the world (Aragie

et al. 2015). The trade-off theory says that there is a positive association between

profitability and leverage. Pecking Order Theory claims that the firm can increase

profitability by using its inside funds rather than debts. In the present study, profitability is

measured using ratios such as P1, P2, P3, and P4. Leverage is measured using three ratios

LV1, LV2, and LV3. Thus the relationship of twelve ratios is being found using coefficient

correlations.

The matrix shows a weak negative relationship between LV3 and P2. However, it shows a

positive relationship with two variables, such as LV3 with P1 and also with P3. The matrix

shows no relation between the nine out of twelve ratios, which accounts for 75% of the

observations that do not show any relationship between leverage and profitability variable.

In other words, leverage of the bank is not affected by profitability and vice versa in the

present study. This shows that a capital structure choice does not have a relation with bank

performance. Finally, every bank should maintain an optimum leverage level to ensure that


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financing risk does not increase beyond an acceptable limit, which will decrease the returns

of the shareholder.

5.4.3.3 Correlation between Liquidity and Leverage

The leverage and liquidity variables are interlinked in a variety of ways. Past studies reveal

a positive as well as a negative relationship between leverage and liquidity. Thus there is a

controversial issue concerning leverage - liquidity relationship. The relationship between

these variables sometimes depends on the dividend policy. Higher the level of leverage will

put the firm in a poor position. Since in case of bankruptcy, the debenture holder has a

higher priority of settlement of debt, leading towards poor liquidity. This shows the inverse

relationship between liquidity and leverage. Also, sometimes the inverse relationship can

be seen through another way whereby increased liquidity lowers the cost of equity, making

equity more attractive. In other words, those firms which enjoy liquidity adopt significantly

less debt in the capital structure. There is also a positive relationship between leverage and

liquidity, such as a highly leveraged company hold the liquid asset to absorb the economic

shocks in the market.

The present study measures liquidity using two variables, such as L1 and L2, and it

measures leverage using three variables such as LV1, LV2, and LV3. Six observations are

used to judge the relationship between liquidity and leverage. In the present study, 60% of

the observations in the matrix table 5.6 do not show any relationship between liquidity and

leverage, and 40% of observations show a positive relationship between liquidity and

leverage.

5.5 Summary

The present study analyzed various lending ratios that could be useful as an internal risk

monitoring tool for the banks. Although there is similarity in the trend of specific ratios

over the thirteen years under study, the sector-wise comparison showed that there are

statistically significant differences between the two sectors with regards to specific ratios

such as Gross NPA to Gross Advances, Net NPA to Net Advances, Total Loans to Total

Asset, Provisions for NPA to NPA, CAR, ROA. However, there is no significant difference

in the two ratios, such as Total Loan to Total Deposit, Total Loan to Total Equity. Since


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there is a statistically significant difference in the majority of ratios selected, the results fail

to accept the null hypothesis. In other words, it means that there is a significant difference

in the CRMP of public and private banks due to fact that the NPA's of Public banks are

more than the private banks, this could be on account of better management of credit risk

by private sector banks then public banks. The literature review reveals that increasing

NPA worsens the efficiency of banks, and in turn, profitability decreases. The bad loan

Scenario of private banks as compared to their counterparts in the public sector is much

better (Firstpost April 2018). Also, an Analysis of NPA of different bank groups indicates

that public sector banks hold larger NPA then private banks (Arunkumar and Kotreshwar

2005).

Another tool used to analyze the lending ratios was one-way ANOVA. The result of this

test shows a statistically significant difference in all the public banks for all the ratios

except for Gross NPA to gross Advance ratio and Net NPA to Net Advance ratio, and this

result fails to accept a null hypothesis. Similarly, there was a statistically significant

difference in all the ratios of all the private banks, and this also fails to accept the null

hypothesis in the case of private banks too. Further, the post hoc test showed the bank,

which reveals a statistically significant difference.

The present study also used financial ratios to analyze the liquidity, profitability, and

leverage position of the banks. The Liquidity position of the bank is measured through L1

and L2 ratio. The result of descriptive statistics states that both public and private banks

were experiencing low L1 and L2 ratios from 2005-2015; however, the trend is improved in

the recent two to years, such as 2016-2017. The improvement may be on account of the

implementation of Basel III norms. We accept the null hypothesis based on the results of

the Independent sample t-test as there was no significant difference in the liquidity position

of public and private banks. In both the ratios, Bank of Baroda and Nainital bank from

public and private categories show the highest ratio. Similarly, IDBI and Kotak Mahindra

bank from the public and private category depict a poor liquidity position.

The profitability position of the bank is measured through P1, P2, P3, and P4 ratio. The

result of time series data analysis shows a decreasing trend of profitability in public sector


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banks and fluctuations in private banks. This type of trend shows a difference in the

profitability position of public and private banks, which is supported by the results of the

Independent sample T-test. The test result fails to accept the null hypothesis.

The leverage position of the bank is measured through LV1, LV2, and LV3. The result of

descriptive statistics shows the lower ratio for public banks and higher for private banks.

The time series shows the stable leverage position of public sector banks and fluctuating

trends in the case of private banks. These results show a difference in the leverage position

of public and private banks, supported by the results of the Independent sample T-test. The

test result fails to accept the null hypothesis.

Lastly, this study examined the relationship between liquidity profitability and leverage

with the help of the Pearson’s correlation coefficient. Present study found an inverse

relationship between liquidity and profitability, and no relation between profitability and

leverage and also between liquidity and leverage.

Chapter VI Application and Recalibration of Bankruptcy Models in Indian Banking Sector

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Chapter VI

Application and Recalibration of Bankruptcy Models in Indian

Banking Sector

6.1 Introduction

The bank frauds and the bank crisis have been a crucial part of the Indian financial system.

Most of the banking failure, in 1910 took place in Punjab. The location of banking failures

then shifted significantly towards Southern India and West Bengal. In all over India, 350

banks closed from 1913 to 1934. These factors led to the evolution of the Reserve Bank of

India (RBI) and the development of the banking regulation act. Even with the presence of

these regulations, banks witnessed bad loans crisis leading them towards the state of

bankruptcy. Till today, giant scams are happening as the case of Kingfisher Airlines and

Gitanjali Gems and Jewelry, whereby the proceedings are in process in the court and banks

are not able to recover their dues.

The other scenario of Indian banks shows that they operate in the competitive financial

sector. The competitive pressure for Indian banks from within the country, and cross

border forces banks to lend money, neglecting the deep assessment of the financial position

of the borrowers. This may increase bad loans, further leading banks to a bankruptcy state.

The bankruptcy is a state, whereby the banks become bankrupt when their assets are worth

less than their liabilities. Banks may also become insolvent, if they cannot pay its debts as

they fall due even after having more assets then liabilities, this is due to liquidity problems.

A bank failure is a major concern to the economy around the world as it creates high cost

and heavy losses to the individual banks and society. According to Lin Huljuan, (2015), the

effect of the bank becoming insolvent often leads to adverse consequences for many

stakeholders. It affects individual banks in terms of direct and indirect costs. The direct

cost will be in terms of the legal and administrative cost associated with bankruptcy

proceedings and indirect cost will be in terms of loss of depositor’s confidence, withdrawal

of amount from the bank and avoidance of investment by the customer. Similarly, it affects

the global economy in different contexts. A failure of giant banks may traumatize not only


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the domestic economy but also put the global at stake (Mayanka and Sharma 2013). The

failure of one bank has a spillover effect on the other banks and throughout the world

economy. According to Lawrence (2015), the failure of business organizations has a

significant economic effect on their owners, creditors and society overall. Thus the

prediction of bankruptcy may be highly beneficial to the individual bank, government and

also to the other stakeholders. The prediction of bankruptcy remains a concern for the

various stakeholders in a company, including the owners, managers, investors, creditors

and business partners as well as government agencies (Martin et al. 2011). If the

bankruptcy could be predicted with a reasonable accuracy ahead of time, banks could better

protect their business and could take action to minimize risk and loss of business, perhaps

even to prevent bankruptcy (Ramage and Pongstal 2004). The bankruptcy Study is

important for auditors as they are acknowledging the probability in the going concern of the

bank Jouzbarkartol (2013). Government officials can use the financial distress model to

forecast the financial status of some industries. Further, the bankruptcy study will provide

information on variables that will influence the health of the bank (Stingar and Warstuti

2014). A proper prediction of a firm’s bankruptcies might, therefore, be extremely

important to the relevant financial actors. There is a need for early warning of financial

distress due to changes happening in the recent economic environment. Thus, to avoid the

risk of bank failure, banks should find reliable ways to predict bankruptcy.

The review of past studies shows different methods available for predicting bankruptcy.

One of the methods for bankruptcy study is the Scoring model. The Scoring model is a

linear combination of the accounting variables, weighted by the coefficients which provide

a relevant score. The output of the scoring model is compared with a standard value to

determine the financial health of the banks. The score value calculated using models is

useful to public sector banks to demand loans from the Reserve Bank of India (RBI) or any

other funding agency (Pradhan 2014). The scoring model has the ability to classify banks

into different predefined groups through an appropriate tool that replaces the human

assessment. The scoring model has become very popular during the last 40 years in the

credit risk applications, forming a vast and fast-growing literature. The basic principle of

the scoring models is to determine the factors that can influence the default probability and


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combine them into a relevant score. These factors are, accounting variables that are

weighted and used into a multivariate model. According to Imanzadeh (2011), the

bankruptcy prediction model is a technique for anticipating the future condition of the firm,

which estimates the possibility of bankruptcy by combining a group of financial ratios.

There are many other models available for bank failure predictions such as Standard and

Moody’s financial ratios, Beaver model, Altman Z- score Model, Ohlson’s Model, CAMEL

Model, Grover Model, Springate Model, Neural Network and Zmijewskis Model to predict

the health of the bank. Among many models available for evaluating the financial

performance of banks, a literature survey shows that the majority of international failure

prediction studies employ the Altman-Z score model, Grover model, Springate model and

Zmijewski model. In the past, Vaziri (2012) and Warastuti (2014) used these scoring

models for predictability of failure of the financial institution and the models were found to

have high predictive ability. Since the model has the high predictive ability, the present

study applies these models to an Indian banking sector.

According to the study of Kleinert (2014), the existing literature showed that a single

bankruptcy prediction model faces limitations and multiples bankruptcy prediction models

improved the prediction of accuracy in bankruptcy prediction. Therefore, the present study

applies multiple models to assess the bank’s bankruptcy. Some of the researchers applied

multiple models and its comparison to find the accuracy and predictive power of the models

such as Imanzadeh (2011), Kordlar (2011 ), Vaziri (2012), Karamzadeh (2013), Avenhuis

(2013), Hussein (20 14), Timmermans (2014), Sajjan (2016), Aminian (2016), Stinjak et

al. (2015), Primassari (2017), Monousaridis (2017) and Syamni (2018). After comparison,

some of the studies criticized certain models concerning their predictive power, selection of

variables, the time factor, accuracy rate, change in the economic environment, etc.

A study by Grice and Dugan (2001) stated that when the population of the firm differs (e.g.,

Country) from the original methodology accuracy rate of model changes. According to

Sartija and Jeger (2011), financial ratios that were, less important in one period becomes

more important in the next period. Thus, the model does not provide credible results under


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all conditions. The findings of a study by (Grice and Dugan, 2001) reveal that predictions

are very sensitive to the period. The author notes the fact, when applying the model in a

different period, then those considered when the models were developed, accuracy is

significantly affected. In addition, the bankruptcy model fails to take into account non-

quantitative factors such as market psychology, legal issues, and knowledge; hence, they

lack accuracy, especially when applied to business outside the U.S. and emerging countries.

Till today, a scoring model with high model accuracy stills remains a challenge since no

model performs with a 100% accuracy rate. According to Timmerman (2014) in its study, it

is said that when an old original model is applied to a more recent sample, the predictive

power of the model is very low, and the bankruptcy is over predicted. According to Henley

and Hand (1997), the common problem that arises using a scoring model is population

drift, reject references and sample selection bias. The population drift is a tendency that

population change over time as the environment in which population activates changes.

Sample selection bias is another problem that arises when you construct a new model based

on the unbiased training test. This proves that there is a need to recalibrate the Altman Z-

score model by changing coefficients of the original model and using current data. Its study

found better results after the recalibration of these models.

The problem is that the prediction models used in this research might have been used for

other industries rather than banking and, or may be dated. Thus, the models may not

provide credible results under all conditions and may need some kind of calibration.

According to Timmerman (2014), when an old original model is applied to a more recent

sample, the predictive power of the model is very low, and the bankruptcy is over

predicted, its study also found better results after the recalibration of bankruptcy models.

Due to this fact, the present study in the spirit of Timmermans (2014) recalibrates these

models by changing the coefficients of the original model using current data. This may lead

to another problem. There may be a difference in the predictive ability in the original and

the recalibrated model. This demands a need for comparing the results of both the models

with the robust test, to decide the accuracy of the followed models.


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Based on the above facts, the present study analyze the risk of bankruptcy of public and

private banks using the bankruptcy models, judge their accuracy and ranks the banks based

on its score. The public and the private sector banks belong to the Indian banking sector.

There is a need to study bankruptcy prediction of the public and the private banks in India,

because the NPA of these banks is increasing year by year. Although both the types of

banks are facing the illusion of NPA’s, the extent of NPA is more in the case of public

banks than private banks. As per the RBI report, the Gross NPA shows an increasing trend

from the year 2009 (2.31%) to 2019 (9.1%). This fact of increasing NPA’s leading banks

on the path of bankruptcy. In such a condition, it becomes necessary to assess the financial

state of the bank and estimate their distress probabilities. Hence, distress prediction model

(bankruptcy prediction model) comprises a valuable tool in the hands of stakeholders who

need to estimate the business risk. In the present study, model recalibration is covered, as

the existing model loses its importance due to time factors, changes in the economic

environment, and other factors.

The present chapter is an empirical chapter that summarizes the findings from the

secondary data. This chapter consist of results and discussion into five parts. Part I consist

of application and the recalibration of the Altman model, results of independent sample t-

test and results of the robust test, Part II comprises of application and recalibration

Springate model, results of independent sample t-test and results of the robust test. Part III

comprises of application and recalibration Zmijewski model and results of independent

sample t-test. Part IV comprises of application and recalibration Grover model and results

of independent sample t-test. Lastly, Part V consists of a ranking of banks as per

bankruptcy models and comparison of bankruptcy scores between public and private banks.

6.1.1. Operational Definitions of Bankruptcy

In general, bankruptcy is defined as a failure of the organization in carrying out operations

to achieve its objectives. The bankruptcy is an accumulation of mismanagement of the

company in the long run Rudianto (2013). According to Lesmana (2013), the definition of

bankruptcy is a risk, which is related to uncertainty about the ability of a company to

continue its operations the financial condition, which is owned, has decreased. In simple


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words, the bankruptcy is usually interpreted as a failure of the company in running the

company’s operations to generate profits.

According to Pambekti and Hussain (2014), the bankruptcy is a situation in which the

company’s assets exceed liabilities and generally occurs due to the lack of capital, as it does

not utilize capital resources well, not maintain sufficient cash and inefficient management

in all activities. The bankruptcy usually does not only show up in the company, but it can

be indicated or identified early. Financial difficulty is an early indication of the bankruptcy

of the company (Haryetti, 2010). Another important issue is the existence of the common

mistakes that interpret financial distress and bankruptcy are the same things. Financial

distress is just an early indicator of company bankruptcy. A company experiencing

financial distress does not mean it is bankrupt, if the financial condition appears to have

been improved.

6.2 Review of Literature

The result of improper credit risk management may be the breakdown of banks, further

leading to the bankruptcy of banks. A review of the available literature shows that different

methods are available for predicting bankruptcy. One of the methods for bankruptcy study

is the scoring model. This study covers the reviews of scoring models such as the Altman

Z-score model, the Springate model, the Zmweskis model, and the Grover model.

There was extensive literature found on the Altman Z-score model. This model was applied

by many researchers in the past such as Chaudhary and Nandi (2011), Popker (2013),

Sharma and Mayanka (2013), Chieng (2013), Chotalia (2014), Pradhan (2014), Lin

(2015), Maina and Sawka (2017), and Khaddafi et al. (2017).

A study by Nandi and Chaudhary (2011) developed an internal credit rating model for

banks with the help of the Altman Z-score model and found that the developed model is

more accurate than then Altman Z-score model. A study by Popker (2013), Sharma and

Mayanka (2013) applied the model in Indian banks and conclude that the performance in

the selected sample is under the safety zone. A study by Chieng (2013) applied the Altman


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Z- score model on Eurozone bank’s failure and found it as a reliable predictor. A study by

Chotalia (2014) applied the model and concludes that sampled private banks are uncertain

about credit risk. Pradhan (2014) applied Altman Z-score and Neural Network and

concludes that the neural network technique outperforms the Altman model. Lin (2015), in

its study, found that ratio (Sales/Total Asset) from the Altman model has little contribution

to distinguish the bankrupt and non-bankrupt firm, hence its study renewed the original

Altman model and concludes, renewed model outperforms the original Altman model.

Maina and Sawka (2017) applied the Altman model and the result indicates that the

financial health of the listed company needs to be improved. Kaddafi et al. (2017) tested

the Altman model and concludes that Altman has good predictive power. Thus above

reviews applied the Altman model, which helped the researcher to understand the

methodology of the model.

Some of the researchers applied multiple comparative models to find the accuracy and

predictive power of the models. These studies include Imanzadeh et al. (2011), Kordlar

(2011), Vaziris et al. (2012), Kumar and Kumar (2012), Karamzadeh (2013), Avenhuis

(2013), Husein (2014), Kleinert et al. (2014), Timmermans (2014), Sajjan (2016),

Aminian., et al. ( 2016), Warstuti, and Stinjak, (2014), Wati and Hidayat (2015), Primasari

(2017), Monousaridis (2017), and Syamni et al. (2018).

A study by Vaziris et al. (2012) conducted comparative predictability of failure of the

financial institution and concludes that the Z-score model makes the best prediction.

Karamzadeh (2013) worked on the comparison of Altman and Ohlson model to predict

bankruptcies and concludes Altman works better. A study by Primsari (2017), made a

comparison of Altman, Grover, Springate, and Zmijewski model to find its accuracy and

concludes that the most accurate model is the Altman Z-score. Another study by Syamni et

al. (2018), applied Altman, Ohlson, Grover and Zmweskis model for predicting bankruptcy

of coal mining companies in Indonesia. Its study found that Ohlson and modified Altman

were dominant in prediction. Another study by Chieng (2013), suggests that Altman Z-

score is a reliable predictor.


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Some of the studies found that the Altman model gives a correct result, however, some

other studies propounded for the renewal of the Altman Model as it gives a higher error

rate. Such as Shumway (1999) proved that the Z-score model is dead and not trustworthy

anymore for predicting corporate bankruptcy. A study by Altman (2000) discussed two

models, such as the Z-score model - 1968 and Zeta Model - 1977 and its study

demonstrates that the Zeta model has improved accuracy over the existing failure of the Z-

score model. A conceptual study by Cantemir (2014) criticized the Z-score model. A study

by Timmermans (2014), tested the accuracy of Altman, Ohlson and Zmijewski model after

its recalibration and found that the accuracy of the model increased after recalibrations. A

study by Warstuti and Stinjak (2014), determined the variables that affect the level of

health of the banks by using Grover, Altman, Springate, Ohlson and Zmijewskis Model, its

study found working capital has a positive influence on the health of banks in all models of

bankruptcy, except in the model of Altman -1973. A study by Monousaridis (2017) applied

Altman, Springate, Zmweskis and Grover’s model on the firms from the emerging market

and its study concludes that the Altman model is outdated. Kumar (2016) applied multiple

models such as Altman Z-score, Ohlson’s O-score and Zmweskis model on Texmo

industries and concludes O-score model is proposed with better prediction performance.

The Springate model is the second model used in the present study, which was introduced

by Gordon LV Springate (1978). A study by Sajjan (2016), aimed at presenting a

theoretical foundation and compares the result of two models, such as Zavgren and

Springate. The result indicates the adjusted Springate model was efficient than another

model. Also, a study by Warstuti and Stinjak (2014) and Manousaridis (2017), express this

model is better compared to the Altman model. However, few other studies such as,

Imanzadeh (2011), Aminian et al. (2016) and Primsari (2017), found other models more

accurate than the Springate model.

The Zmijewskis model is the third model used in the present study, which was developed

by Mark E. Zmijewski in the year 1978. Many studies applied this model, such as

Timmermans (2014), Warastuti and Stinjak (2014), Kumar (2016) and Manousaridis

(2017). Some of the studies used multiple models and found the Zmijewski model as the


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best predictor. These studies are Husein (2014) and Imanzadeh (2011), whereas

Timmermans (2014), tested the accuracy of the Zmijewski model and found better results

after recalibration of this model.

The Grover model is the fourth model used in the present study, which was developed by

the restoration of the Altman Z- model by Jeffrey S. Grover. A study by Aminian et al.

(2016) and Qumruzzaman (2016), shows that the Grover model reflects a better bankruptcy

prediction whereas a study by Primsari (2017), found Grover as the least accurate model.

.

The above reviews on comparison of models show that some researchers found better

results using the Altman model, while other studies found better results using the Springate

model. Similarly, few other studies found Zmijewski and Grover's model works better.

Further, some other researchers have criticized these models on the ground of its predictive

ability and propounded for the renewal of these models as it gives a higher error rate. The

reviews assisted the researcher in identifying models, understanding the critiques of the

model and focusing on the recalibration of these models.

Some other reviews present a divergent view on all of these four models. These reviews are

Avenhuis (2013), Kordlar (2011), Wati (2015), who examined the predictive ability of

Zmweskis, Ohlson and Altman model for measuring the financial performance of listed and

delisted the banks of Indonesia stock exchange. Its prediction shows, there exists a smaller

difference in the predictive ability of these models.

6.2.1 Description of Models

In the present study, four bankruptcy models, such as the Altman Z-score model, the

Springate model, the Zmweskis model, and the Grover model is used. These four models

used different accounting variables to arrive at a bankruptcy score, and the bases of

discrimination differ from model to model. A brief description of the model is presented in

Table 6.1.


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Table 6.1: Description of bankruptcy models

N

o.

Model Equation Description Bases of

Discrimination

1

Altman

(1993)

Z-score = 6.56X1 +

3.26X2 + 6.72X3 +

1.05X4

Z = Altman-Z-Score

X1 = Working Capital/Total Asset

X2 = Retained Earnings/Total Asset

X3 = Earnings Before Interest and

Taxes/Total Asset

X4 = Market Value of Equity/Total Debt

Z < 1.10 = Distress

Z = 1.10-2.60 = Grey

Z > 2.60 = Safe

2 Springate

(1978)

S-Score = 1.03X1 +

3.07X2+0.66X3+0.4X4

S = Springate Score

X1 = Working capital/Total asset

X2 = Profit Before Interest and Tax/ Total

Asset

X3 = Profit Before Tax/Current

Liability

X4 = Sales/Total asset

S > 0.862 = Safe

S < 0.862 = Distress

3 Grover

(2001)

G- Score = 1.650X1 +

3.404X3 – 0.016ROA +

0.057

G = Grover Score

X1 = Working Capital/Total Assets

X2 = Earnings Before Interest and

Tax/Total Assets

ROA = Net Income/Total Assets

G ≤-0.02 = Distress

G ≥ 0.01 = Safe

4 Zmijewski

(1983)

X- Score = - 4. - 4.5X1

+ 5.7X2

– 0.004X3

X = Zmijewski Score

X1 = ROA (Net income/ Total assets

X2 = Total Liabilities/Total Assets

X3 = Current Assets/Current Liability

X > 0.5 = Safe

X < 0.5 = Distress

Source: Compilation of data based on literature review


This chapter used secondary data, collected from various sources such as audited

statements of individual banks and reputed data sources such as Centre for Monitoring

Indian Economy (CMIE) and Indiastats.com. The sample of the study comprises of 44

Indian banks, out of which 21 banks are the public sector banks, 18 are the private banks

and five are the non-working banks. The type and the number of banks are selected based

on the availability of data and the consequences of time limitations.

This study applied Altman Z-score, Springate, Grover and Zmijewski bankruptcy model on

the above banks. The reason for the selection of these models amongst various models

available for evaluating the financial performance of banks, as a literature survey shows

that the majority of international failure prediction studies employed these models. Further,

the reason for the selection of more than one model is because a single model may not give


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a perfect result and might lead to an inaccurate estimation, which may lead to improper

policy recommendations.

The data was taken for thirteen years period from the year 2004-5 to 2016-17. The data

period consists of two phases the pre-implementation and post-implementation phase of

Basel II and Basel III norms. This is the period where the Basel committee put norms on

the banks in the form of minimum capital requirement, the supervisory review process,

market discipline, capital buffers, requirements of the leverage ratio, liquidity ratio and Net

Stable Funding Ratio (NSFR) to monitor their NPA’s and assesses their credit risk. The

effect of all these norms is reflected in the financial statements of the banks.

After the application of these models, the model accuracy rate was found, using Type I and

Type II errors. Based on these results, the study further extended to recalibrate these

models. The sample used for the recalibration is 44 banks (public and private and non-

working banks) and the model was recalibrated by changing coefficients of the original

model using current data. The data period used to find the new coefficients of the

recalibrated model is from 2005 to 2010. The recalibrated model consists of the same

variable as the original model, but the coefficients differ. The multiple linear regression is

used to find the coefficients of the recalibrated model. In total, 255 observations were used

to find the coefficient of the recalibrated model; however these observations were reduced

in every model after removing their individual model’s outliers.

In order to develop a recalibrated model, all the assumptions required for the regression

were checked. These assumptions are correlation coefficients, independence of errors,

multicollinearity, homoscedasticity, normality of data, normality of residuals, presence of

outliers and criteria for measuring the goodness of fit of the multiple regression models

such as R2, adjusted R2 and the ANOVA value.

The values of coefficients were used to decide whether the relation is positive, negative,

strong or weak. Durbin Watson Statistics was used to check the weather data is subject to

Independence of observation. Multicollinearity is verified based on Variable Impact Factor

(VIF) and the tolerance level. The outliers were identified as unusual cases and suitably, the


123

same was removed. Homoscedasticity was checked by observing the scatter diagram and

the assumption of normality was checked with the help of a histogram and PP plot.

The recalibrated model was applied to 41 public, private banks and non-working banks

covering a period of 2011-2017. Three non-working banks were not taken in the sample

during the application of the recalibrated model as this model was applied from 2011 -

2017 and these three banks merged before 2011, hence the data was not available from

2011 - 2017. The model accuracy rate of the recalibrated model was also checked by

seeking Type I and Type II error. According to Lin (2015), Type I error means predicting a

Non-Working Banks (NWB) in the safe position and Type II error means predicting a

working banks in the distress position. Type I error is calculated by taking ‘wrongly

predicted score of NWB’ divided by ‘total number of banks in sample,’ while Type II error

is calculated by ‘wrongly predicted score of safe banks’ divided by ‘total number of banks

in sample.’ The accuracy rate of the original and the recalibrated model shows the

difference, hence Independent sample T-test was conducted to test the significant difference

in the mean of the original and recalibrated model.

The difference in the original and the recalibrated model triggered the attention of the

researcher to perform the robust test. The robust test was conducted, taking into account

profitability and asset quality as a variable. Based on the comparative results of the original,

recalibrated, and robust test, the final four models were selected out of eight models. Final

ranks to individual banks were given based on results four models such as recalibrated

Altman Z-score model, original Springate model, original Zmijewski model and

recalibrated Grover model. The individual ranks given to the banks, assisted in the

judgment of the accuracy of the models.

6.4 Results and discussion

The results and discussion are divided into five parts. Part I consist of application and the

recalibration of the Altman model, results of independent sample t-test and results of the

robust test. Part II comprises of application and recalibration of Springate model, results of

independent sample t-test and results of the robust test. Part III comprises of application


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and recalibration Zmijewski model and results of independent sample t-test. Part IV

consists of comprises of application and recalibration Grover model and results of

independent sample t-test. Part V consists of a ranking of banks as per bankruptcy models

and comparison of bankruptcy scores between public and private banks.

6.4.1 An Application of the Altman - 1993 Model (I)

This part applies the original Altman Z- score model, to find whether the high predictive

ability that, Altman’s claimed is still valid in the current business environment. This model

was applied to 44 public, private and non-working banks.

Table 6.2: Results of the tested Altman Model on 21 public 18 private and 5 NWB

S.

N.

Public Sector Banks

(Working Banks)

Score Result S.

N

Private Banks

(Working Banks)

Score Result

1 Allahabad Bank 1.15 Grey 1 Axis Bank 1.57 Grey

2 Andhra Bank 1.34 Grey 2 Catholic Syrian Bank 1.62 Grey

3 Bank of Baroda 1.55 Grey 3 City Union Bank 1.46 Grey

4 Bank of India 1.28 Grey 4 DCB Bank 1.18 Grey

5 Bank of Maharashtra 1.12 Grey 5 Dhanalaxmi Bank 1.44 Grey

6 Canara Bank 1.35 Grey 6 Federal Bank 1.58 Grey

7 Central Bank Of India 1.11 Grey 7 H D F C Bank 1.40 Grey

8 Corporation Bank 1.22 Grey 8 ICICI Bank 1.55 Grey

9 Dena Bank 1.39 Grey 9 Indusind Bank 1.46 Grey

10 I D B I Bank Ltd. 1.07 Distress 10 Jammu & Kashmir Bank 1.49 Grey

11 Indian Bank 1.19 Grey 11 Karnataka Bank 1.39 Grey

12 Indian Overseas Bank 1.27 Grey 12 Karur Vysya Bank 1.28 Grey

13 Oriental Bank of

Commerce

1.32 Grey 13 Kotak Mahindra Bank 1.46 Grey

14 Punjab & Sind Bank 1.10 Safe 14 Lakshmi Vilas Bank 1.31 Grey

15 Punjab National Bank 1.31 Grey 15 Nainital Bank 2.65 Safe

16 State Bank of India 1.22 Grey 16 R B L Bank 1.74 Grey

17 Syndicate Bank 1.07 Distress 17 South Indian Bank 1.27 Grey

18 Uco Bank 1.17 Grey 18 Yes Bank 1.06 Distress

19 Union Bank Of India 1.04 Distress Non-Working bank (NWB)

20 United Bank Of India 1.18 Grey 1 Bank of Punjab (NWB) 1.42 Grey


125

Source: Calculated from secondary data of Annexure Table A.40 to A.42

An observation to Table 6.2 shows that, majority of public sector banks come in a grey area

and three banks are under the distress area. Similarly, in the case of private banks, majority

of them are in a grey area and one bank lies in a distress position. In the case of NWB, one

bank is in the distressed zone and rest four banks are under the grey area. These results

reflect certain working banks in the distress position and the majority of NWB in the safe

zone. These results trigger the researcher a doubt on the predictive ability of the model and

hence identify the model accuracy rate.

Table 6.3: Altman Model Accuracy rate

Altman Model Error Rate Model Accuracy Rate

NWB Type I - 2% 89%

Working Banks Type II – 9%

Source: Authors calculations based on Table 6.2

Note: Type I Error- Prediction of NWB which will not go bankrupt, Type II Error- Prediction of

working banks to be bankrupt

In the present study, the total error of Altman's model is 11% comprising of type I error –

2% and type II error-9%. This shows the 89% accuracy rate of Altman’s model. Since the

accuracy rate of Altman’s model applied to the banks is low, compared to the original

model, therefore the researcher expresses its view for recalibration of this model. A study

by Timmerman (2014) says that, when an old original model is applied to a more recent

sample, the predictive power of the model is very low and the bankruptcy is over predicted.

Another opponent to the Altman model is Hilliest's (2004), who in its study, found model is

deficient and failed to include a measure of asset volatility. A study by Moyer (1977) found

21 Vijaya Bank 1.25 Grey 2 Bank of Rajasthan

(NWB)

1.52 Grey

3 Bharat Overseas Bank

(NWB)

1.39 Grey

4 Centurion Bank Of

Punjab (NWB)

1.03 Distress

5 ING Vysya Bank

(NWB)

1.31 Grey


126

the model accuracy rate of 75%. According to Cantemir (2014), the Altman model has a

series of shortcomings that cripples their prediction efficiency. A study by Grice and Dugan

(2003) stated that ratios used in the equation were not selected on a theoretical basis, rather

selected based on their performance in the prior studies.

6.4.1.1 Recalibration of Altman Model

The low accuracy rate and the criticism done in the past reviews influence the researcher

for model recalibration. The model is recalibrated by changing coefficients of the original

model using current data and multiple regression technique. The recalibrated model

consists of the same variable as the original model, but the coefficients differ. The data

from 2005-2010 of 44 banks is used for finding the new coefficients of the model. In total,

250 observations were used after removing outliers to find the coefficient of the

recalibrated model. Durbin - Watson statistics value 1.928 states that, recalibrated Altman

model meets the assumption of Independence of error and signifies no autocorrelation.

The correlation matrix is drawn, to check the linear relationship between the dependent and

independent variables. In the present study dependent variable is Z-score and Independent

variables are Working Capital to Total Asset (WC/TA), Retained Earnings to Total Asset

(RE/TA), Earnings before Interest and Tax to Total Asset (EBIT/TA) and Market Value of

Equity to Total liability (MVE/TL).

Table 6.4 Correlation Matrix of Recalibrated Altman Model

Variables WC/TA RE/TA EBIT/TA MVE/TA Z -score

WC/TA 1 0.099 .065 -.305 0.698

0.104 .297 .000* 0.00*

RE/TA 0.099 1 -.090 -0.04 0.092

.100 .154 0.946 0.143

EBIT/TA .065 -0.090 1 0.016 0.249

.297 .154 0.799 0.00*

MVE/TA -.305 -0.04 0.016 1 0.012

.000* 0.946 0.799 0.842

Z-Score 0.598 0.092 0.249 0.012 1

0.00* 0.143 0.00* 0.842

Source: Authors Calculations

Note: * Correlation is significant at the 0.01 level (2-tailed).

There is a linear relationship between the dependent and independent variables in the

recalibrated Altman Model, as per table 6.4. The relationship between dependent and


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independent variables is significant at a 1% level for the variable Z-score and WC/TA, Z-

score and EBIT/TA. The relationship among the Independent variable of the banks shows

the correlation coefficient value less than 0.70. This shows that there is no problem of

multicollinearity.

The third assumption of testing the outliers was checked with the help of casewise

diagnostics. Observations 118,120,124,213,221 are identified as unusual cases. Suitably

these outliers were removed. The homoscedasticity test shows residuals are randomly

scattered and observation to histogram satisfied the normality test. A histogram with a

superimposed normal curve depicts the normality of data, and PP-plot showed the

alignment of plots along with the diagonal line. This implies that there exists an

approximately linear pattern that depicts the consistency of the data with a normal

distribution. There was no problem with multicollinearity as the variance inflation factor

was less than five and the tolerance value was less than 0.2.

Table 6.5: Coefficient Test of Recalibrated Altman Model

Predictor Unstandardised

Coefficient

T-Value Sig.

Constant 2.22 2.219 0.27

WC/TA .754 17.405 .000*

RE/TA .036 .870 .385

EBIT/TA .199 4.829 .000*

MVE/ TL .239 5.553 .000*

Source: Authors Calculations based on Annexure Table A.40 - A.42

Note: Significance level – 1%*, ANNOVA F (4, 251) = 86.97, Sig = 0.00, Model Fitness: R= 0.763,

R2 = 0.582, AdjR2 = 0.575

Multiple linear regression was run to understand the coefficients of the equation for the

recalibrated model. Table 6.5 shows the results of regression analysis on public and private

sector banks data. The Results of regression show that adjusted R2 is 0.57 which means

that 57% of the variation in the dependent variable that is Z-score is due to the explanatory

variables (WC/TA, RE/TA, EBIT/TA and MVE/ TA) and remaining 43% variation is due

to other factors. F value is significant at 1%, hence we can say that the overall model is a

good fit. The value of beta (β) explains the contribution of the independent variable.

WC/TA shows the highest beta value, which means its contribution is more than other


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independent variables in Z-score. The above observations show the model fits the data well.

The recalibrated equation is as follows:

Recalibrated Equation of Altman Model

Z-scores = β+βX1+βX2+βX3+βX4.................................6.1

Z-scores = 2.22 + 0.754X1 + 0.036X2 + 0.199X3 + 0.239X4

Where X1 = Working capital/Total assets, X2 = Retained earnings/Total assets, X3 =

Earnings before interest and taxes/Total assets, X4= Market value equity/ Total liabilities

and Z = overall index.

6.4.1.2 Application of Recalibrated Altman Model

The recalibrated model is applied to 41 banks covering a period of 2011-2017. Three

merged banks were not taken in the sample of the recalibrated model as the recalibrated

model was applied for a period 2011 - 2017 and these three banks merged before 2011,

hence the data was not available between 2011 and 2017. The following table shows the

result of Recalibrated - Altman Model

Table 6.6: Results of Recalibrated Altman Model of 21 Public, 18 Private and 2 NWB

S.

N.

Public Banks

(Working Banks)

Score Result S.N

.

Private Banks (Working

Banks)

Score Result

1 Allahabad Bank 2.34 Grey 1 Axis Bank 2.33 Grey

2 Andhra Bank 2.31 Grey 2 Catholic Syrian Bank 2.30 Grey

3 Bank of Baroda 2.41 Grey 3 City Union Bank 2.34 Grey

4 Bank of India 2.33 Grey 4 DCB Bank 2.28 Grey

5 Bank of Maharashtra 2.30 Grey 5 Dhanalaxmi Bank 2.32 Grey

6 Canara Bank 2.32 Grey 6 Federal Bank 2.35 Grey

7 Central Bank of India 2.30 Grey 7 HDFC Bank 2.37 Grey

8 Corporation Bank 2.30 Grey 8 ICICI Bank 2.36 Grey

9 Dena Bank 2.30 Grey 9 Indusind Bank 2.41 Grey

10 IDBI 2.29 Grey 10 Jammu & Kashmir Bank 2.33 Grey

11 Indian Bank 2.30 Grey 11 Karnataka Bank 2.30 Grey

12 Indian Overseas Bank 2.32 Grey 12 Karur Vysya Bank 2.31 Grey

13 Oriental Bank of

Commerce 2.30 Grey

13 Kotak Mahindra Bank 2.35 Grey


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14 Punjab & Sind Bank 2.29 Grey 14 Lakshmi Vilas Bank Ltd. 2.29 Grey

15 Punjab National Bank 2.32 Grey 15 Nainital Bank 2.47 Grey

16 State Bank Of India 2.32 Grey 16 R B L Bank 2.34 Grey

17 Syndicate Bank 2.28 Grey 17 South Indian Bank 2.32 Grey

18 Uco Bank 2.31 Grey 18 Yes Bank 2.32 Grey

19 Union Bank Of India 2.31 Grey Non-Working Banks (NWB)

20 United Bank Of India 2.32 Grey 1 Bharat Overseas Bank (NWB) 0.98 Distress

21 Vijaya Bank 2.28 Grey 2 ING Vysya Bank (NWB) 1.14 Grey

Source: Authors calculations based on Appendix Table A.52

The tested recalibrated model shows all the working banks in the grey area, indicating the

firm’s likelihood of failure is less; however it also shows 50% of the NWB in the grey area.

These results draws the attention of the researcher on the predictive ability of this model

and hence identify the recalibrated model accuracy rate.

Table 6.7: Recalibrated Altman Model Accuracy Rate

Recalibrated Altman Error Rate Model Accuracy Rate

NWB Type I - 2% 98%

Working Banks Type II – 0%

Source: Authors calculations based on table 6.6

Note: Type I error- Prediction of NWB which will not go bankrupt, Type II error- Prediction of working

banks to be bankrupt

The recalibrated model accuracy rate applied to the present study shows an accuracy rate of

98%, whereas the original model applied to the present study shows an accuracy rate of

89%. This shows a difference in the accuracy rate of both models. This difference is

statistically checked with the help of an independent sample t-test. It is hypothesised for the

test that there are no statistical differences in the bankruptcy scores using the original and

the recalibrated bankruptcy models. The difference in these models may not arise, as both

models are using the same accounting variable to arrive at a score.


130

Table no 6.8 - Result of Independent Sample T-test of Altman and Recalibrated Altman Model

Hypothesis P-value Decision

Mean Z-score of Altman Model = Mean Z-score of

Recalibrated Altman Model

0.000 Not supported

Source: Authors calculations based on Appendix table A.40, A.41, A.42 and A.52

The result of the independent sample t-test shows that there is a significant difference in the

mean of the original Altman and the mean of recalibrated Altman Model. These results fail

to accept the null hypothesis. The difference in the predictive ability between the two

models may be on account of environmental factors, industry conditions, country

conditions, period, etc. Thus, the model does not provide credible results under all

conditions. Further, since there is a significant difference in the Z-score of the original

Altman and recalibrated Altman model, a need felt to prove the accuracy of these models.

The accuracy of the model is checked based on a robust test.

6.4.1.3 Results of the Robust Tests

The robust test was conducted, taking into account profitability and asset quality as a

variable. These variables are measured using a proxy to a variable such as ROA and Net

NPA to Net Advances ratio, respectively. The objective of a robust test is to measure the

financial health of the banks in the current years. Therefore ROA and Net NPA to Net

Advance ratio for the year 2017 are considered. The robust test was calculated using the

data of 39 banks. The robust test was not conducted on non-working banks, as these banks

were merged before 2017 and the data about the same was not available. Based on these

ratios, the bases of discriminations have arrived. The bases of discrimination are shown in

Table 6.9

Table 6.9 Bases of discrimination as per Robust Test

Cases ROA and Net NPA to Net

Advance ratio

Zones

I R-score ≤ 1 Distress zone

II 1-2 A grey area

III 2-3 Safe zone

Source: Authors calculations based on Appendix table A.61


131

The data on the ROA ratio is sorted in ascending order for 39 banks, which ranged from 0-

3. Based on the 0-3 range, three categories are made, such as (Safe, Grey and Distress zone)

to discriminate between banks. The score of ROA 0-3 is divided as 0-1.5 = distress zone,

1.5 - 2.5 = grey zone and 2.5 - 3.5 = safe zone. Higher the ratio reflects better profitability

positions of banks and vice versa.

The data on Net NPA to Net Advance ratio is sorted in descending order for thirty-nine

banks, which ranged from 0-15. Based on the 0-15 range, three categories are made, such

as (safe, grey and distress zone) to discriminate between banks. In this case score of Net

NPA to Net Advances ratio ranging from 0-14 is categorized as 0-5= Safe Zone, 5-10=Grey

Zone, and 10-15 = Distress Zone. Lower the ratio reflects better solvency position of banks

and vice versa.

Table 6.10: Results of tested Robust Test on 39 Indian banks.

S.

N.

Banks R-

Score

Result S.N. Banks R-

Score

Result

1 Allahabad Bank 1.5 Grey 21 Indusind Bank 3 Safe

2 Andhra Bank 2 Grey 22 Jammu & Kashmir Bank 2 Grey

3 Axis Bank 2.5 Safe 23 Karnataka Bank 2.5 Safe

4 Bank Of Baroda 2.5 Safe 24 Karur Vysya Bank 3 Safe

5 Bank Of India 1.5 Grey 25 Kotak Mahindra Bank 3 Safe

6 Bank Of Maharashtra 1 Distress 26 Lakshmi Vilas Bank 2.5 Safe

7 Canara Bank 2 Grey 27 Nainital Bank 1 Distress

8 Catholic Syrian Bank. 2 Grey

28 Oriental Bank of

Commerce 1.5 Grey

9 Central Bank Of India 1 Distress 29 Punjab & Sind Bank 2 Grey

10 City Union Bank 3 Safe 30 Punjab National Bank 2 Grey

11 Corporation Bank 2 Grey 31 R B L Bank 3 Safe

12 D C B Bank 2.5 Safe 32 South Indian Bank 2.5 Safe

13 Dena Bank 1.5 Grey 33 State Bank of India 2.5 Safe

14 Dhanlaxmi Bank. 2.5 Safe 34 Syndicate Bank 2 Grey

15 Federal Bank 2.5 Safe 35 UCO Bank 1.5 Grey

16 H D F C Bank 3 Safe 36 Union Bank Of India 2 Grey

17 I C I C I Bank 2.5 Safe 37 United Bank Of India 2 Grey

18 I D B I Bank 2 Grey 38 Vijaya Bank 2.5 Safe

19 -Indian Bank 2.5 Safe 39 Yes Bank. 3 Safe

20 Indian Overseas Bank 1 Distress

Source: Author's calculations based on appendix table A.6. Note: R-Score = Robust test score


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The result of the robust test shows four working banks under the distress category, stating a

90% accuracy rate. Further, the results of the original applied Altman and Recalibrated

Altman model are reconciled with a robust test by making comparisons amongst them.

Table 6.11: Comparison of Original and Recalibrated Altman Model with the Robust test

S.N

. Ba

nk

s

Alt

ma

n

Mo

del

Res

ult

Rec

ali

bra

te

d A

ltm

an

Mo

del

Res

ult

Dif

fere

nce

Ro

bu

st t

est

sco

re

Res

ult

1 Allahabad Bank 1.15 Grey 2.34 Grey 1.15 1.5 Grey

2 Andhra Bank 1.34 Grey 2.31 Grey 0.97 2 Grey

3 Axis Bank Ltd. 1.57 Grey 2.33 Grey 0.76 2.5 Safe

4 Bank Of Baroda 1.55 Grey 2.41 Grey 0.86 2.5 Safe

5 Bank Of India 1.28 Grey 2.33 Grey 1.05 1.5 Grey

6 Bank Of

Maharashtra 1.12 Grey 2.30 Grey 1.18 1 Distress

7 Canara Bank 1.35 Grey 2.32 Grey 0.97 2 Grey

8 Catholic Syrian

Bank 1.62 Grey 2.30 Grey 0.71 2 Grey

9 Central Bank Of

India 1.11 Grey 2.30 Grey 1.19 1 Distress

10 City Union Bank 1.46 Grey 2.34 Grey 0.88 3 Safe

11 Corporation


12 D C B Bank 1.18 Grey 2.28 Grey 1.1 2.5 Safe

13 Dena Bank 1.39 Grey 2.30 Grey 0.95 1.5 Grey

14 Dhanalaxmi

Bank 1.44 Grey 2.32 Grey 0.88 2.5 Safe

15 Federal Bank 1.58 Grey 2.35 Grey 0.77 2.5 Safe

16 H D F C Bank 1.40 Grey 2.37 Grey 0.97 3 Safe

17 I C I C I Bank 1.55 Grey 2.36 Grey 0.81 2.5 Safe

18 I D B I Bank 1.07 Distress 2.29 Grey 1.24 2 Grey-----1st

case

19 -Indian Bank 1.19 Grey 2.30 Grey 1.11 2.5 Safe

20 Indian Overseas

Bank 1.27 Grey 2.32 Grey 1.05 1 Distress

21 Indusind Bank 1.46 Grey 2.41 Grey 0.89 3 Safe

22 Jammu &

Kashmir Bank 1.49 Grey 2.33 Grey 0.81 2 Grey

23 Karnataka Bank 1.39 Grey 2.30 Grey 0.91 2.5 Safe


133

24 Karur Vysya

Bank 1.28 Grey 2.31 Grey 1.03 3 Safe

25 Kotak Mahindra

Bank 1.46 Grey 2.41 Grey 0.95 3 Safe

26 Lakshmi Vilas


27 Nainital Bank 2.65 Grey 2.47 Grey -0.18 1 Distress

28 Oriental Bank

Of Commerce 1.32 Grey 2.30 Grey 0.98 1.5 Grey

29 Punjab & Sind

Bank 1.10 Distress 2.29 Grey 1.19 2

Grey----II

case

30 Punjab National


31 R B L Bank 1.74 Grey 2.34 Grey 0.6 3 Safe

32 South Indian


33 State Bank Of

India 1.22 Grey 2.32 Grey 1.1 2.5 Safe

34 Syndicate Bank 1.07 Grey 2.28 Grey 1.21 2 Grey

35 Uco Bank 1.17 Grey 2.31 Grey 1.14 1.5 Grey

36 Union Bank Of

India 1.04 Distress 2.31 Grey 1.25 2

Grey----III

case

37 United Bank Of

India 1.18 Grey 2.32 Grey 1.14 2 Grey

38 Vijaya Bank 1.25 Grey 2.28 Grey 1.07 2.5 safe

39 Yes Bank 1.06 Distress 2.32 Grey 1.26 3 Safe

Source: Authors compilations based on table 6.2, 6.6 and 6.9

Table 6.11 shows the status of banks based on the discrimination score of applied original

Altman model, Recalibrated Altman Model and Robust Test. The status of three banks are

matching when compared to recalibrated Altman and the robust test, but are different,

compared to original Altman and recalibrated Altman model. This shows the accuracy of

the recalibrated model compared to the original Altman model.

6.4.2 An Application of the Springate Model-1978 (II)

This part applies the original Springate model, to find whether the high predictive ability

that, the Springate model claimed is still valid in the current business environment and the

service sector. The Springate model has taken sales as a measure to decide management


134

capability to deal with competition, however, in the present study in place of sales, total

income is taken as an indicator. In the present study, the Springate model was applied to 44

public, private and non-working banks.

Table 6.12: Results of the applied Springate model on 21 public 18 private and 5 NWB

S.N. Public Banks (Working

Banks) S-Score Result S.N.


banks)

S-

Score Result

1 Allahabad Bank 2.23 Safe 1 Axis Bank Ltd. 1.67 Safe

2 Andhra Bank 2.20 Safe 2 Catholic Syrian Bank 2.33 Safe

3 Bank Of Baroda 1.64 Safe 3 City Union Bank 2.11 Safe

4 Bank Of India 1.82 Safe 4 DCB Bank 1.34 Safe

5 Bank Of Maharashtra 1.79 Safe 5 Dhanalaxmi Bank 1.99 Safe

6 Canara Bank 2.06 Safe 6 Federal Bank 2.34 Safe

7 Central Bank Of India 1.62 Safe 7 HDFC Bank 0.95 Safe

8 Corporation Bank 1.87 Safe 8 ICICI Bank 1.28 Safe

9 Dena Bank 2.19 Safe 9 Indusind Bank 1.78 Safe

10 IDBI Bank Ltd. 1.81 Safe 10 Jammu & K Bank 2.13 Safe

11 Indian Bank 1.69 Safe 11 Karnataka Bank 2.48 Safe

12 Indian Overseas Bank 2.05 Safe 12 Karur Vysya Bank 2.36 Safe

13 Oriental Bank Of Comm. 2.23 Safe 13 Kotak Mahindra Bank 1.21 Safe

14 Punjab & Sind Bank 2.44 Safe 14 Lakshmi Vilas Bank 2.00 Safe

15 Punjab National Bank 1.76 Safe 15 Nainital Bank 1.79 Safe

16 State Bank Of India 0.94 Safe 16 R B L Bank 1.60 Safe

17 Syndicate Bank 1.78 Safe 17 South Indian Bank 2.20 Safe

18 Uco Bank 1.98 Safe 18 Yes bank 1.23 Safe

19 Union Bank Of India 2.04 Safe Non-Working Banks (NWB)

20 United Bank Of India 1.77 Safe 1 Bank Of Punjab (NWB) 1.42 safe

21 Vijaya Bank 2.30 Safe 2 Bank Of Rajasthan. (NWB) 1.14 safe

3 Bharat Overseas. Bank Ltd.

(NWB)

1.81 safe

4 Centurion Bank Of Punjab

Ltd. (NWB)

0.92 safe

5 ING Vysya Bank (NWB) 1.14 safe

Source: Authors calculations based on Appendix table A.43-A.45

As per the results in table 6.6, shows all the good banks in the grey area indicating the

firm's likelihood of failure is less; however it also shows 50% of the NWB in the grey area.


135

These results draw the attention of the researcher on the predictive ability of this model and

hence identify the recalibrated model accuracy rate.

In table 6.12 the Springate model shows all the working public and private banks in a safe

position, however this model also shows the NWB in the safe position, this drives the

researcher to check the accuracy of this model.

Table 6.13: Model Accuracy Rate of Springate Model

Springate model Error Rate Model Accuracy Rate

NWB Type I -11% 89%

Working Banks Type II -0%

Source: Authors calculations based on the results of Table 6.12

Note: Type I error- Prediction of NWB which will not go bankrupt, Type II error - Prediction of

working banks to be bankrupt

The model’s accuracy rate was calculated by finding out Type I and Type II error in

predicting the bankruptcy of banks. Table 6.13 shows the error rate of 11% and an accuracy

rate of 89%. This model failed to predict the bankruptcy of NWB correctly. As per this

model, all the NWB banks fall in the safe category. Therefore, this model needs revision.

As evidence of this fact, a study by Sajjan (2016) compared the result of investigating two

models Zavgren and Springate. The results indicate the adjusted Springate model was

efficient than another model in the bankruptcy year. This suggests the need for

recalibrations of this model.

6.4.2.1 Recalibration of Springate Model

The criticism has done of the Springate model in past studies and the error present in the

tested model drives the researcher for recalibration of the model. The model is recalibrated

by changing coefficients of the original model using current data and multiple regression

technique. The recalibrated model consists of the same variable as the original model, but

the coefficients differ. The data from 2005-2010 of 44 banks is used for finding the new

coefficients of the model. In total, 239 observations were used after removing suitable

outliers, to find the coefficient of the recalibrated model. Durbin- Watson statistics value

1.84 states that, recalibrated Springate model meets the assumption of Independence of

error and signifies no autocorrelation.


136

The correlation matrix is drawn to check the linear relationship between the dependent and

independent variables. In the present study dependent variable is S-score and Independent

variables are Working Capital to Total Asset (WC/TA), Earnings before Interest and Tax to

Total Asset to Total Asset ratio (EBIT/TA), Earnings before Interest and Tax to Current

Liability (EBIT/CL) and Total Income to Total Asset (TI/TA).

Table 6.14: Correlation Matrix of Recalibrated Springate Model

Variables WC/TA EBIT/TA EBIT/CL TI/TA S-score

WC/TA 1 0.033 0.232 -0.108 0.391

0.302 0.000** 0.048 0.00**

EBIT/TA 0.033 1 0.456 0.715 0.493

0.302 0.00** 0.00** 0.00**

EBIT/CL 0.232 0.456 1 0.146 0.930

0.000** 0.000 0.012* 0.00**

TI/TA -0.108 0.715 0.146 1 0.930

0.048 0.00** 0.012* 0.00**

S-score 0.391 0.493 0.930 0.165 1

0.00** 0.00** 0.00** 0.005**

Source: Authors Calculations. Notes: *Correlation at 5% ** Correlation at 1%

There is a linear relationship between the dependent and independent variables in the

recalibrated Springate Model, as per table 6.14. There exists a strong positive relationship

between S-score and ‘EBIT/CL’. It may be strong because the difference in the values of

EBIT to CL is very less; hence the final value of the ratio is more. This makes a high

correlation between these two variables; however with the other independent variables,

there exists a moderate positive relationship with the dependent variable. As evidence of

this fact, Warastuti (2014), in its study, removed this ratio of EBIT/CL from the model

since it has a problem of multicollinearity. Its study also says that variable EBIT and

working capital have a significant positive impact on the health of the bank, whereas the

other two variables have less impact on the dependent variable. The relationship among the

Independent variable of the banks shows the correlation coefficient value less than 0.70.

This shows that there is no problem of multicollinearity. The relationship between the

independent variables is significant at 5% and 1% level of significance concerning all

variables except for WC/TA to EBIT/TA.


137

This model satisfies the assumption of linearity between the dependent and independent

variables after the transformation of data. Before the transformation of data, a partial

regression plot shows a partial relationship between S-score and WC/TA, TI/TA, EBIT/TA,

and a perfect relationship between S-score and EBIT/CL. The linear relationship between

the dependent and Independent variable was also checked collectively, by checking the

studentized residuals value against unstandardized predicted value. There is a linear

relationship between the independent and dependent variables. This model shows a

satisfactory VIF and tolerance level, signifying an absence of multicollinearity. The outliers

present in the model were removed. The assumption of Homoscedasticity states that the

residuals are equal for all the values of the predicted dependent variable. The residuals in

the recalibrated model appear randomly scattered, on this basis, it would appear that the

assumption of Homoscedasticity has been met. The assumption of Normality is checked

with the help of a Histogram and PP-plot. In this case, a histogram with a superimposed

normal curve depicts the normality of data, and PP-plot shows the alignment of plots along

with the diagonal line, which also signals the normality of data. This implies that there

exists an approximately linear pattern that depicts the consistency of the data with a normal

distribution.

Table 6.15: The Coefficient test of Recalibrated Springate Model

Source: Authors calculations based on the annexure Table A.43, A.44 and A.45

Note: Durbin Watson Test: 1.847, Homoscedasticity Test: residuals are randomly scattered, ANOVA value-

F (4, 239) =597.38, Sig = 0.00 Model Fitness: R= 0.931 R2 = 0.891, AdjR2 = 0.872


recalibrated model. Table 6.15 shows the results of regression analysis on public and

private sector bank's data. The Results of regression show that adjusted R2 is 0.87 which

S.N. Predictor Unstandardised

Coefficient

T-value Sig

Constant .093 1.298 .195

1 WC/TA 1.371 9.942 .000

2 EBIT/TA 7.124 5.015 .000

3 EBIT/CL .585 34.444 .000

4 TI/TA .999 1.686 .093


138

means that 87% of the variation in the dependent variable i.e., S-score is due to the

explanatory variables (WC/TA, EBIT/TA, EBIT/CL and TI/TA) and remaining 13%

variation is due to other factors. F value is significant at 1% hence, we can say that the

overall Model is a good fit. The value of beta (β) explains the contribution of the

independent variable. EBIT/TA shows the highest beta value, which means its contribution

is more than other independent variables in S-score. The above observations show the

model fits the data well. The recalibrated equation is as follows:

Recalibrated Equation of Springate Model

S-scores = β +β1X1+β2X2+β3X3+β4X4.........................(6.2)

S-scores = 0.093 + 1.37X1 + 7.124X2 + 0.585X3 - 1.988X4

Where X1 = Working capital/Total Assets, X2 = Earnings before Interest and Tax/Total

Assets, X3 = Earnings before interest and taxes/Current Liability, X4 = Total Income/Total


6.4.2.2 Application of Recalibrated Springate Model

The recalibrated model is applied to 41 banks covering a period of 2011-2017. Three

merged banks were not taken in the sample of the recalibrated model as the recalibrated

model was applied for a period 2011 -2017, and these three banks merged before 2011,

hence the data was not available between 2011 and 2017. The following table shows the

result of Recalibrated - Springate Model

Table 6.16: Results of Applied Recalibrated Model on 21 Public, 18 Private and 2 NWB S.

N.

Public Banks

(Working Banks)

S-score Result S.

N.

Private Banks

(Working Banks)

S-

score

Result


2 Andhra Bank 3.12 Safe 2 Catholic Syrian Bank

Ltd. 3.08

Safe

3 Bank Of Baroda 2.36 Safe 3 City Union Bank Ltd. 3.14 Safe

4 Bank Of India 2.57 Safe 4 DCB Bank Ltd. 0.77 Distress

5 Bank Of Maharashtra 2.64 Safe 5 Dhanalaxmi Bank Ltd. 2.73 Safe

6 Canara Bank 2.83 Safe 6 Federal Bank Ltd. 3.12 Safe

7 Central Bank Of India 2.49 Safe 7 H D F C Bank Ltd. 5.01 Safe

8 Corporation Bank 3.17 safe 8 ICICI Bank Ltd. 2.65 Safe

9 Dena Bank 3.10 Safe 9 Indusind Bank Ltd. 2.95 Safe

10 I D B I Bank Ltd. 2.84 Safe

10 Jammu & Kashmir

Bank Ltd. 2.94

Safe


139

11 -Indian Bank 2.77 Safe 11 Karnataka Bank Ltd. 3.18 Safe

12 Indian Overseas Bank 2.88 Safe 12 Karur Vysya Bank Ltd. 3.39 Safe

13 Oriental Bank Of

Commerce 3.21

Safe 13

Kotak Mahindra Bank

Ltd. 0.80

Distress

14 Punjab & Sind Bank 3.66 Safe

14 Lakshmi Vilas Bank

Ltd. 3.11

Safe

15 Punjab National Bank 2.82 Safe 15 Nainital Bank Ltd. 2.47 Safe

16 State Bank Of India 2.15 Safe 16 R B L Bank Ltd. 2.55 Safe

17 Syndicate Bank 2.58 Safe 17 South Indian Bank Ltd. 3.06 Safe

18 Uco Bank 2.42 Safe 18 Yes Bank Ltd. 0.45 Distress

19 Union Bank Of India 2.99 Safe Non - Working Banks (NWB)

20 United Bank Of India 2.49 Safe 1 Bharat Overseas. Bank

Ltd. (NWB)

1.92 safe

21 Vijaya Bank 3.21 Safe

2 ING Vysya Bank Ltd.

(NWB)

1.34 safe

Source: Authors calculations based on the appendix table A.53

The tested recalibrated model shows three working banks in the financial distress position

and all NWB banks in a safe position. These results drive the researcher to check the

recalibrated model's accuracy rate.

Table 6.17: Model Accuracy Rate of Recalibrated Springate Model

Recalibrated Springate Model Error Rate Model Accuracy Rate

NWB Type I - 5% 88%

Working Banks Type II - 7%

Source: Authors calculations based on table 5.19 Note: Type I error- Prediction of NWB which will not go bankrupt, Type II error - Prediction of working banks to be

bankrupt


predicting the bankruptcy of banks. Table 6.17 shows the total error rate of 12%. The

recalibrated Springate model has an accuracy rate of 88%, which is low compared to the

accuracy rate of the original Springate model. The recalibrated Springate model shows

three private banks in the distress position however the same banks are safe in the original

Springate model. Also, the scores calculated in the recalibrated model are higher than that

of the original model. This is due to the higher coefficients of the recalibrated model. This

shows a difference in the recalibrated and the original model. Thus the results of the tested

Springate model and the tested recalibrated Springate model differ concerning 5% of banks

in a sample. To prove this difference, a statistical method such as an Independent sample t-

test was conducted. It is hypothesised for the test that there are no statistical differences in

the bankruptcy scores using the original and the recalibrated bankruptcy models. The


140

difference in these models may not arise, as both models are using the same accounting

variable to arrive at a score. The result of the T-test is as follows:

Table no 6.18 - Result of Independent Sample T-test of Springate and Recalibrated Springate Model


Mean S - score of Springate Model = Mean S - score of Recalibrated

Springate Model

0.000 Not supported

Source: Authors calculations based on appendix table A.43, A.44, A.45 and A.53

The result of the independent sample t-test shows a significant difference in the bankruptcy

scores of the original Springate and the recalibrated Springate model. These results fail to

accept the null hypothesis. The difference in the predictive ability between the two models

may be on account of environmental factors, industry conditions, country conditions,

period, etc. Thus, the model does not provide credible results under all conditions. Further,

since there is a significant difference in the S-score of the original Springate and

recalibrated Springate model, a need felt to prove the predictive ability of these models.

The predictive ability of the model is checked based on a robust test. The result and the

bases of discrimination for a robust test are shown in table 6.9 and 6.10, respectively.

6.4.2.3 Comparison of original and Recalibrated Springate Model with the Robust test

The results of the original Springate recalibrated Springate model is compared with the

robust test to judge the accuracy of the model

Table 6.19: Comparison of original and Recalibrated Springate Model with the Robust test S

. N.

Ba

nk

s

Orig

ina

l

Mo

del

Decisio

n

Reca

libra

te

d M

od

el

Decisio

n

Differen

ce

Ro

bu

st test

Decisio

n

1 Allahabad Bank 2.23 Safe 3.06 Safe 1.15 1.5 Safe

2 Andhra Bank 2.20 Safe 3.12 Safe 0.97 2 Safe

3 Axis Bank 1.67 Safe 2.76 Safe 0.76 2.5 Safe

4 Bank Of Baroda 1.64 Safe 2.36 Safe 0.86 2.5 Safe

5 Bank Of India 1.82 Safe 2.57 Safe 1.05 1.5 Safe

6 Bank Of Maharashtra 1.79 Safe 2.64 Safe 1.18 1 Safe

7 Canara Bank 2.06 Safe 2.83 Safe 0.97 2 Safe


141

8 Catholic Syrian Bank 2.33 Safe 3.08 Safe 0.71 2 Safe

9 Central Bank Of India 1.62 Safe 2.49 Safe 1.19 1 Safe

10 City Union Bank 2.11 Safe 3.14 Safe 0.88 3 Safe

11 Corporation Bank 1.87 Safe 3.17 Safe 1.08 2 Safe

12 D C B Bank 1.34 Safe 0.77 FD 1.1 2.5 Safe

13 Dena Bank 2.19 Safe 3.10 Safe 0.95 1.5 Safe

14 Dhanlaxmi Bank 1.34 Safe 2.73 Safe 0.88 2.5 Safe

15 Federal Bank 2.34 Safe 3.12 Safe 0.77 2.5 Safe

16 H D F C Bank 0.95 Safe 5.01 Safe 0.97 3 Safe

17 I C I C I Bank 1.28 Safe 2.65 Safe 0.81 2.5 Safe

18 I D B I Bank 1.81 Safe 2.84 Safe 1.24 2 Safe

19 -Indian Bank 1.69 Safe 2.77 Safe 1.11 2.5 Safe

20 Indian Overseas Bank 2.05 Safe 2.88 Safe 1.05 1 Safe

21 Indusind Bank 1.78 Safe 2.95 Safe 0.89 3 Safe

22 Jammu & Kashmir

Bank 2.13

Safe 2.94

Safe 0.81 2

Safe

23 Karnataka Bank 2.48 Safe 3.18 Safe 0.91 2.5 Safe

24 Karur Vysya Bank 2.36 Safe 3.39 Safe 1.03 3 Safe

25 Kotak Mahindra Bank 1.21 Safe 0.80 FD 0.95 3 Safe

26 Lakshmi Vilas Bank 2.00 Safe 3.11 Safe 0.98 2.5 Safe

27 Nainital Bank 1.79 Safe 2.47 Safe -0.18 1 Safe

28 Oriental Bank of

Commerce 2.23

Safe 3.21

Safe 0.98 1.5

Safe

29 Punjab & Sind Bank 2.44 Safe 3.66 Safe 1.19 2 Safe

30 Punjab National Bank 1.76 Safe 2.82 Safe 1.01 2 Safe

31 R B L Bank 1.60 Safe 2.55 Safe 0.6 3 Safe

32 South Indian Bank 2.20 Safe 3.06 Safe 1.05 2.5 Safe

33 State Bank Of India 0.94 Safe 2.15 Safe 1.1 2.5 Safe

34 Syndicate Bank 1.78 Safe 2.58 Safe 1.21 2 Safe

35 Uco Bank 1.98 Safe 2.42 Safe 1.14 1.5 Safe

36 Union Bank of India 2.04 Safe 2.99 Safe 1.25 2 Safe

37 United Bank of India 1.77 Safe 2.49 Safe 1.14 2 Safe

38 Vijaya Bank 2.30 Safe 3.21 Safe 1.07 2.5 Safe

39 Yes Bank 1.23 Safe 0.45 FD 1.26 3 Safe

Source: Authors Compilation of data based on Table 6.9, 6.12 and 6.16


142

Table 6.19 shows the status of banks based on the discrimination score of applied original

Springate model, Recalibrated Springate Model and Robust Test. The results of the three

cases are different when compared to the robust test and recalibrated Springate model but

are matching when compared to the original Springate and the robust test. This shows the

accuracy of the original Springate model compared to the recalibrated Springate model,

evidenced by the fact of the accuracy rate of both models. Overall the results show original

Springate model performs better than the recalibrated Springate model, hence it can be

concluded that original Springate model works better in the country of its origin as it shows

93% accuracy rate in their own country, but it proves a less accurate in some other country

as the economic conditions differ from country to country. Also, the original model shows

less accuracy rate due to its sample bias - as originally it was applied for the manufacturing

firms. Still, in the present study, it is used in the service sector.

6.4.3 Application of Zmijewski Model - 1984 (III)

This part applies the original Zmijewski model, to find whether the high predictive ability

that, Zmijewski model claimed is still valid in the current business environment and service

sector. In the present study, the Zmijewski model was applied to 41 public, private and

non-working banks. Table 6.20 shows the X-score and probability of bankruptcy of the

respective bank.

Table 6.20: Result of the tested Zmijewski model on 21 public, 18 private and 2 NWB

S. N

.

Pu

blic

Ba

nk

s

(Wo

rkin

g

Ba

nk

s)

X-S

core

Pro

ba

bility

Decisio

n

S. N

.

Priv

ate

ba

nk

s

(Wo

rkin

g

ba

nk

s)

X-S

core

Pro

ba

bility

Decisio

n

1 Allahabad Bank -3.09 0.1

1

Safe 1 Axis Bank -4.32 0.1

0

Safe

2 Andhra Bank -3.87 0.0

9

Safe 2 Catholic Syrian

Bank

-0.05 0.3

0

Safe

3 Bank Of Baroda -4.81 0.0

1

Safe 3 City Union Bank -4.43 0.1

5

Safe

4 Bank Of India -2.74 0.0

6

Safe 4 D C B Bank 6.89 1.0

0

Distress

5 Bank Of

Maharashtra

-1.82 0.1

8

Safe 5 Dhanalaxmi Bank -0.59 0.2

6

Safe

6 Canara Bank -3.21 0.0

2

Safe 6 Federal Bank -3.27 0.1

1

Safe

7 Central Bank Of

India

-1.12 0.2

7

Safe 7 H D F C Bank -4.87 0.1

5

Safe


143

Source: Authors calculations based on appendix table A.46, A.47, and A.48

The results of the application of the Zmijewski model show that in the case of public sector

banks, most of the banks fall in the safe area with the exception of one bank in distress zone

whose probability of bankruptcy is 69%. Similarly, in the case of private banks, the

majority of them are in a safe area, and two banks lie in distress position whose probability

of bankruptcy is more than 50%. The probability of bankruptcy is very high for the DCB

bank. In the case of NWB, four banks are in the distressed zone, and only one bank is in the

safe category. The NWB in the distressed zone has a higher probability of bankruptcy.

These results reflect certain working banks in the distress position and NWB in the safe

zone. This draws the attention of the researcher to find the accuracy rate of the model. This

accuracy rate was found out by seeking Type I and Type II error.

8 Corporation Bank -3.03 0.0

9

Safe 8 I C I C I Bank -3.29 0.2

3

Safe

9 Dena Bank -1.8 0.1

6

Safe 9 Indusind Bank 0.25 0.6

1

Distress

1

0

I D B I Bank 1.26 0.6

9

Distress 10 Jammu & Kashmir

Bank

-3.94 0.1

6

Safe

1

1

Indian Bank -1.24 0.2

3

Safe 11 Karnataka Bank -2.48 0.0

1

Safe

1

2

Indian Overseas

Bank

-2.71 0.3

2

Safe 12 Karur Vysya Bank -3.30 0.1

9

Safe

1

3

Oriental Bank of

Commerce

-1.72 0.2

0

Safe 13 Kotak Mahindra

Bank

-6.73 0.3

1

Safe

1

4

Punjab & Sind

Bank

-1.69 0.3

1

Safe 14 Lakshmi Vilas

Bank

-2.01 0.3

0

Safe

1

5

Punjab National

Bank

-2.47 0.1

5

Safe 15 Nainital Bank -4.42 0.1

5

Safe

1

6

State Bank of

India

-1.93 0.2

3

Safe 16 R B L Bank -1.53 0.2

8

Safe

1

7

Syndicate Bank -3.15 0.0

9

Safe 17 South Indian Bank -3.48 0.2

3

Safe

1

8

UCO Bank -1.82 0.1

7

Safe 18 Yes Bank -2.85 0.3

0

Safe

1

9

Union Bank Of

India

-1.95 0.1

8

Safe Non-Working Banks (NWB)

2

0

United Bank Of

India

-2.16 0.3

5

Safe 1 Bank Of Punjab

[NWB] 7.02 1.0

0

Distress

2

1

Vijaya Bank -1.07 0.2

4

Safe 2 Bank Of Rajasthan

[NWB] -0.76 0.2

0

Safe

3 Bharat Overseas

Bank [NWB] 2.07 0.9

8

Distress

4 Centurion Bank Of

Punjab [NWB] 2.45 0.9

9

Distress

5 I N G Vysya Bank

[NWB] 0.02 0.5

1

Distress


144

Table 6.21: Model Accuracy rate of Zmijewski Model

Zmijewski Model Error Rate Model Accuracy Rate

NWB Type I -2% 91%

Working Banks Type II 7%

Source: Authors Calculation based on table 6.20




predicting the bankruptcy of banks. Table 6.21 shows the error rate of 9% and accuracy rate

of 91% of applied on the Zmijewski model. Platt and Platt (2002) argue that although

Zmijewski tried to avoid choice-based sample bias, his empirical test was weak. The author

claims that Zmijewski ran only one regression for each sample size and could not test the

individual estimated coefficients for bias against the population parameter, a more direct

test of bias. Whereas, a study by Platt and Platt (2002), used more standard tests of bias,

comparing the mean estimated coefficient to the population parameter.

Shumway (2001) argues that, the model of Zmijewski (1984) is only a one-variable model.

As per his study, the variable TL/TA is strongly correlated (p = 0.40) with NI/TA.

Shumway (2001) stated that the model of Zmijewski (1984) does not have strong predictive

power for bankruptcy. Grice and Dugan (2003) stated that one of the limitations of the

study of Zmijewski (1984) is that the ratios were not selected on a theoretical basis, but

rather based on their performance in prior studies. He reported an accuracy rate of the

Zmijewski model as 81.3%. These error rates of applied Zmijewski model and the critiques

of the model drive the attention of the researcher for its revision in terms of recalibrations

of this model.

6.4.3.1 Recalibration of Zmijewski Model Development

The model is recalibrated by changing coefficients of the original Zmijewskis model using

current data and multiple regression technique. The recalibrated model consists of the same

variable as the original model, but the coefficients differ. The data from 2005-2010 of 41

banks is used for finding the new coefficients of the model. In total, 245 observations are

used after removing suitable outliers, to find the coefficient of the recalibrated model.


145

To develop a recalibrated Zmijewski model, all the assumptions required to run regression

were checked. Durbin- Watson statistics value 1.21 states that, recalibrated Zmijewski

model meets the assumption of Independence of error and signifies no autocorrelation. The

assumption linear relationship between the dependent and independent variables is tested

with the help of the correlation matrix.

Table 6.22: Correlation Matrix of Zmijewski Model

Variables ROA TD/TA CA/CL X-score

ROA 1 -0.374 -.003 -0.661

0.00 .480 .000**

TD/TA -0.374 1 0.261 0.274

0.00 0.00 0.00

CA/CL -.003 0.261 1 -0.25

.480 0.00 .348

X-Score -0.661 0.274 -0.25 1

.000** 0.00 .348

Source: Authors Calculations Notes:*Correlation at 5%** Correlation at 1%

In the above table, the dependent variable is X-score and the independent variables are

Return on Assets (ROA), Total Liabilities/Total Assets (TL/TA), Current Assets / Current

Liabilities(CA/CL). The relationship between the dependent and independent variables is

verified at 1% and 5% level of significance. Table 6.22 shows the existence of a strong

negative relationship between X-score and ‘ROA’, and it signifies an increase in the ROA

will decrease the X-score. However, it may be strong because the difference in the values

of EBIT to CL is very less; hence the final value of the ratio is more. The relationship of X-

score with the other independent variables is negative and weak. Hence smaller the X-

score better for the bank to avoid bankruptcy. In other words, negative the X-score, lesser

the chance for the bank to go bankrupt. The relationship among the independent variable of

the banks shows the correlation coefficient value less than 0.70. This shows that there is no

problem of multicollinearity.

The value of VIF in the regression output is one, and the value of tolerance is less than 1 for

all the independent variables. This depicts that there is no multicollinearity in the

recalibrated model. The outliers are indicated by the points in the upper-right corner of the

plot. Observations 226,228,229,231,232,234,237,243 and 246 are identified as unusual


146

cases because the absolute value of the standardized residuals is greater than 2. These

outliers were removed. The residuals in the recalibrated model appear randomly scattered;

on this basis, it would appear that the assumption of Homoscedasticity has been met. A

histogram with a superimposed normal curve and PP-plot, along with the diagonal line,

depicts the normality of data. This implies that there exists an approximately linear pattern

that depicts the consistency of the data with a normal distribution.

Table 6.23: Coefficient Test of Zmijewski Model


Coefficient

T-value Sig

Constant -2.534 -5.10 .610

ROA -3.79 -12.370 .000

TD/TA 4.36 .781 .435

CA/CL -.081 -.759 .448

Source: Authors calculations based on annexure A.46, A.47 and A.48

Note: Durbin Watson Test-1.21, Homoscedasticity Test: residuals are randomly scattered, ANOVA

value-F (3,242) =6322, Sig = 0.000, Model Fitness: R= 0.663, R2 = 0.439 AdjR2 = 0.432



private sector banks data. The Results of regression show that adjusted R2 is 0.43, which

means that 47% of the variation in the dependent variable i.e., X-score is due to the

explanatory variables (ROA, TD/TA and CA/CL) and remaining 57% variation is due to

other factors. F-value is significant at 1% hence, we can say that the overall Model is good

fit. The value of beta (β) explains the contribution of the independent variable. ‘TD/TA’

shows the highest beta value, which means its contribution is more than other independent

variables in X-score. The above observations show the model fits the data well. The

recalibrated equation is as follows:

Recalibrated Equation of Zmijewski Model

X-scores =β +β1X1+β2X2-β3X3............................. (6.3)

X-scores = -2.534 - 3.79X1 + 4.246X2 - 0.081X3


to Current Liabilities and X-Score = Overall index.


147

6.4.3.2 Application of Recalibrated Zmijewski Model

The recalibrated Zmijewski model was applied on 41public and private banks covering a

period of 2011-2017. The following table shows the result of the recalibrated Zmijewskis

Model.

Table 6.24: Result of the tested Recalibrated Zmijewski model of 21 Public 18 Private and 2NWB

S.N

.

Pu

blic

Ba

nk

s

(wo

rkin

g

ba

nk

s)

Av

g

Pro

ba

bilit

y

Resu

lt

S.N

.

Priv

ate

ba

nk

s

(Wo

rkin

g

ba

ks)

Av

g

Pro

ba

bilit

y

Resu

lt

1 Allahabad Bank -1.37 0.08 Safe 1 Axis Bank -2.94 0.0

1

Safe

2 Andhra Bank -2.22 0.02 Safe 2 Catholic Syrian

Bank

-1.28 0.0

9

Safe

3 Bank Of Baroda -2.67 0.01 Safe 3 City Union Bank -2.24 0.0

2

Safe

4 Bank Of India -1.79 0.04 Safe 4 D C B Bank -0.08 0.4

6

Safe

5 Bank Of

Maharashtra

-1.32 0.08 Safe 5 Dhanalaxmi Bank 1.79 0.9

6

Distres

s

6 Canara Bank -1.99 0.03 Safe 6 Federal Bank -2.51 0.0

1

Safe

7 Central Bank Of

India

-0.72 0.21 Safe 7 H D F C Bank -2.65 0.0

1

Safe

8 Corporation Bank -1.51 0.06 Safe 8 I C I C I Bank -2.47 0.0

1

Safe

9 Dena Bank -1.84 0.03 Safe 9 Indusind Bank 2.20 0.9

1

Distres

s

10 IDBI Bank Ltd. 1.09 0.87 Distr

ess

10 Jammu &

Kashmir Bank

-4.53 0.0

0

Safe

11 Indian Bank -1.30 0.09 Safe 11 Karnataka Bank -1.01 0.1

4

Safe

12 Indian Overseas

Bank

-1.40 0.07 Safe 12 Karur Vysya

Bank

-1.96 0.0

3

Safe

13 Oriental Bank Of

Commerce

-0.93 0.16 Safe 13 Kotak Mahindra

Bank

-2.73 0.0

1

Safe

14 Punjab & Sind

Bank

-1.49 0.06 Safe 14 Lakshmi Vilas

Bank

-2.23 0.0

2

Safe

15 Punjab National

Bank

-1.94 0.03 Safe 15 Nainital Bank -5.31 0.0

0

Safe

16 State Bank Of

India

-0.79 0.19 Safe 16 R B L Bank -8.83 0.0

0

Safe

17 Syndicate Bank -5.35 0.01 Safe 17 South Indian

Bank

-1.43 0.0

7

Safe

18 Uco Bank -2.36 0.01 Safe 18 Yes Bank -1.96 0.0

3

Safe

19 Union Bank Of

India

-1.04 0.13 Safe Non-Working Banks

20 United Bank Of

India

-1.05 0.13 Safe 1 Bank Of

Rajasthan [NWB]

-1.02 0.1

4

Safe


148

Source: Authors calculations based on appendix table A.54

In table 6.24 recalibrated Zmijewski model shows three banks in the distress condition,

whose probability of bankruptcy is more than 50%. However, this model also shows the

non - working banks in the safe position, and this attracts the attention of the researcher to

check the accuracy of this model. This accuracy is checked by detecting the type I and type

II errors in the model.

Table 6.25: Model Accuracy Rate of Recalibrated Zmijewski Model

Zmijewski Model Error rate Model accuracy rate

NWB Type I – 4% 89%

Working banks Type II -7%


Note: Type I error- Prediction of NWB which will not go bankrupt, Type II error- Prediction of working banks to be

bankrupt



recalibrated Zmijewski model has an accuracy rate of 89%, which is low compared to the

accuracy rate of the original Zmijewski model. This shows a difference in the recalibrated

and the original model. To prove this difference, a statistical method such as an

Independent sample t-test was conducted. It is hypothesised for the test that there is no

statistical differences in the bankruptcy scores using the original and the recalibrated

bankruptcy models. The difference in these models may not arise, as both models are using

the same accounting variable to arrive at a score. The result of the T-test is as follows:

Table no 6.26 - Result of Independent Sample T-test of Zmijewski and Recalibrated Zmijewski Model


Mean Z-score of Zmijewski Model = Mean Z-score of Recalibrated

Zmijewski Model

0.067 Supported


The results of the Independent sample T-test shows that there is no significant difference in

the mean of original Zmijewski and the mean of recalibrated Zmijewski model. These

21 Vijaya Bank -0.61 0.25 Safe 2 ING Vysya bank

[NWB]

1.83 0.9

7

Distres

s


149

results help to accept the null hypothesis. Finally, to sum up, this study shows that the

original Zmijewski model performs slightly better than the recalibrated Zmijewski model as

the accuracy rate of the original model is higher by 2% than the recalibrated model. This

result is supported by previous studies. Research by Husein and Pambekti (2014) states

that the Zmijewski model is the most appropriate model used to predict the financial

difficulties at the corporation registered in Securities List Sharia. Additionally, numerous

other studies, such as Sinarti (2015), Hastini (2015) and Purnajaya (2014) states that there

is a significant difference in the models Zmijewski against the Altman model. Although

Grice and Dugan (2001) and Shumway (2001) criticizes the Zmijewski model, still prefer a

probit model rather than the MDA approach of Altman.

6.4.4 An Application of Grover Model -2003 (IV)

This part applies the original Grover model, to find whether the high predictive ability that,

the model claims is still valid in the current business environment and service sector. In the

present study, the model is applied to 44 public, private and Non-Working Banks (NWB).

Table 6.27: Results of the Tested Grover model on 21 Public, 18 Private and 5 NWB

S.N

.

Public Banks (Working

Banks)

Scor

e

Result S.N

.

Private banks

(Working banks)

Score Result


2 Andhra Bank

0.44

Safe 2 Catholic Syrian Bank

Ltd. 0.57

Safe


4 Bank Of India 0.43 Safe 4 DCB Bank Ltd. 0.36 Safe



7 Central Bank Of India 0.41 Safe 7 HDFC Bank Ltd. 0.33 Safe

8 Corporation Bank 0.41 Safe 8 ICICI Bank Ltd. 0.39 Safe


10 IDBI Bank Ltd.

0.41

Safe 10 Jammu & Kashmir

Bank Ltd. 0.47

Safe

11 Indian Bank 0.40 Safe 11 Karnataka Bank Ltd. 0.46 Safe

12 Indian Overseas Bank

0.45

Safe 12 Karur Vysya Bank

Ltd. 0.41

Safe

13 Oriental Bank Of Commerce

0.44

Safe 13 Kotak Mahindra Bank

Ltd. 0.28

Safe

14 Punjab & Sind Bank

0.40

Safe 14 Lakshmi Vilas Bank

Ltd. 0.44

Safe



17 Syndicate Bank

0.39

Safe 17 South Indian Bank

Ltd. 0.44

Safe


150

Source: Authors calculations based on annexure table A.49, A.50, and A.51

Table 6.27 shows all the public and private banks in the safe position as per the Grover

model, whereby all the banks have less chance of becoming bankrupt. However, this model

also shows the NWB banks in a safe position; this attracts the attention of the researcher to

check the accuracy of this model. This accuracy is checked by detecting the Type I and

Type II errors in the model.

Table 6.28: Type I and Type II error of Grover Model

Grover Model Error Rate Model Accuracy Rate

NWB Type I -11% 89%

Working Banks Type II 0%


Note: Type I error- Prediction of NWB, which will not go bankrupt, Type II error- Prediction of working

banks to be bankrupt.

Table 6.28 shows that Grover models have an accuracy rate of 89%. This model has failed

to predict the bankruptcy of NWB correctly. As per this model, all the NWB fall in the safe

category, hence this model also needs a revision through its recalibrations. As evidence to

this fact, Qamruzzaman (2016), made a comparative study between different models, its

study concluded that G-score provides conflicting predictions. The data period used by the

Grover model is from the year 1982-1996. This data period may not hold the present

situations. Syamni (2018) also found contradictory views in its study. As per its study, a

model, when applied to coal mining companies, shows companies in healthy condition,

however, the same as per Ohlson and Altman model shows in bankruptcy condition.

Aminian (2016) and Primasari (2017), questioned on the predictive ability on the of Grover

model in its study. Thus the above criticism and the model accuracy rate influence a

researcher to recalibrate this model.

18 Uco Bank 0.43 Safe 18 Yes Bank Ltd. 0.33 Safe

19 Union Bank Of India

0.39

Safe Non- Working

Banks (NWB)

20 United Bank Of India

0.45

Safe 1 Bank Of Punjab Ltd.

[NWB] 0.53

safe

21 Vijaya Bank

0.48

Safe 2 Bank Of Rajasthan

Ltd. [NWB] 0.51

safe

3 Bharat Overseas Bank

Ltd.[NWB] 0.33

safe

4 Centurion Bank Of

Punjab Ltd.[NWB] 0.43

safe

5 ING Vysya Bank Ltd.

[NWB] 0.43

safe


151

6.4.4.1 Recalibration of Grover model

The model is recalibrated by changing coefficients of the original Grover model using

current data and multiple regression techniques. The recalibrated model consists of the

same variable as the original model, but the coefficients differ. The data from 2005-2010 of

44 banks is used for finding the new coefficients of the model. In total, 250 observations

are used after removing suitable outliers to find the coefficient of the recalibrated model.

To develop a recalibrated Zmijewski model, all the assumptions required to run regression

were checked. Durbin- Watson statistics value 2.07 states that, recalibrated Zmijewski

model meets the assumption of Independence of error and free from autocorrelation as the

value 2.07 fits in the criteria of 1 to 4. The assumption linear relationship between the

dependent and independent variables is tested with the help of the correlation matrix.

Table 6.29: Correlation Matrix of Grover model

Variables WC/TA EBIT/TA ROA G-Score

WC/TA 1 -.043 -.035 .693

0.250 0.291 0.00**

EBIT/TA -.043 1 -.035 .218

0.250 0.00** .000**

ROA -.035 -.035 1 -.036

0.291 0.00** .284

G-score .693 .218 -.036 1

0.00** .000** .284

Source Authors calculations Notes*Correlation at 5%** Correlation at 1%

In the above table, the dependent variable is G-score and the independent variables are

Working Capital to Total Asset Ratio (WC/TA), Earnings before Interest and Tax to Total

Asset to Total Asset ratio (EBIT/TA), and Return on Asset (ROA). The relationship

between dependent and independent variables is checked at 1% and 5% level of

significance. Table 6.29 shows the existence of a strong and positive relation between G-score

and 'WC/TA.' Also, there exists a positive relationship between G-score and ‘EBIT/TA.'

This strong relationship depicts that, profitability and liquidity variable has more impact on

G-score. However, the relationship between G-score and ‘ROA’ is not significant. It means

that the impact of this variable on in contribution of the G-score is not significant. This may


152

be on account of sufficient contribution to G-score made by the other profitability variable,

such as EBIT/TA. The relationship between the independent variables WC/TA to EBIT/TA

and ROA to WC/TA is negative amongst each other. This negative relationship occurs on

account of the nature of the variable, as WC/TA is a liquidity variable, whereas ROA and

EBIT/TA is a profitability variable. Both these variables move in the opposite direction.

Firms achieve Profitability at the cost of liquidity; hence the relationship between these

variables is inverse in nature. The relationship among the independent variable of the banks

shows the correlation coefficient value less than 0.70. This shows that there is no problem

of multicollinearity.

The value of VIF in the regression output is one and the value of tolerance is less than 1 for

all the independent variables. This depicts that there is no multicollinearity in the

development of the recalibrated model. A linear relationship between dependent and

independent variables was also checked collectively, by checking the studentized value

residuals against unstandardized predicted value. There is a linear relationship between the

independent and dependent variables. The residuals in the recalibrated model appear

randomly scattered; on this basis it would appear that the assumption of Homoscedasticity

has been met. A histogram with a superimposed normal curve and PP-plot, along with the

diagonal line, depicts the normality of data. This implies that there exists an approximately

linear pattern that depicts the consistency of the data with a normal distribution.

Table 6.30: Results of Coefficient Test of Grover Model


Coefficient

T-value Sig

Constant .074 14.26 .000

WC/TA 1.654 142.26 .000

EBIT/TA 3.091 39.89 .000

ROA -.014 -11.63 .000

Source: Authors Calculations based on Annexure table A.49, A.50 and A.51

Notes: Significance level 1*. Durbin Watson Test-2.07, Homoscedasticity Test: residuals are randomly

scattered, ANOVA value-F (3, 248) = 7165.71, Sig = 0.000, Model Fitness: R= 0.89, R2 = 0.88, AdjR2 = 0.87


153



private sector bank's data. The Results of regression show that adjusted R2 is 0.87, which

means that 87% of the variation in the dependent variable i.e., G-score is due to the

explanatory variables (WC/TA, EBIT/TA and ROA) and remaining 13% variation is due to

other factors. F-value is significant at 1% hence, we can say that the overall Model is a

good fit. The value of beta (β) explains the contribution of the independent variable.

'EBIT/TA' shows the highest beta value, which means its contribution is more than other

independent variables in G-score. The above observations show the model fits the data

well. The recalibrated equation is as follows:

Recalibrated Equation of Grover Model

G-scores =β +β1X1+β2X2-β3X3 ............................(6.4)

G-scores = 0.074 + 1.65X1 + 3.09X2 - 0.014X3

Where X1 = Working capital/Total assets, X2 = Earnings before Interest and Tax/Total

assets, X3 = Return on Asset (ROA) and G-score = Overall index.

6.4.4.2 Application of Recalibrated- Grover model

The recalibrated model is applied on public, private and Non-working banks to check the

accuracy of the recalibrated model.

Table 6.31: Result of the recalibrated Grover Model of 21 Public, 18 Private and 2 NWB

S.

N.

Public Banks (Working

Banks)

G-

Score

Result S.

N.


Banks)

G -

score

Result


2 Andhra Bank 0.45 Safe 2 Catholic Syrian Bank Ltd. 0.35 Safe


4 Bank Of India 0.46 Safe 4 DCB Bank Ltd. 0.30 Safe



7 Central Bank Of India 0.41 Safe 7 H D F C Bank Ltd. 0.56 Safe

8 Corporation Bank 0.42 Safe 8 ICICI Bank Ltd. 0.39 Safe


10 I D B I Bank Ltd. 0.45 Safe 10 Jammu & Kashmir Bank Ltd. 0.40 Safe

11 Indian Bank 0.40 Safe 11 Karnataka Bank Ltd. 0.32 Safe

12 Indian Overseas Bank 0.47 Safe 12 Karur Vysya Bank Ltd. 0.41 Safe

13 Oriental Bank Of

Commerce 0.44

Safe 13

Kotak Mahindra Bank Ltd. 0.42

Safe


154

14 Punjab & Sind Bank 0.40 Safe 14 Lakshmi Vilas Bank Ltd. 0.41 Safe



17 Syndicate Bank 0.46 Safe 17 South Indian Bank Ltd. 0.45 Safe

18 Uco Bank 0.44 Safe 18 Yes Bank Ltd. 0.36 Safe

19 Union Bank Of India 0.45 Safe NWB

20

United Bank Of India 0.47

Safe 1 Bharat Overseas Bank Ltd.

[NWB] 0.43

safe

21

Vijaya Bank 0.37

Safe 2 Centurion Bank Of Punjab

Ltd. [NWB] 0.43

safe

Source: Authors Calculation based on appendix table A.55

In table 6.31, recalibrated the Grover model shows all the working banks in the safe

condition. However, the same model also shows the non - working banks in a safe position,

and this attracts the attention of the researcher to check the accuracy of this model. This

accuracy is checked by detecting the type I and type II errors in the model.

Table 6.32: Model Accuracy rate of Recalibrated Grover Model

Recalibrated Grover Model Error Rate Model Accuracy Rate

NWB Type I -5% 95%

Working Banks Type II - 0 %

Source: Authors calculation based on table 6.35



The model's accuracy rate was calculated by finding out Type I and Type II error in


recalibrated Grover model has an accuracy rate of 95%, which is high compared to the

accuracy rate of the tested original Grover model. This shows a difference in the

recalibrated and the original model. To prove this difference, a statistical method such as an

Independent sample t-test was conducted. It is hypothesised for the test that there are no

statistical differences in the bankruptcy scores using the original and the recalibrated

bankruptcy models. The difference in these models may not arise, as both models are using

the same accounting variable to arrive at a score. The result of the T-test is as follows:


155

Table 6.33 - Result of Independent Sample T-test of Grover and Recalibrated Grover Model Hypothesis P-value Decision

Mean G-score of Grover Model = Mean G-score of Recalibrated Grover

Model

0.87 Supported


The results of the Independent sample T-test show that there is no significant difference in

the mean of the original Grover and the mean of the recalibrated Grover model. These

results help to accept the null hypothesis. Overall, the results show that the recalibrated

Grover model performs better than the original Grover model due to the higher accuracy

rate compared to the applied model. In the further part of this study, a sample banks were

ranked based on these models and these ranks were compared based on the rank given by

other models. The result of the comparison shows that recalibrated Grover models rank

match with the other models, hence the recalibrated Grover model performs better.

6.4.5 Ranking of Banks as per Bankruptcy Models

The main aim of ranking the banks as per model is to find the accuracy of these models and

understand the order of its bankruptcy score. In this study, four original models and four

recalibrated model is applied to the public and private banks in India. In the present study,

the recalibrated Altman depicted better results compared to the original Altman model;

therefore, the former model is used for comparison. Similarly, the results of the robust test

show that the original Springate model works better than the recalibrated model, hence the

original Springate model is chosen as a base for comparing with other models. Further, the

original Zmijewskis model is used for comparison as it has a higher accuracy rate compared

to a recalibrated model. Lastly, based on the accuracy rate, the recalibrated Grover model is

used for comparison. Finally, four models, such as recalibrated Altman Z-score model,

Original Springate model, and Original Zmijewski model and recalibrated Grover model is

to give ranks to the banks and decide the accuracy of the model.


156

Table 6.34: Table showing Ranks of banks

Pu

bli

c

ba

nk

s

Rec

ali

bra

t

ed

Alt

ma

n

Ori

gin

al

Sp

rin

ga

te

Ori

gin

al

Zm

ijew

ski

Rec

ali

bra

t

ed G

rov

er

Pri

va

te

Ba

nk

s

Rec

ali

bra

t

ed

Alt

ma

n

Ori

gin

al

Sp

rin

ga

te

Ori

gin

al

Zm

ijew

ski

Rec

ali

bra

t

ed G

rov

er

Allhabad bank 2 3 5 2 Axis Bank 10 12 5 5

Andhra Bank

10 5 2 8 Catholic Syrian Bank

Ltd.

15 4 16 16

Bank Of Baroda 1 19 1 1 City Union Bank Ltd. 7 7 3 4

Bank Of India 3 12 7 5 D C B Bank Ltd. 18 14 18 18

Bank Of Maharashtra 14 14 13 12 Dhanalaxmi Bank Ltd. 12 9 15 12

Canara Bank 4 7 3 6 Federal Bank Ltd. 5 3 10 3

Central Bank Of

India

15 20 19 16

H D F C Bank Ltd.

3 18 2 2

Corporation Bank 16 11 16 15 I C I C I Bank Ltd. 4 15 9 11

Dena Bank 13 6 15 14 Indusind Bank Ltd. 14 11 17 13

I D B I Bank Ltd. 19 13 21 20 Jammu & K Bank Ltd. 11 6 6 10

Indian Bank 17 18 18 17 Karnataka Bank Ltd. 16 1 12 17

Indian Overseas

Bank

5 8 8 3 Karur Vysya Bank

Ltd.

6 2 8 8

Oriental Bank Of

Commerce

18 4 16 10 Kotak Mahindra Bank

Ltd.

2 17 1 7

Punjab and Sind

Bank

19 1 17 18 Lakshmi Vilas Bank

Ltd.

17 8 13 14

Punjab National

Bank

6 17 9 13

Nainital Bank Ltd.

1 10 4 1

State Bank Of India 7 21 12 19 R B L Bank Ltd. 8 13 14 14

Syndicate Bank 9 15 4 7 South Indian Bank

Ltd.

9 5

7 6

Uco Bank 12 10 14 11 Yes Bank 13 16 11 15

Union Bank Of India 11 9 11 9

United Bank Of

India

8 16 10 4

Vijaya Bank 20 2 20 21

Source: Author’s calculations based on appendix table A.40 to A.55

Table 6.34 shows the ranks given to the banks as per the scores adopted by the recalibrated

Altman Z-score model, original Springate model, original Zmijewski model and

recalibrated Grover model. The results highlight that the ranks given by three models, such

as recalibrated Altman Z-score model, original Zmijewski model and recalibrated Grover

model are matching with each other except for Springate model. From the observation to

Table 6.34, it is understood that the ranks given by the Springate model vary greatly

compared to the ranks given by other models. The reason for this difference may be an

account of the variables used in the model. Springate model used an additional variable to

measure operational efficiency apart from the other variables such as profitability, liquidity,


157

and leverage used by the other models. Also, the Springate model works better in the

country of its origin as it shows a 93% accuracy rate but proves less accurate in an Indian

economy, due to varying economic conditions. Springate model was originally developed

taking a sample of manufacturing companies, however in the present study, the model is

applied to the service sector such as banking companies. This could be another reason for

its difference in comparison with other models.

Table 6.34 also shows the ranks given by these models to the different public and private

banks. Amongst the public banks, Bank of Baroda is given the first rank by the recalibrated

Altman, original Zmijewskis and the recalibrated Grover model. This highest rank

bestowed by Bank of Baroda is due to a better liquidity position from 2005 – 2017 as

liquidity is a variable used by all three models in arriving at a score. The ‘WC/TA’ ratio

and current ratio was highest for Bank of Baroda in these years. Further, past studies also

show a better financial position of Bank of Baroda compared to other banks. A study by

Nandi (2012) gave Bank of Baroda as the first rank in its study. Prasad and Ravindra

(2012) and Gupta (2014) gave a second rank to Bank of Baroda in its study of the analysis

of Indian nationalized banks using the CAMEL model. Similarly, studies by Gowda et al.

(2013) gave Bank of Baroda the fourth rank among all the banks considered in its studies.

Allahabad bank is given the second rank by recalibrated Altman and recalibrated Grover

model and the Springate model gave the third rank to the same bank. This is on account of

profitability variable used by all models to arrive at a score. The EBIT/TA is higher for

Allahabad bank from 2005-201. Also, the past studies such as Karthikeyan and Sivakami

(2014), shows the Allahabad bank at the fourth position in its studies of financial

performance analysis of Indian banks.

Table 6.34 shows Bank of India is given the third rank by the Recalibrated Altman model.

This is due to the higher market capitalization of Bank of India compared to other banks.

Also, a study by Nandi (2012) gave the sixth rank to Bank of India during an analysis of

Indian banks as per the CAMEL model.


158

Vijaya Bank was given 20th rank by recalibrated Altman original Zmijewski model and the

recalibrated Grover model. An evidence to this fact shows the studies of Gowda et al.

(2013) gave Vijaya bank the 19th rank in its study of the financial performance of

nationalized banks. Similarly, IDBI bank was given 19th rank by recalibrated Altman, 21st

rank given by the original Zmijewski model and 20th rank given by the recalibrated Grover

model. The rank is low due to poor liquidity and profitability position of IDBI. The current

ratio and ‘EBIT/TA’ and ‘TI/TA’ was lowest for IDBI from 2005-2017.

Table 6.34 also shows the ranks given by these models to the different private banks.

Nainital Bank is given the first rank by the recalibrated Altman, and the recalibrated Grover

model. This highest rank is on account of the better liquidity position of Nainital bank

from 2005 -2017 as liquidity variable is used by all models to arrive at a score. The WC/TA

and current ratio was highest for Nainital bank, depicting the best liquidity position among

the private banks. Further, past studies also show a better financial position of Nainital bank

compared to other banks.

Kotak Mahindra Bank is given the first rank by the original Zmijewskis model and second

rank by recalibrated Altman. This is on account of market capitalization as the ‘MVE/TL’

ratio is higher for this bank. The above table shows, HDFC bank, is given the second rank

by the original Zmijewski and recalibrated Grover model and third rank by recalibrated

Altman model. Past studies such as Gowda et al. (2013), ranked HDFC bank at the third

position in the analysis of financial performance as per CAMEL.

Table 6.34 shows the DCB was given the last rank (18th) by all the models. Evidence of this

fact shows the studies of Gowda et al. (2013) gave DCB bank the 18th rank in its study of

the financial performance of nationalized banks. Similarly, the Laxmi Vilas bank was given

the second last rank by recalibrated Altman. This may be on account of the lowest ‘RE/TA'

ratio, which shows a poor leverage position of this bank. The study of Gowda et al. (2013)

gave Laxmi Vilas bank 17th rank among the eighteen private banks in the sample.


159

Table 6.34 also shows the accuracy and reliability of the models. The ranks given by

recalibrated Altman, original Zmijewski's, and the recalibrated Grover model to the public

sector banks and private banks depict more or less the same pattern, hence these models are

accurate since it gives similar results.

6.4.6 Comparison of Bankruptcy Score between Public and Private

Banks

The present study also tries to seek the difference in the bankruptcy score between public

and private banks in India. The difference in the bankruptcy scores depicts the difference in

the liquidity, profitability and leverage position of the public and private banks, as the

bankruptcy scores are the result of these variables. To prove this difference statistically, an

Independent sample T-test was conducted. It is hypothesized that there is no significant

difference in the bankruptcy score between public and private banks in India. The

difference may not arise due to the similarity in the lending function performed by both the

banks. Both these banks are carrying the risk of default by the borrower and therefore have

an equal chance of becoming bankrupt due to nonpayment by the borrower.

Table 6.35: Result of Independent Sample T-test in Bankruptcy score of Public and Private Banks

No. Hypotheses P-

value

Decision

H1 Mean Z-score of Altman Model of public banks is equal to mean Z-score of

the Altman model of private banks

0.001 Not

supported

H2 Mean Z-score of Recalibrated Altman Model of public banks is equal to

mean Z-score of private banks

0.034 Not

supported

H3 Mean S-score of Springate model of public banks is equal to mean S-score of

Springate model of private banks

0.469 Supported

H4 Mean S-score of Recalibrated Springate Model of public banks is equal to

mean S-score of private banks

0.844 Supported

H5 Mean X-score of Zmijewski model of public banks is equal to mean X-score

of Zmijewski model of private banks

.0401 Not

Supported

H6 Mean X-score of Recalibrated Zmijewski model of public banks is equal to

Mean X-score of private banks

0.030 Not

supported

H7 Mean G-score of Grover model of public banks is equal to mean G-score of

Grover model of private banks

0.661 Supported


160

H8 Mean G-score of Grover model of public banks is equal to Mean G-score of

private banks

0.050 Not

Supported

Sources: Authors calculations based on appendix table A.40 to A.55 Notes: 5% level of Significance

The result of the independent sample T-test shows that there was a significant difference in

the mean bankruptcy score of public and private banks in the five models from the eight

models used in the study. Since the majority of models show the difference in the

bankruptcy scores of public and private banks, it can be concluded that there is a significant

difference in the mean bankruptcy scores of public and private banks. This shows a

difference in the liquidity, profitability and leverage variable of the public and private

banks as these variables are used to arrive at a score. This result fails to accept the null

hypothesis. The difference in the bankruptcy score may be on the ground that historically

the NPA of public banks is higher than the private banks. Therefore the chance of bank

becoming bankrupt is higher for public banks than private banks. Further, public sector

banks are highly controlled by the government sector whereby to implement the

government schemes, public sector banks, even though with its poor liquidity and

profitability conditions, will have to face the challenge of implementing government

policies. This may drive them towards the bankruptcy route.

6.5 Summary

The study of bankruptcy models attempts to find whether the high predictive ability that

bankruptcy models claimed is still valid in the current business environment. The present

study used four bankruptcy models such as Altman -1968, Springate -1978, Zmijewski -

1984, and Grover -2003. After the application of these models in the Indian banking sector,

the results did not hold the models very strongly. The model accuracy rate was low

compared to what was claimed by the original models in their own country. With this

background of low model accuracy rate and the critiques of models in the past literature,

the need felt to recalibrate these models. The models were recalibrated by changing its

coefficients using current data and retaining the same variables used by original models.

Multiple regression techniques were used to find the coefficients of the recalibrated model.


161

The results of the recalibrated Altman model show the difference in the predictive ability

between Altman and the recalibrated Altman model. The model accuracy rate of both the

models was varying, and also the results of both the models were not ongoing with the

robust test. Finally, the recalibrated model shows the increased accuracy compared to the

original Altman model. This proves the work of other researchers who criticizes that the

model is not working in the current period.

The results of the second model called Springate -1978, also shows a difference in the

predictive ability between Springate and recalibrated Springate model. The model accuracy

rate of the recalibrated model was low compared to the original Springate model and also

the results of the robust test were matching with the original Springate model. Thus original

Springate model performs better than the recalibrated Springate model, however, it still

works better in the country of its origin as it showed a 93% accuracy rate, but it shows less

accuracy in some other country as the economic conditions differ from country to country.

Further, the original model shows less accuracy rate due to its sample bias - as originally it

was applied for the manufacturing firms, but in the present study, it is applied in the service

sector.

The results of the third model, Zmijewski -1984, did not show any difference in the

predictive ability between the Zmijewskis and recalibrated Zmijewski's model, maybe due

to less difference in the of both the models. However, the original Zmijewski model rate

was higher, therefore the original Zmijewski model was considered for the final ranking of

banks.

The results of the fourth model known as Grover model, also does not show any difference

in the predictive ability between the Grover and recalibrated Grover model due to less

difference in the model accuracy rate of both the models. However, the recalibrated Grover

model rate was higher. Therefore it can be concluded that the recalibrated Grover model

performs better than the original Grover model.

The above summary of all models shows that recalibrated Altman and Grover performs

better than the original model and original Springate and Zmijewski showed the improved


162

accuracy over the recalibrated model. The study further extended and attempts to resolve

the issues on seeking the most accurate model amongst the above four models, hence all the

banks were ranked as per these models scores. The results of ranking highlight the fact that

the ranks given by three models, such as recalibrated Altman Z-score model, Original

Zmijewski model and recalibrated Grover model, indicate the same pattern, except for

Springate model.

An observation to the Table 6.34, it is noticed that the ranks given by the Springate model

vary greatly compared to the ranks given by other models. The reason for this difference

may be an account of the variables used in the model, economic conditions, sample bias,

etc. The ranking result also shows that the Bank of Baroda and Nainital bank is in the first

position from the public and private sector, respectively, indicating a safer financial

position of these banks. However, IDBI Bank and Karnataka bank ltd. From the public

and private sector is at the last position attracting the signal of bankruptcy, hence they need

to be cautious about their financial position.

Finally, the piece of research of bankruptcy aims to contribute to understanding the Credit

Risk Management Practices among public and private banks in India. Therefore

Independent sample t-test was conducted based on the hypothesis that means a score of

bankruptcy model of public banks is equal to the mean score of the bankruptcy model of

private banks. The results strongly fail to accept the hypothesis and conclude that there is a

significant difference in the mean bankruptcy scores of public and private banks. Since

there is a difference in the bankruptcy scores of public and private banks in India, it means

that the credit risk management practices of these banks differ.

Chapter VII Preparedness and Compliance for Basel III Norms in the Indian Banking Sector

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Chapter VII

Preparedness and Compliance for Basel III Norms in the Indian

Banking Sector

7.1 Introduction

The concern for credit risk management at an international level began in the year 1974,

with the creation of a committee for regulation and supervision of banking practices known

as Basel Committee on Banking Supervision (BCBS). Due to this concern, the formal

framework for bank's capital structure evolved in 1988, with an introduction of Basel I. In

India, the RBI implemented Basel I in the year 1992. Basel I primarily focused on credit

risk and appropriate Risk-Weighted Asset (RWA). These norms suggested a portfolio

approach to credit risk by assigning appropriate risk weight. Basel I was criticized for its

rigidity of the "One Size Fit" approach and the absence of risk sensitivity in capital

requirements (Jayadev 2013). According to Chkrabarti (2014), Basel I dealt only with

credit risk while other risks are not covered. These deficiencies made the BCBS introduce

Basel II. Basel II is a much more comprehensive framework of banking supervision as

compared to Basel I (Sarma 2007). It was built on three mutually reinforcing pillars, such

as Minimum Capital Requirements (Pillar1), Supervisory Review (Pillar 2) and Market

discipline (Pillar 3). Even in the presence of Basel II requirements, banks had to face a

global financial crisis in the year 2007-08. Therefore, in response to the 2007-08 crisis,

BCBS issued its latest guidelines called Basel III norms to ensure that credit flows even

during economic crisis.

The Indian banks are now performing their activities at the international level; hence the

Indian banks cannot afford to have a regulatory deviation from global standards. According

to the speech made by Subbarao (2017), he said “deviation from Basel III will also hurt us

in actual practice. We have to recognize that Basel III provides for improved risk

management systems in banks. Indian banks must have the cushion afforded by these risk


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management systems to withstand shocks from external systems, especially as they deepen

their links with the global financial system going forward.” Thus it becomes necessary for

them to meet international norms commonly known as the Basel norms.

Basel III aims at making most banking activities more capital intensive and focuses on four

vital banking parameters viz. capital, leverage, funding, and liquidity. These norms

proposed Capital Conservation Buffer (CCB), Counter-Cyclical Capital Buffer (CCCB),

Leverage Ratio (LR), Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio

(NSFR). In the Basel III norms, banks are required to hold more reserves by January 2015,

with common equity requirements raised to 4.5% from 2% at present. Basel III introduced

an additional reserve of 2.5% known as CCB which brings the total Tier I Capital reserves

to 7%. The new regime introduced CCCB which varies between 0% - 2.5 percent to meet

the demand of the nation’s economy in comparison to Gross Domestic Product (GDP). It

also proposes a leverage ratio to be at 3%, liquidity ratio and NSFR to be maintained at

100%.

This discussion brings to the notice of the researcher that, the implementation of the Basel

III accord is a real challenge for the Indian banks. The critical challenges for the banks are

deciding the best solution that allows them to comply with Basel III with the least cost. The

risk managers, finance managers, and Basel III program managers are under pressure to

implement Basel III. This triggers heavy preparations on the part of the Indian banks for the

successful implementation of the Basel III accord. Banks need proper preparations as it

involves huge cost and resources in terms of capital, manpower and technology. Further,

Basel III preparations are affected by several factors influencing the researcher, to find the

most significant factors in Basel III preparedness. Considering this fact, the present study

covers the preparation by the Indian banks for the implementation of the Basel III accord.

Simultaneously, a study on Basel III norms compliance is also required because RBI has

given the transitional schedule of Basel III norms, specifying the minimum requirement of

ratios, covering a period of 2013-14 to 2018-19. As per the RBI reports and the reply of the

RBI employee (2015) in the personal interview states that some banks have recently moved

towards Basel III guidelines and still in the process of following standard approaches and

not covered the advanced approaches. Thus all banks are at their own pace of meeting the


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Basel III requirements. This demands the need to study the compliance of Basel III by the

banks. As per Kant and Jain, (2013), “It is necessary to know the compliance of Basel III,

as central banks are finding it difficult to implement Basel III during the current

environment of a global economic slowdown”. A study by Parsha (2013) states that Basel

III norms favor the large banks that have better risk management and expertise, better

capital adequacy ratio and geographically diversified portfolios. Therefore this becomes a

challenge for the smaller banks that will need to restructure and to adapt to the new

environment.

The impact of the financial crisis on Indian banks was negligible, may be due to the

adequate capital position of the banks in this period. This period is popularly known as the

pre-implementation phase of Basel III norms. The post-implementation phase of Basel III

requires the bank to maintain higher capital to meet the norms. The Basel III ratios of the

banks differ in the pre and post-implementation phase of Basel III. The post-

implementation period of Basel III norms is a worry for the bank as it requires an infusion

of additional capital to maintain quantity, quality, consistency, and transparency of capital

as per set norms. According to Athira and Shanti (2014), RBI estimated that banks require

an additional capital of 5 lakh crores and the public sector banks require common equity of

1.4 - 1.5 trillion to meet Basel III requirements. Apart from this, Basel III norms require

banks to follow the CCB and CCCB for the better risk management framework of banks.

All these stipulations trigger the banks to enhance their capital base; hence there is a worry

among the bankers that higher capital requirements under Basel III would reduce the

profitability of banks and make the loan more expensive. This demands a need to

understand the state of Basel III ratios in the pre and post-implementation phase of Basel III

and also find whether public or private maintain higher capital ratios in both the phases.

The present chapter is an empirical chapter, divided into three parts. Part I explains the

Basel III preparedness (BP) of public and private sector banks in India, in terms of

understanding the factors influencing BP. Part II and Part III focuses on the compliance of

Basel III ratio by the Indian banks. Part II covers the comparison of the implemented Basel

III ratios of public and private banks with minimum requirements specified by RBI. Part III


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covers the comparison of the Basel III ratios between public and private banks in the pre-

implementation phase and also in the post-implementation phase of Basel III. Further, this

part covers the comparison of these ratios before and after the adoption of Basel III norms.

7.2 Theory and Literature Review

This part explains the Legal Theory of Finance (LTF) and a brief review of studies on

Preparedness of Basel III and compliance of Basel III norms.

7.2.1. Legal Theory of Finance (LTF)

Legal Theory of Finance was proposed by Pistor (2013) and considered as the cornerstone

for the political economy of finance. The author argues that finance is hierarchical and the

stringent enforcement of legal obligation in the financial market depends on one’s hierarchy

in the financial network. It is observed from the theory, that if the stringent implementation

of laws, puts the financial institution in a dire situation of survival, then the suspension of

the full force of law is a priority for the survival of the financial institution. This theory

could be related to understanding the contributory factors for the Basel III preparedness by

the banks. In case the cost incurred for the enforcement of norms exceeds the benefits

derived, then it will be prudent not to implement these norms for the survival of the banks.

7.2.2. Literature Review

The literature on Basel III norms highlights the critiques of Basel III and the challenges

banks may face in the implementation of Base III norms. These studies include Fry et al.

(2011), Adamson (2012), Subbarao (2012), Jain (2013), Jayadev (2013), Sbarcea (2014),

Chkrabarti and Rakshit (2014), Vishwanath (2015) and Barua (2016). Most of these

reviews state that the implementation of the Basel III accord is a potential challenge for the

Indian banks and there is a need for huge capital requirements to comply with Basel III

norms. This triggered substantial preparation on the part of Indian banks for the successful

implementation of the Basel III accord. Some of the studies have explored the preparations

made by banks such as Ernst and Young (2003), Hussein and Al-Tamami (2008), KPMG

(2008), Mirchandani and Rathore (2013), Ernst and Young (2013), Roy (2014), Hussein

and Hassan (2015), Al-Tamimi (2015), Kapoor and Kaur (2015) and Boora and Jangra

(2018).


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A study by Ernst and Young (2003) assessed the readiness of the South African banking

industry for Basel II implementation. A study by Hussein and Al-Tamimi et al. (2008)

investigated the Basel II preparations of UAE banks using four factors. A study by KPMG

(2008) examined the preparedness of thirty-five Asia Pacific banks and concluded that

banks need to devote time and resources for the full implementation of approaches. A study

by Mirchandani and Rathore (2013) explored the readiness to comply with the regulations

of Basel III of Indian public sector banks and conclude that implementation will have a

significant impact on the profitability and lending activities of the banks. Also, Ernst and

Young (2013) measured the readiness of implementation of Basel II and Basel III in the

Russian banking sector and concluded that, strategic plans are required for Basel III

awareness programs. A study by Roy (2014) found that banks follow a huge challenge to

deal with the capital aspect of Basel III preparedness.

Studies by Al- Tamimi et al. (2015) found the availability of resources as the most

important factor for Basel III preparations amongst the three factors in a sample of UAE

banks. A study by Kapoor and Kaur (2016) found that anticipated benefit is a significant

factor in Basel III preparations amongst the four factors in Indian public and private banks.

A study by Boora and Jangra (2018) found that anticipated benefit and expected cost are

the significant factor in Basel III preparations amongst the six factors in Indian public and

private banks.

According to Swamy (2013), banking is one of the most heavily regulated businesses since

it is heavily leveraged. Hence a need was felt to check the compliance of regulations.

Kumar (2014) compared the level of compliance of Basel II norms for selected

nationalized, foreign and private banks. Datey and Tiwari (2014), concludes that the Basel

III accord is expected to generate a positive response to the economy. Balsubramaniam

(2013), in its study, concludes that Indian banks have to plan for more capital in years

ahead. A study by Al-Hares et al. (2013) concludes that implementation will not take place

until 2019. A study by Sharma (2017) found a significant difference in the Tier 1 capital

ratio under Basel II and Basel III norms and also found a significant difference in the CAR

of public and private banks.


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Many studies are available on identifying the factors affecting the Basel II preparations;

those concerning Basel III preparations found to be lacking. Basel III preparations are

affected by several factors; thus there is a need to find the most significant factors

influencing its preparedness relating to Indian banks. Literature is limited on compliance of

Basel III norms as per the requirements of RBI in the Indian banking sector. Though some

studies are available on comparing the CAR, Tier I and Tier II capital ratio before and after

the adoption of Basel II Norms, but the same concerning Basel III norms left to be

examined.


To assess the Basel III preparedness of Indian banks, primary data was collected through a

predesigned structured questionnaire. Before designing a questionnaire, desk research was

conducted to study the literature on the available subject. Different studies were reviewed

to have a thorough understanding of various parameters to be included in the questionnaire.

Accordingly a self-administered and structured questionnaire was designed to collect

information about respondents. In this study, a modified questionnaire is used to examine

the preparedness level of Indian public sector banks. The questionnaire was modified based

on the reference to previous studies such as Al-Tamimi (2016), Ernst and Young (2013),

Kaur and Kapoor (2016) and Boora (2018) and further tested through content validity and

pilot study. The content validity of the questionnaire was done by taking views from

bankers and subject experts. Twenty-seven statements were asked on a five-point Likert

scale to evaluate the Basel III preparedness and four factors have been used to generalize

findings. The demographic profile of the respondents is shown in table 4.2. Reliability

analysis was used on these twenty-seven statements to assess the chances of random error

entered.

The descriptive statistics (mean, median, variance in data, Kurtosis and the maximum and

minimum value) presented in Table 7.2 were used to summarize and describe the

characteristics of the sample. The Pearson correlation coefficient was used to check the

relationship between independent variables. This study used multiple linear regression

analysis study to measure the impact of independent variables on the dependent variable.


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This is a frequently used and generally - accepted method of regression, also used in studies

such as Hussain and Tamimi (2008), and Kaur and Kapoor (2015). Assumptions of

multiple linear regression such as linearity, independence of observation, outliers,

homoscedasticity, normality, normal PP-plot etc. were checked before running a regression.

An independent sample T-test was conducted to compare the differences in Basel III

preparedness of public and private banks.

The second part of the present chapter studies the compliance of Basel III norms. The

compliance of Basel III norms in the Indian banking sector is assessed using secondary data

from Indiastat.com and annual reports of individual banks. The sample of the study consists

of 21 public and 18 private banks from the Indian banking industry. In order to assess the

implemented Basel III requirement ratio with the minimum requirement ratio, one-sample

t-test is used and the data period is from 2013 to 2018 was used. This data period consists

of Basel III implementation phase. The Basel III ratios such as the Minimum Common

Equity tier I ratio, Tier I ratio, Tier II ratio, Capital Adequacy Ratio and the liquidity ratio

were used in the test.

The present study also compares the mean Tier I, Tier II and the CAR in the pre and post-

implementation phase of Basel III using paired sample t-testt and further between public

and private banks using independent sample t-test. The tests are run on a sample of 21

public sector banks and 18 private banks. The data period used for the same is from 2009 to

2018. This data period shows two phases such as pre-implementation (2009-2013) and

post-implementation phase (2014-2015) of Basel III norms.

7.4 Conceptual framework

The conceptual framework is explained in two sections. Sections I cover conceptual clarity

on the factors influencing Basel III preparedness. Section II explains the operational

definitions of Basel III ratios.


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7.4.1. Conceptual Framework on Basel III Preparedness

It is understood from the literature that, banks are undergoing difficulties and facing

challenges in Basel III implementations, demanding a need for adequate preparations of

Basel III. The literature also states Basel III preparations are affected by different factors.

Previous studies such as Al- Tamimi (2008) evaluated Basel II preparedness in terms of six

factors such as Anticipated Benefit, Expected Impact, Education and Resources, Cost and

challenges, but its study conclude that only four factors have significant contribution in the

Basel III preparedness. Kapoor and Kaur (2016) evaluated Basel II preparedness depending

on four factors such as Anticipated Benefit, Anticipated Cost, Perceived Impact and

Expected Cost. A study by Al-Tamimi et al. (2015) evaluated Basel III preparedness in

terms of three factors, such as Expected benefit, Awareness and Expected Resources. Also,

Boora (2018) evaluated Basel III preparedness depending on five factors such as

Awareness level, Required Resources, Expected Benefit, Expected Cost and Expected

challenges. It is understood from the above literature that most of the studies conclude that

Anticipated Benefit (AB), Anticipated Cost (AC), Anticipated Impact (AI) and Expected

Challenge (EC) are the significant factors contributing to Basel III preparedness. This

signifies that Basel III Preparedness (BP) is dependent on the above four factors.

Thus the regression equation of BP is as follows:

BP= f (AB+AC+AI+EC)……….. (7.1)

In the above equation BP is a response variable which is measured using four explanatory

variables such as AB, AC, AI and EC

Where BP = Basel III Preparedness, AB = Anticipated Benefit, AC = Anticipated Cost, AI

= Anticipated Impact and EC = Expected Challenges.

The present study evaluated Basel III preparedness based on the above equation, consisting

of four factors such as Anticipated Benefit (AB), Anticipated Cost (AC), Anticipated

Impact (AI) and Expected Challenge (EC). However, 'Required Resource’ from the

literature is not considered, as it may result in a multicollinearity problem because

statements on the ‘Required Resource’ factor are included in the ‘Expected Cost’ factor.


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7.4.1.1 Basel III Preparedness (BP)

Basel III preparedness is evaluated on the four statements mentioned in annexure table

A.56. These statements are based on past studies such as Kapoor and Kaur (2016) and Al-

Tamimi et al. (2015), who perform similar studies but in a different environment. These

statements are similar but not identical, having been revised after taking into account the

environment and opinions of experts. ‘BP’ is evaluated in terms of high priority attributed

for implementation of Basel III by the management, availability of competent human

resources in the bank, familiarity and awareness among bank’s staff and the bank access to

update the technologies for Basel III implementation.

7.4.1.2 Anticipated Benefit (AB)

Anticipated Benefit is a prime factor in the preparedness of Basel III norms. The perceived

benefit is the most striking feature, which lures banks to achieve timely implementation of

Basel III norms effectively. Normally, the tendency of banker is greater the Basel III

adoption perceived by the bank, higher will be the preparedness by banks. ‘Anticipated

Benefit’ is evaluated in Basel III using five statements presented in annexure A.56. These

statements focus on the improvement of the loss absorption capacity of banks, better

liquidity risk management with an increase in short term liquidity coverage, more

transparent and detailed market disclosure, provision of counter-cyclical mechanism and

reduction in the excessive leverage risk.

7.4.1.3 Anticipated Cost (AC)

Anticipated Cost is the second factor for the Basel III implementation. The normal

tendency of the banker is higher the cost lower will be the preparedness by banks for Basel

III implementation. The factor ‘AC’ is evaluated using six statements (Annexure A.56).

These statements relates to an involvement of substantial outlay for data acquisition,

software and hardware, incurring expenditure on recruitment and training of personnel, cost

of complying with multiple regulations and disclosure requirements, cost of raising

additional capital to meet additional buffers of capital, cost of hiring consultants, and cost

of outsourcing of risk management model.


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7.4.1.4 Anticipated Impact (AI)

This factor is evaluated based on the eight statements (Annexure A.56) These statements

pertains to provision of better foundation for future development in risk management and

reduction in risk of banking crisis, pressure on Indian banks to increase capital, reduction

in the pro-cyclical behavior of bank, pressure on banks profitability and ROE, significant

increase in the RWA, increase of the dominance by large institution, decrease in investors

return, reduction in the lending capacity of banks.

7.4.1.5 Anticipated Challenges (AC)

This factor is evaluated based on the five statements (Annexure A.56). These statements are

related to increase in the RWA, design of comprehensive liquidity management framework

and information technology, closer integration of finance and risk management functions,

maintaining data integrity within the bank, recruitment of skilled staff for risk management

to meet Basel III requirements.

7.4.2. Operational Definitions (Basel III Compliance)

7.4.2.1 Minimum Common Equity Tier I

According to the RBI common equity component of tier I consist of a) paid-up equity

capital issued by banks b) share premium c) statutory reserves d) capital reserves e) other

free disclosed reserves f) surplus g) common share issued by subsidiaries h) less regulatory

adjustment prescribed by RBI.

7.4.2.2 Tier I Capital

Tier I capital comprises of minimum common equity tier I capital, capital conservation

buffer and additional tier 1 capital. The elements of tier1 capital include perpetual non-

cumulative preference shares, stock surplus, debt capital instruments, any other type of

instruments notified by reserve bank, less regulatory adjustments.

7.4.2.3 Tier II Capital

Tier II capital is an additional capital that sucks up the losses in the event of closure and

hence provides less protection to its depositors. Tier II items qualify as regulatory capital to


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the extent that they absorb losses arising from the bank’s activities. The elements of Tier II

capital include undisclosed reserves, revaluation reserves, general provisions and loss

reserves, hybrid capital instruments, subordinated debt and investment reserve account.


Capital adequacy ratio protects banks against excess leverage, insolvency and keeps them

out of difficulty. It is defined as the ratio of bank capital concerning its current liabilities

and risk-weighted assets. Risk-weighted assets are a measure of the number of bank assets,

adjusted for risks. An appropriate level of capital adequacy ensures that the bank has

sufficient capital to expand its business, while at the same time its net worth is enough to

absorb any financial downturns without becoming insolvent. It is the ratio that determines

banks capacity to meet the time liabilities and other risks such as credit risk, market risk,

operational risk, etc. As per RBI norms, Indian Schedule commercial banks should have a

CAR of 9% (1% more than stipulated Basel norms) while public sector banks are

emphasized to keep this ratio at 12%.

7.4.2.5 Leverage ratio

Basel III proposed an International framework for leverage called leverage ratio. The

objective of this ratio is to limit banks to leverage and dishearten the deleveraging that may

undermine the overall economy. One of the advantages of the leverage ratio is controlling

of the off-balance sheet leverage. Assigning too much significance to leverage ratio could

incentivize banks to focus higher risk assets with the greater return then low-risk assets

with a lower yield because all assets are equally weighted (Swami 2010). According to

(Accenture 2010), every bank should maintain a leverage ratio of 3%.

7.5 Results and Discussion

This chapter is an empirical chapter that summarizes the findings from the primary data and

secondary data. Part I explains the Basel III preparedness (BP) of public and private sector

banks in India. Part II covers the comparison of the implemented Basel III ratios of public

and private banks with minimum requirements specified by RBI. Part III covers the

comparison of the Basel III ratios between public and private banks in the pre-


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implementation phase and also in the post-implementation phase of Basel III. Further, this

part covers the comparison of these ratios before and after the adoption of Basel III norms.

7.5.1 Basel III Preparedness

This part consists of the results and discussion in terms of the sample characteristics

(demographic profile, pilot study and the test of content validity), reliability analysis,

descriptive statistics, Pearson’s correlation, multiple linear regression analysis and

Independent sample T-test.

7.5.1.1 Sample Characteristics

The details related to the sample characteristics are explained in chapter four of the present

study, under section 4.5.1 (4.5.1.a - Demographic profile, 4.5.1.b – Pilot study, 4.5.1.c –

Test of content validity).

7.5.1.2 Reliability Analysis

The reliability of the data collected on Likert five-point scales in the questionnaire from

annexure table A.56 was checked using Cronbach’s Alpha reliability coefficients, which

measure the consistency with which respondents answer the question within a scale.

Table 7.1: Reliability test on 27 statements of 116 observations

Constructs Results

Basel III Preparedness 0.73

Anticipated Benefit 0.79

Anticipated Cost 0.74

Anticipated Impact 0.81

Expected Challenge 0.76

Source: Authors calculations based on responses from the questionnaire in appendix table A.56

Table 7.1 reports that the Cronbach’s alpha coefficient of all the variables is higher than the

prescribed level (0.70), indicating excellent reliability of constructs. These results of the

reliability test are also matching with the results from other studies, such as Al- Tamimi

(2008), Kapoor and Kaur (2016), Boora and Jangra (2018).


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7.5.1.3 Descriptive Statistics and correlation analysis

This part consists of the results and discussion on descriptive statistics of the factors of

Basel III preparedness. The descriptive statistics are used to get an idea of differences in the

attributes of public and private banks. The descriptive statistics describe the data in terms of

mean, median, variance in data, Kurtosis and the maximum and minimum value.

Table 7.2: Descriptive Statistics of 77 respondents of public and 39 respondents of private banks

Public banks Private banks

AB AC AI EC BP AB AC AI EC BP

Mean 4.60 2.19 4.00 4.39 4.18 4.90 4.02 4.21 4.35 4.67

Median 4.13 4.09 3.83 3.09 4.08 4.56 4.45 4.06 4.34 4.19

Std. Dev 1.10 0.45 0.75 0.34 1.06 0.78 0.87 0.35 0.56 0.21

Kurtosis 2.3 1.2 0.1 1.34 1.43 1.7 0.03 1.7 0.82 2.8

Minimum 2.80 1.20 1.8 3.1 1.5 3.2 2.05 1.8 1.90 4.16

Maximum 5 5 5 5 5 5 5 5 5 5

Count 77 77 77 77 77 39 39 39 39 39

Source: Authors calculations based on the response from the questionnaire in appendix table A.56

The mean score for the variable AB is highest amongst the other variables under study.

This states that public and private banks are aware of and perceives the anticipated benefit

of Basel III. The respondents strongly agree with the benefits associated due to the

implementation of Basel III. The mean score of the public sector banks for the AC is lowest

amongst all the factors affecting the BP. This signifies that the perception of respondents

towards the variable AC disagrees with the statements. The standard deviation in the above

table shows that variance in data of private banks is low than the public banks. The value of

kurtosis is less than 3 in the above table indicate the normality of data.

7.5.1.4 Development of Model

This part evaluates the impact of factors of Basel III preparedness on the Basel III

preparedness of the banks with the help of multiple linear regression. The Model uses BP as

the response variable and four variables such as AB, AC, AI and EC as an explanatory

variable. The explanatory variable and response variables are derived through a literature

review. Each of these variables is important to measure and examine the Basel III

preparedness of banks. The regression analysis is carried out to analyze the effect of


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explanatory variables on the Basel III preparedness of public and private banks. The

regression model is developed to test the following null hypothesis.

H05: There is no statistical impact of factors such as Anticipated Benefit, Anticipated Cost,

Anticipated Impact and Expected Challenge on the Basel III Preparedness of the public and

private banks in India.

The hypothesis states that the factor AB, AC, AI and EC are not the influential factors for

Basel III preparedness. This hypothesis is framed based on the assumption of fundamental

uncertainty of Legal Theory of Finance (LTF). The theory states that, if the cost of

enforcement of norms exceeds the benefit it gains, then the cut down of these norms will be

a priority for the survival of banks. This theory is related to understanding the Non-

contributory factors for the Basel III preparedness by the banks. The factors will be non

contributory to BP, when there is no impact of these factors on BP.

The basic aim behind the development of the model is to test the above hypothesis and to

determine the significance of the independent variable on the dependent variable. There

were 77 cases of public sector banks and 39 cases of private banks considered to run the

multivariate regression. All the assumptions required to run the regression were checked.

There was no problem with autocorrelation as the Durbin Watson Statistics lied in the range

of 0 to 4. The Durbin value (1.24 = Public banks and 1.87 = Private banks) meets the

assumption of independence of observation. There was no problem with multicollinearity

as the Variance Inflation Factor (VIF) was less than five and the Tolerance value was less

than 0.2. A scatter plot showed the linearity of data. Outliers were checked with the help of

casewise diagnostics, 71 observations of public banks and 37 observations of private banks

(after removing outliers) were used to develop a model.

The assumption of Homoscedasticity was satisfied by observing the scatter plot, which

shows the residuals randomly scattered. The normality test was satisfied by observation to

the histogram. The assumption of the correlation between the independent variable is

shown with the help of the correlation matrix. This matrix explains the extent and degree of


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the relationship between the independent variable. The matrix shows the relationship

between the independent variable such as AB, AC, AI, and EC.

Table 7.3: Correlation matrix calculated using 77 respondents of public and 39 respondents of private banks

Public Sector banks Private Banks

Variables AB AC AI EC Variables AB AC AI EC

AB 1 - 0.390 0.59 0.43 AB 1 - 0.243 0.53 0.56

0.021* 0.050* 0.082 0.02* 0.02* 0.03*

AC - 0.390 1 0.39 0.53 AC - 0.243 1 0.34 0.59

0.021* 0.031* 0.001* 0.02* 0.04* 0.05*

AI 0.59 0.39 1 0.45 AI 0.53 0.34 1 0.67

0.050* 0.031* 0.030* 0.02* 0.04* 0.00*

EC 0.43 0.53 0.45 1 EC 0.56 0.59 0.67 1

0.082 0.001* 0.030* 0.03* 0.05* 0.00*

Source: Authors calculations based on responses from the appendix table A.56.

Notes: *Correlation at 5% level of significance

The correlation analysis is conducted to meet the assumption of multiple linear regression.

Table 4.7 shows the relationship between the independent variable such as AB, AC, AI, and

EC. The relationship among the independent variable of the banks shows the correlation

coefficient value less than 0.60. As per the rule of thumb, the correlation among

independent variables should not exceed 0.70 (Malhotra, 2006). This shows that there is no

problem of multicollinearity. These results are in accordance with the results of Kapoor

and Kaur (2016), Al Tamimi (2015) and Boora (2018).

Table 7.4: Result of Regression Analysis calculated using 71 respondents of public and 37 respondents of private banks

Public sector banks Private Banks

Unstandardised Coefficient Unstandardised Coefficient

β Std.

error

T-value sig β Std.

error

T-value Sig

Constant 4.38 .321 6.06 0.000* Constant 5.17 .131 8.93 0.000*

AB .352 .023 4.36 0.000* AB 0.380 .145 3.37 0.000*

AC 0.043 .134 2.73 0.030* AC -0.056 .176 2.97 0.450

AI .126 .043 2.90 0.004* AI 0.154 .112 3.12 0.006*

EC .078 .180 .648 0.043* EC 0.067 .142 2.13 0.350

Source: Authors calculations *Significance level 5%

Public banks: Durbin Watson Test 1.24, ANOVA value-F (4, 67) = 9.42, Sig = 0.00 Model Fitness: R= 0.38, R2 = 0.25,

AdjR2 = 0.23, Private Banks: Durbin Watson Test 1.87, ANOVA value-F (4, 33) = 8.069, Sig = 0.00 Model Fitness: R=

0.57, R2 = 0.35, AdjR2 = 0.342

BP and AB

The impact of AB on BP is tested with the help of regression analysis. The results of public

and private banks in Table 7.4 show that there is a significant and positive impact of AB on


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BP. This variable is the important predictor amongst the independent variable as it is the

highest contributor to the dependent variable compared to other variables. Therefore, an

inference can be drawn that BP in the Indian banking industry is significantly influenced by

AB. This result shows the bank's perception towards Basel III in terms of improvement in

the quality of capital, better liquidity risk management, provision of counter-cyclical

mechanism, and reduction in the risk of excessive leverage. These results are in par with

Kapoor and Kaur (2016) Al-Tamimi and Al Mazoorie (2015) and Boora and Jangra (2018).

However, these results do not support the null hypothesis. The significant positive impact

of AB factor shows the motivation of the banks towards the thorough preparation of the

Basel III preparations.

BP and AC

The impact of AC on BP of banks in India highlights that AC has a significant impact on

BP in the case of public banks, whereas it has an insignificant impact on the BP with

respect to private banks. This result reveals the fact that public sector banks are ready to

bear the cost of implementation. These banks are ready to spend on data acquisition,

software and hardware development, raising additional capital, hiring consultants, etc.

The negative impact of AC on the BP shows that, hike in the cost of implementation would

affect the reduction in the preparedness of Basel III implementation. Thus private banks are

more concerned about the substantial outlay involved in data acquisition, software and

hardware development, expenditure on recruitment and training of personnel, cost of

complying with multiple regulators and disclosure requirements, cost of raising additional

capital, outsourcing of risk management model, etc. The results are in line with the results

of Kaur and Kapoor (2016) and Accenture (2012). These findings of private banks are

supported by the assumption of fundamental uncertainty of Legal Finance Theory whereby,

if the cost of enforcement of norms exceeds the benefit it gains, then the cut down of these

norms will be a priority for the survival of banks.

BP and AI

The impact of AI on BP of banks in India highlights that AI has a significant impact on BP.

These results highlight the significant contribution of the anticipated impact of Basel III


179

preparedness. These results fail to accept the null hypothesis. The banks perceive the

anticipated impact in terms of raising additional capital, improvement in profitability and

liquidity, etc. These aspects drive the banks towards Basel III preparations. These results

are in line with Kapoor and Kaur (2016) and Al-Tamimi (2015).

BP and EC

The results of public sector banks show that there is a significant impact of EC on BP.

These results fail to accept the null hypothesis. It also signifies that banks need to face a

challenge in terms of an increase in RWA calculations, designing of comprehensive

liquidity management framework, the achievement of integration between finance and risk

management function, maintenance of data integrity and recruitment of skilled staff. These

challenges will drive the banks towards Basel III preparedness. However, the results of

private banks in India show an insignificant impact of EC on BP. These results support the

null hypothesis and signify the expected challenge not as a driving force for Basel III

preparations. Therefore, an inference can be drawn that BP in the private banking industry

is insignificantly influenced by EC.

Model Fit

Multiple linear regression was run to understand the impact of independent variables (AB,

AC, AI and EC) on the Basel III preparedness of banks. Table 7.4 shows the results of

regression analysis on public and private sector bank’s data. The Results of public sector

banks show that adjusted R2 is 0.23, which means that 23% of the variation in the

dependent variable i.e. BP is due to the explanatory variables (AB, AC, AI and EC) and

remaining 77% variation is due to other factors. F-value is significant at 1%; hence, we can

say that the overall Model is a good fit. The value of beta (β) explains the contribution of

the independent variable. The factor AB shows the highest beta value, which means its

contribution, is more than other independent variables in BP. In the case of private banks,

results show that adjusted R2 is 0.34 which means that 34% of the variation in the

dependent variable that is BP is due to the explanatory variables (AB, AC, AI, and EC) and

remaining 66% variation is due to other factors. F-value is significant at 1%; hence it can

be said that overall Model 1 is a good fit. The value of beta (β) explains the contribution of


180

the independent variable. The factor AB shows the highest beta value, which means its

contribution, is more than other independent variables in BP, whereas AC contributes

negatively to BP.

Moreover, the regression results conclude that the most important factor in implementing

Basel III is the expected benefits from Basel III. This will positively affect the preparations

of banks towards Basel III. The more the Indian banks comply with Basel III norms, the

more benefits they will attain and these benefits will enrich them in terms of the improved

risk management process. The factor ‘AC’ shows a negative contribution to the Basel III

preparedness of private banks. The negative contribution of ‘AC’ endorsed the theory of

legal finance and supported the assumption of the fundamental uncertainty of this theory.

As per this assumption, if the cost of enforcement of norms exceeds the benefit it gains,

than the suspension of enforcement of these norms will be at the priority for the survival of

banks.

7.5.1.5 Comparison of the factors of BP between public and private banks

The present study also tries to compare the difference in the Basel III preparedness between

public and private banks in India. Thus, if there is a difference in the factors of BP between

public and private banks in India, it means that the level of Basel III preparedness of these

banks differs. To prove this difference statistically, an Independent sample t-test was

conducted based on the hypothesis that the mean score of factors of public banks is equal to

the mean score of factors of private banks.

The above hypothesis also states, the uniformity in the preparations of Basel III norms by

public and private banks in India. This hypothesis is framed based on the uniform schedule

or the phase-in arrangement of the Basel III norms given by the RBI to the Indian banks.

This schedule makes the uniform enforcement of the Basel III norms by public and private

banks. Therefore both these banks are undertaking similar steps for the preparations of

Basel III norms. Hence there may not be any difference in the preparations of Basel III

norms by public and private banks in India. The Independent sample t-test was conducted

based on the satisfaction of the assumption of normality and homogeneity of data.

Table 7.5: Result t-test of 77 observations of public banks and 39 observations of private banks


181

Hypotheses P-value Decision

Mean AB of public banks is equal to mean AB of private banks 0.91 Supported

Mean AC of public banks is equal to mean AC of private banks 0.00 Not Supported

Mean AI of public banks is equal to mean AI of private banks 0.08 Supported

Mean EC of public banks is equal to mean EC of private banks 0.07 Supported

Source: Author's calculations based on responses from appendix table A.52. Note: Significance level at 5%

The result of the independent sample t-test supports the null hypothesis. The result shows

an insignificant difference in the three factors of BP amongst the four factors between the

public and private banks. Thus, the factors suggest that both the categories of banks are on

the equal foot of Basel III preparedness. These results are supported by the results of the

regression analysis of the present study, which is similar in terms of model fit for data.

7.5.2 Compliance of Basel III Ratios by an Indian Banks

This Part explains the compliance of Basel III ratios of public and private banks with the

minimum required ratio prescribed by RBI in Basel III. This study used five Basel III

ratios, such as the Minimum Common Equity Tier 1 Capital ratio, Minimum Tier 1 Capital,

Tier II capital, Capital Adequacy Ratio (CAR) and Leverage ratio. The comparison of

actual ratios with the prescribed limit is made using a one-sample t-test. It is hypothesized

that the mean actual Basel III compliance ratios of public and private banks in India are

lower or equal to the minimum Basel III ratio prescribed by the RBI. The actual Basel III

requirement ratios of public banks and private banks could be lower because, historically

banks were maintaining a low provision coverage ratio, as the Indian banks have a high

proportion of NPAs that are not provided for the capital.

Table 7.6: Result of one-sample t-test of the capital ratios of 21 public and 18 private banks

Year 2013 2014 2015 2016 2017 2018

Minimum Common Equity Tier I

Test value 4.5 5 5.5 5.5 5.5 5.5

Mean of Public banks Non-

Compliant

7.65 7.82 8.11 8.07 7.89

Mean of Private banks Non-

Compliant

Non-

Compliant

Non-

Compliant

Non-

Compliant

Non-

Compliant

11.92

Results of public banks

t (20) =16.81,

P=0.000 (SD)

t (20) =13.80,

P=0.000 (SD)

t (20) = 13.78,

P=0.00(SD)

t (20) =12.96,

P=0.000 (SD)

t (20) =16.81,

P=0.000 (SD)

t (20) =

7.95, P=0.000

(SD)

Result of Private banks

Non-

Compliant

Non-

Compliant

Non-

Compliant

Non-

Compliant

Non-

Compliant

t (10)

=7.516, P=0.000

(SD)

Minimum Tier I Capital

Test value 6 6.5 7 7 7 7


182

Mean of Public banks 8.68 8.11 8.30 8.30 9.07 8.90

Mean of Private banks 12.12

11.60 12.16 11.49 11.99 12.53

Results of public banks t(20)=13.336,

P=0.00(SD)

t(20)=8.09,P=0.

00(SD)

t(20)=7.538,P=

0.00(SD)

t(20)=7.538,P=

0.00(SD)

t(20)=9.71,

P=0.00(SD)

t(20)=5.9

9,P=0.00(SD)

Result of Private banks t(17)=11.571,

P=0.00(SD)

t(17)=7.63,P=0.

00(SD)

t(17)=7.857,

P=0.00(SD)

t(17)=6.759,P=

0.00(SD)

t(17)=9.585,

P=0.00(SD)

t(17)=9.1

27,P=0.00(SD)

Tier II capital

Test value 3 2.5 2.625 3.25 3.875 4.5


Mean of Private banks 1.96 1.9 1.7 1.69 1.52 1.84

Results of public banks

t(20)=2.89,P=

0.009(SD)

t(20)=4.44,P=0.

00(SD)

t(20)=3.198,P=

0.005(SD)

t(20)=-

6.69,P=0.00(S

D)

t(20)=-

9.5,P=0.00(

SD)

t(20)= -

17.482,P

=0.000(SD)

Result of Private banks t(17)=-

2.9,P=0.009(SD)

t(17)=-

1.513,P=0.149 (NSD)

t(17)=-

3.085,P=0.007(SD)

t(17)= -

4.977P=0.00(SD)

t(17)=-

9.918,P=0.00(SD)

t(17)=-

8.254,P=0.00(SD)

Capital Adequacy Ratio (CAR)

Test value 9 9 9.625 10.25 10.875 11.50


Mean of Private banks 14.7 14.00 14.30 13.58 13.68 14.58

Results of public banks t(20)=17.54,P

=0.00(SD)

t(20)=10.96,P=

0.00(SD)

t(20)=9.19,P=0.

00(SD)

t(20)=4.63,P=0.

00(SD)

t(20)=4.05,

P=0.001(SD)

t(20)=-

8.21,P=0.42(NSD)

Result of Private banks t(17)=10.76,P

=0.00(SD)

t(17)=8.587,P=

0.00(SD)

t(17)=7.390,P=

0.00(SD)

t(17)=

5.572,P=0.00(S

D)

t(17)=5.392,

P=0.001(SD

)

t(17)=-

5.004,P=

0.00(SD)

Leverage ratio

Test value 3 3 3 3

Mean of Public banks NA NA Non -Compliant 5 5.13 4.68

Mean of Private banks NA NA Non- Compliant 7.64 8.04 8.09

Results of public banks NA NA NA t (15) = 14.31, P =

0.00 (SD)

t (15) = 19.21, P =

0.00 (SD)

t (15) = 8.44

P = 0.00 (SD)

Result of Private banks NA NA NA t (13) = 8.09, P =

0.00 (SD)

t (13) =9.46, P =

0.00 (SD)

t (17) =

10.20, P = 0.00 (SD)

Source: Authors Calculation using data from Annexure A.57- A.60. Notes: *SD - Significant difference at 95% confidence level **

NSD- Non-significant Difference

7.5.2.1 Minimum Common Equity Tier1 Ratio

Although the RBI draft guideline states the phase-in arrangement began from 2013

onwards, however, most of the public and private banks from the sample show the

implementation to Basel III from 2014 onwards as per a reference to their annual reports.

The minimum common equity ratio was 2% in Basel II norms, however, in Basel III norms

it increased to 4.5%. An observation to Table I shows, all the public banks are compliant

towards this ratio from the year 2014 onwards, whereas as per the reference to annual

reports it shows that private banks were not maintaining this ratio separately, but it was


183

included in the Tier I capital. Most of the private banks have started maintaining this ratio

separately from the year 2018 onwards.

Another observation to Table I show that, this ratio was maintained at a higher level by all

the public banks compared to what was prescribed by the RBI. This depicts a difference in

the actual and the prescribed ratio lay down by the RBI. To test this difference, a

statistically one-sample t-test was used. The result of one sample t-test fails to accept the

null hypothesis as the mean difference in implemented Basel III requirement ratios (sample

mean) of public and private banks is higher than the minimum requirement ratios of RBI

(population mean).

7.5.2.2 Tier 1 capital Ratio

There was a difference of 2% in the Tier 1 capital ratio from Basel II to Basel III norms.

Both public and private banks showed compliance with this ratio from the beginning of

Basel III norms. The public and the private banks from the sample show higher compliance

to this ratio. This depicts the difference in the complied ratios and prescribed ratio. To

prove this difference, statistically one sample T-test was used. The result of one sample T-

test fails to accept the null hypothesis as the mean difference in implemented Basel III

requirement ratios (sample mean) of public and private banks is higher than the minimum

requirement ratios of RBI (population mean).

7.5.2.3 Tier II capital Ratio

The public and private banks shows a compliance to this ratio from the beginning of Basel

III norms; The result of one sample T-test shows that 83% of the observations have a

significant difference in the population mean and a sample mean of private banks, This

results accepts the null hypothesis as the mean difference in implemented Basel III

requirement ratios (sample mean) of private banks is lower than the minimum requirement

ratios of RBI (population mean) as per an observation to Table I. In case of public banks

there is a significant mean difference in the population mean and the sample mean. This

difference is due to higher compliance with this ratio in the first three years and lower

compliance in the last three years.


184


The Basel III norms stipulated a Capital Adequacy Ratio (CAR) of 8%. However, as per

RBI, Indian scheduled commercial banks are required to maintain a CAR of 9%. Till 2019

banks are expected to maintain the (CAR) of 11.5%, which will match with the

international standard laid down by BCBS. An observation to Table I show that, all the

public and private banks are complying with the CAR at a much higher level than

prescribed by the RBI from the year 2013-2019 except for private banks in the last year of

the implementation phase. As in the last year, banks are required to maintain a CAR of

11.5%, which is beyond the capacity of private banks. This shows a difference in the

prescribed value and the implemented value of CAR by the public and private banks. To

test this difference statistically, one sample T-test was used. The result of one sample T-test

fails to accept the null hypothesis as the mean difference in implemented Basel III

requirement ratios (sample mean) of public and private banks is higher than the minimum

requirement ratios of RBI (population mean).

7.5.2.5 Leverage Ratio

The Basel III norms levied a 3% leverage ratio of Tier 1. An observation of Table I show

that public and private banks from the sample complied with the leverage ratio at a much

higher level than the stipulated rate laid down by RBI. This shows a difference in the

prescribed value and the implemented value of leverage ratio by the public and private

banks. This difference is tested with aid of statistical technique ‘one sample T-test’. The

result of t-test fails to accept the null hypothesis as there is a significant mean difference in

the population mean and the sample means from the year 2016-2018. This difference is due

to higher compliance by the public sector banks towards the minimum ratio laid down by

RBI.

There is no data in the annual statement about Liquidity Ratio and the Net Stable Funding

Ratio.This shows that banks were not compliant with these ratios as per the timeline

prescribed by the RBI.


185

7.5.3 Comparison of Capital Ratios of Public and Private Banks

This part comprises of comparison of the capital ratios between public and private banks in

the pre-implementation phase and also in the post-implementation phase of the Basel III

period using an Independent sample T-test. This part further consists of a comparison of

capital ratios before and after the adoption of Basel III norms of individual public and

private banks using paired sample T-test.

7.5.3.1 Comparison of Capital Ratios in two Phases

A comparison of capital ratios between public and private banks was made in two phases,

to identify whether banks were maintaining sufficient capital in these phases. The two

phases are the pre-implementation phase of Basel III norms and the post-implementation

period. The results will also identify a change in the capital ratio in the post-implementation

phase of Basel III norms. Thus, a study will be able to identify whether in the pre-

implementation and in the post-implementation period, the capital ratios of public or

private banks is better.

This study used three capital ratios, such as the Minimum Tier 1 Capital, Tier II capital, and

Capital Adequacy Ratio (CAR). The comparison of these ratios between public and private

banks in two phases is made using an independent sample t-test. It is hypothesized that,

‘there is no statistical difference in the mean Tier I, Tier II and Capital Adequacy Ratio

between public and private banks in India in the pre-implementation and post-

implementation phase of Basel III norms.’ The pre-implementation phase covers the period

from 2009-2013 and in this phase, banks were following Basel II norms. Similarly, the

post-implementation phase covers the period from 2014 -2018 and in this period, banks

were following Basel III norms. Due to the implementation of these norms by the banks,

the difference in the above ratios may not be there in this period between public and private

banks.

Table no 7.7: Result of Independent sample t-testt on Cross-sectional data of 21 public and18 private banks

Pre -Implementation phase Post - Implementation phase

Public banks Mean tier I

capital ratio

Mean Tier II

capital ratio

Mean CAR Mean tier I

capital ratio

Mean Tier II

capital ratio

Mean CAR

Allahabad Bank 8.38 4.33 12.71 7.76 2.55 10.31


186

Andhra bank 8.82 4.46 13.27 8.26 2.97 11.23

Bank of Baroda 9.73 4.45 14.18 9.68 2.41 12.09

Bank Of India 8.50 3.72 12.22 8.74 2.94 11.68

Bank Of Maharashtra 7.30 5.36 12.66 9.10 2.58 11.68

Canara Bank 9.51 4.47 13.98 8.93 2.75 11.69

Central Bank Of India 7.20 4.98 12.18 7.85 2.38 10.23

Corporation Bank 8.70 4.98 13.68 8.42 2.72 11.15

Dena Bank 8.16 3.69 12.21 8.34 2.85 11.19

I D B I Bank Ltd. 7.43 5.42 12.85 8.08 3.16 11.24

Indian Bank 11.21 2.15 13.36 10.46 1.86 12.32

Indian Overseas Bank 8.17 5.37 13.54 7.58 2.48 10.06

Oriental Bank Of Comm. 9.78 3.12 12.90 8.64 2.63 11.26

Punjab & Sind Bank 8.28 5.03 13.31 8.76 2.22 10.98

Punjab National Bank 9.11 4.08 13.19 8.52 2.64 11.16

State Bank Of India 9.18 4.10 13.28 9.90 2.66 12.57

Syndicate Bank 8.66 3.99 12.65 8.59 2.89 11.48

Uco Bank 7.84 5.23 13.07 8.52 2.75 11.27

Union Bank Of India 8.28 4.13 12.41 8.27 2.72 10.99

United Bank Of India 8.36 4.33 12.70 8.16 2.68 10.84

Vijaya Bank 8.71 4.08 12.78 9.40 2.89 12.30

Mean of public banks 8.63 4.36 13.1 8.67 2.65 11.31

Private banks

Axis Bank Ltd. 10.31 4.25 14.56 12.44 3.22 15.66

Catholic Syrian Bank Ltd. 8.95 2.59 11.54 9.82 1.11 10.92

City Union Bank Ltd. 12.14 0.95 13.09 15.34 0.49 15.83

DCB Bank Ltd. 12.19 1.89 14.08 12.73 1.66 14.40

Dhanalaxmi Bank Ltd. 9.49 2.66 12.14 8.02 1.96 9.98

Federal Bank Ltd. 14.18 1.16 15.35 8.02 0.57 14.39

H D F C Bank Ltd. 11.75 4.78 16.53 12.94 2.61 15.55

ICICI Bank Ltd. 13.09 5.46 18.54 13.79 3.65 17.43

Indusind Bank Ltd. 10.75 3.65 14.40 13.63 0.72 14.35

Jammu & Kashmir Bank 11.98 2.08 14.06 10.20 1.66 11.87

Karnataka Bank Ltd. 10.64 2.40 13.05 11.06 1.53 12.60

Karur Vysya Bank Ltd. 13.31 1.22 14.53 12.17 1.10 13.27

Kotak Mahindra Bank Ltd. 16.00 2.37 18.37 16.67 0.90 17.58

Lakshmi Vilas Bank Ltd. 9.94 2.82 12.76 8.54 2.08 10.62

Nainital Bank Ltd. 14.13 0.80 14.93 14.27 0.42 14.68

R B L Bank Ltd. 34.16 0.46 34.62 12.71 1.32 14.03

South Indian Bank Ltd. 12.12 2.31 14.43 10.45 1.80 12.25

Yes Bank Ltd. 10.30 7.68 17.98 12.52 4.68 17.20

Mean 13.07 2.75 15.83 11.96 1.75 14.03

Result of Independent

sample T-test t(193) =-6.87, P=0.00(SD)

t(193) =7.29 , P=0.00(SD)

t(193) =-4.64 , P=0.00(SD)

t(193) = -13.55, P=0.00(SD)

t(193) = 6.5, P=0.00(SD)

t(193) =-10.11, P=0.00(SD)

Source: Authors calculation based on the data from appendix table A.57-A.60

An observation to table 7.7 shows that both the public and private banks were maintaining

sufficient capital in the pre-implementation phase of the Basel III period. This could be one

of the reasons for having less impact of the financial crisis on the Indian economy, as the

banks were maintaining sufficient capital during this phase. There is a significant difference

in the tier I capital ratio between public and private banks in both the phases, as per the

independent sample T-test. This difference is due to the maintenance of a higher ratio by

private banks. The higher mean ratio of private bank signifies a safer capital position of


187

these banks. Also, a higher mean ratio is on account of the higher ratio maintained by the

RBL bank ltd and Kotak Mahindra Bank as per observation to table 7.7. Further, a ratio

may be higher at the cost of the tier II capital ratio of these banks, as the tier II capital of the

private bank is relatively low compared to public banks.

The results of the mean tier II capital ratio show a significant difference between public and

private banks in both the phases as per the Independent sample T-test. The difference is due

to the maintenance of a higher ratio by the public bank. The private banks have a low tier II

capital ratio that may be at the cost of tier I capital ratio, as these banks have complied to

tier I capital ratio at a much higher level then ratio prescribed by RBI.

There is a statistical significant difference in the mean CAR between public and private

banks in both phases. These results are consistent with the study of Sharma (2017). The

difference is due to the maintenance of a higher ratio by private banks. The private banks

have maintained sufficient capital in the pre-implementation as well as the post-

implementation period of Basel III, signaling better credit risk management practices

followed by these banks.

7.5.3.2 Comparison of Basel III ratios before and after the adoption of Basel III

norms

A paired t-test was run on a sample of 21 public sector banks to determine whether there

was a statistically significant mean difference in the mean of tier I cap ratio, tier II capital

ratio, and the CAR between pre-implementation and post-implementation phase of Basel III

norms. A test is run based on the null hypothesis, which states that there is no statistical

difference in the mean Tier I, Tier II, CAR before and after the adoption of the Basel III

norms of public banks and private banks in India. The significant difference in these ratios

may not arise for public banks, because historically, these banks were maintaining adequate

capital and controls the majority share in the market, possessing good capital health.

Therefore there may not be any difference in the above ratios of public banks before and

after the adoption of Basel III norms.


188

Similarly, the difference in capital ratios may not arise in the case of the private banks, as

these banks are always playing the precautionary role before granting a loan to the business

community and therefore, the NPA’s of these banks were always less compared to NPA’s

of public banks. Further, these banks were maintaining higher capital ratios, even before the

adoption of Basel III norms as per observation to appendix table A.60 (CAR).

Table 7.8 Cross-sectional data showing the result of the Paired T-test of 21 public sector banks

S.N Banks Tier I cap ratio Tier II cap ratio CAR

1 Allahabad Bank t(4)=3.06,P=0.0391(SD) t(4)=4.29,P=0.01(SD) t(4)=6.90,P=0.02(SD)

2 Andhra Bank t(4)=1.527,P=0.201(NSD) t(4)=2.60,P=0.06(NSD) t(4)=2.62,P=0.06(NSD)

3 Bank of Baroda t(4)=0.099,P=0.92(NSD) t(4)=9.60,P=0.001(SD) t(4)=6.48,P=0.003(SD)

4

Bank of India

t(4)=0.38,P=0.723(NSD) t(4)=1.90,P=0.130(NS

D)

t(4)=0.55,P=0.60(NSD)

5 Bank of

Maharashtra

t(4)=-3.3,P=0.030(SD) t(4)=11.92,P=0.00(SD) t(4)=2.12,P=0.1(NSD)

6

Canara Bank

t(4)=1.32,P=0.256(NSD) t(4)=2.25,P=0.087(NS

D)

t(4)=2.25,P=0.08(NSD)

7 Central Bank of

India

t(4)=-1.31,P=0.260(NSD) t(4)=6.71,P=0.003(SD) t(4)=4.96,P=0.008(SD)

8 Corporation

Bank

t(4)=0.69,P=0.524(NSD) t(4)=6.88,P=0.02(SD) t(4)=4.30,P=0.012(SD)

9 Dena Bank t(4)=-0.364,P=0.734(NSD) t(4)=5.23,P=0.006(SD) t(4)= 1.8,P=0.14(NSD)

10 I D B I Bank t(4)=-1.52,P=202(NSD) t(4)=4.91,P=0.008(SD) t(4)=1.9,P=0.12(NSD)

11 Indian Bank t(4)=0.837,P=0.453(NSD) t(4)=0.766,P=0.486(NS

D)

t(4)=1.30,P=0.236(NSD)

12 Indian Oves

Bank

t(4)=2.82,P=0.04(NSD) t(4)=6.21,P=0.003(SD) t(4)=6.52,P=0.003(SD)

13 Oriental Bank

of Commerce

t(4)=3.37,P=0.028(NSD) t(4)=1.44,P=0.22(NSD) t(4)=6.13,P=0.004(SD)

14 Punjab & Sind

Bank

t(4)=-1.45,P=0.220SD) t(4)=24.68,P=0.00(SD) t(4)=9.17,P=0.001(SD)

15 Punjab National

Bank

t(4)=1.13,P=0.318(SD) t(4)=4.12,P=0.01(SD) t(4)=4.24,P=0.001(SD)

16 State Bank of

India

t(4)=-1.79,P=0.146(SD) t(4)=7.29,P=0.002(SD) t(4)=1.299,P=0.26(NSD)

17 Syndicate Bank t(4)=0.17,P=0.813(SD) t(4)=3.14,P=0.03(SD) t(4)=2.98,P=0.04(SD)

18 Uco Bank t(4)=-1.11,P=0.329(NSD) t(4)=6.64,P=0.003(SD) t(4)=2.12,P=0.101(NSD)

19 Union Bank Of

India

t(4)=0.031,P=0.977(NSD) t(4)=5.26,P=0.006(SD) t(4)=2.40,P=0.07(NSD)

20 United Bank Of

India

t(4)=0.432,P=0.688(NSD) t(4)=5.13,P=0.007(SD) t(4)=2.39,P=0.075(NSD)

21

Vijaya Bank

t(4)=-1.08,P=0.339(NSD) t(4)=1.84,P=0.00(SD) t(4)=0.624,P=0.567(NSD

)

Overall

t(104)=13.05

P=0.78(NSD)

t(104)=16.06,

P=0.00(SD)

t(104)=11.74

P=0.00(SD)

Source: Authors calculation based on the data from appendix table A.57- A.60

The result of the paired t-test fails to accept null hypothesis with respect to four banks such

as Allahabad Bank, Punjab and Sind Bank, Punjab National Bank and Syndicate bank.


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These banks show the maintenance of higher ratios in the pre and post-implementation

phase of Basel III. However, concerning the other four banks (Andhra Bank, Bank of India,

Canara Bank, and Indian bank), this test fails to help to accept the null hypothesis. The

result shows the statistical insignificant difference in the compliance of these three ratios in

the post and pre-implementation phase of Basel III. This insignificant difference is on

account of maintaining the same level of ratio in the post and pre-implementation phase. In

other words, these banks were following better ratio in the pre-implementation period of

Basel III compared to what was prescribed in Basel III norms, hence in the post-

implementation phase of Basel III norms, these same banks maintained the same level

prescribed in Basel III norms. Therefore the difference between the two mean is

insignificant.

Overall the result of public sector banks indicates a similarity in the mean of tier1 capital

ratio in the pre and post-implementation phase of Basel III norms. These similarities

indicate that banks were maintaining the same level of tier I capital in the Pre-and post-

implementation phase. In the case of the Tier II capital ratio, the overall paired T-test result

shows a significant mean difference in the pre and post-implementation period of Basel III

norms. This difference indicates that banks were not maintaining a proper level of Tier II

capital. Similarly, the Capital Adequacy ratio of public sector banks shows a significant

mean difference in the pre and post-implementation period of Basel III norms. The

difference is significant due to maintaining a higher level of Capital Adequacy Ratio (CAR)

in the pre-implementation phase.

Table 7.9 - Cross sectional data showing the result of Paired T-test of 18 Private sector banks

Private banks Result of paired T-test

Tier I cap ratio

Result of Paired T-test

Tier II cap ratio

Result of paired T-test

CAR

Axis Bank Ltd. t(4)=-4.01,P=0.016(SD) t(4)=6.65,P=0.003(SD) t(4)=-1.66,P=0.171(NSD) Catholic Syrian

Bank Ltd. t(4)=-1.69,P=0.165(NSD) t(4)=6.58,P=0.003(SD) t(4)=1.037,P=0.358(NSD)

City Union

Bank Ltd. t(4)=-14.95,P=0.00(SD) t(4)=7.40,P=0.002(SD) t(4)=-13.64,P=0.00(SD)

D C B Bank Ltd.

t(4)=-6.7,P=-5.35(NSD) t(4)=0.288,P=0.003(SD) t(4)=-

0.546,P=0.614(NSD) Dhanalaxmi

Bank Ltd. t(4)=0.867,P=0.435(NSD) t(4)=1.67,P=0.169(SD) t(4)=1.037,P=0.358(NSD)

Federal Bank

Ltd. t(4)=0.69,P=0.874(NSD) t(4)=1.67,P=0.169(NSD) t(4)=0.438,P=0.684(NSD)

H D F C Bank t(4)=-4.16,P=0.014(SD) t(4)=3.38,P=0.028(SD) t(4)=2.19,P=0.094(NSD)


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Ltd.

I C I C I Bank

Ltd. t(4)=-1.16,P=0.309(NSD) t(4)=2.142,P=0.099(NSD) t(4)=1.255,P=0.278(NSD)

Indusind Bank

Ltd. t(4)=-4.5,P=0.010(SD) t(4)=4.46,P=0.011(SD) t(4)=0.056,P=0.958(NSD)

Jammu &

Kashmir Bank

Ltd.

t(4)=5.30,P=0.00(NSD) t(4)=0.889,P=0.424(NSD) t(4)=6.48,P=0.003(SD)

Karnataka Bank

Ltd. t(4)=-1.214,P=2.91(NSD) t(4)=13.061,P=0.038(SD) t(4)=1.318,P=0.258(SD)

Karur Vysya

Bank Ltd. t(4)=-1.96,P=0.121(NSD) t(4)=0.439,P=0.683(NSD) t(4)=2.28,P=0.084(NSD)

Kotak Mahindra

Bank t(4)=-0.71,P=0.516(NSD) t(4)=3.68,P=0.021(SD) t(4)=0.859,P=0.439(NSD)

Lakshmi Vilas

Bank Ltd. t(4)=3.022,P=0.039(NSD) t(4)=1.08,P=0.339(NSD) t(4)=2.9,P=0.040(SD)

Nainital Bank

Ltd. t(4)=-0.17,P=0.873(NSD) t(4)=2.107,P=0.103(NSD) t(4)=0.342,P=0.750(NSD)

R B L Bank Ltd. t(4)=3.01,P=0.040(SD) t(4)=-1.88,P=0.132(SD) t(4)=2.84,P=0.047(NSD) South Indian

Bank Ltd. t(4)=5.30, P=0.006(SD) t(4)=1.5,P=0.200(NSD) T(4)=5.67, P=0.005 (SD)

Yes Bank Ltd. t(4)=-3.312, P=0.03(SD) t(4)=5.19,P=0.007(SD) t(4)=1.88, P=0.133 (NSD)

Overall

t(85)=-1.490,

P=0.140(NSD)

t(85)=7.554, P=0.00(SD) t(85)=3.015, P=-.003(SD)

Source: Authors Calculations based on the data from appendix table A.57- A.60

The result of the paired T-test fails to accept the null hypothesis as there was statistical

difference in the City Union Bank with respect to all three ratios. This shows maintenance

of higher ratios in the pre and post implementation phase of Basel III by this bank.

However with respect to other four banks such as Federal bank ltd., ICICI bank ltd., Karur

Vysya bank Ltd. and Nainital bank ltd. test helps to accept the null hypothesis. This

similarity is on account of maintaining the same level of ratio in the post and pre-

implementation phase by these banks. In other words, these banks were following better

ratio in the pre implementation period of Basel III as compared to what was prescribed in

Basel III norms. Hence in the post implementation phase of Basel III norms, these same

banks could maintain the same level prescribed in Basel III norms.

The overall result of private banks indicates a similarity in the mean tier1 capital ratio in the

pre and post-implementation phase of Basel III norms. The insignificant difference

indicates that banks were maintaining the same level of tier I capital in the pre-and post-

implementation phase. In the case of the Tier II capital ratio, the overall paired T-test result

shows a significant mean difference in the pre and post-implementation period of Basel III

norms. This difference indicates that banks were not maintaining a proper level of Tier II


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capital. Similarly, the Capital Adequacy ratio of public sector banks shows a significant

mean difference in the pre and post-implementation period of Basel III norms. The

difference is significant due to maintenance of a higher level of Capital Adequacy Ratio

(CAR) in the pre-implementation phase.

7.6 Summary

The present study explored the Indian bank’s readiness for Basel III implementation

through assessment of factors influencing Basel III preparedness. More specifically, this

study measured the impact of these factors on Basel III preparedness. The results revealed

that 'Anticipated Benefit’ is the most important significant factor influencing positively in

the preparedness of Basel III. Public sector banks are ready to bear the cost of Basel III

implementations as it shows positive relations with the Basel III preparedness. Private

bank’s are worried about the ‘Anticipated cost’ factor of Basel III preparedness; hence it

shows a negative relationship with Basel III Preparedness. The result of the comparison of

these factors between public and private banks highlights that there was no significant

difference in the factors of Basel III preparedness between them.

Indian banks are well prepared to implement Basel III norms. This finding is supported by

the studies of Boora (2018) and Kapoor and Kaur (2016). It is also supported by the fact

that Indian banks have sufficient resources for the implementation of Basel III and there is

still scope for improving their resources with the aid of government and RBI, especially

concerning public sector banks. The availability of resources with Indian banks is

understood from the factor 'Anticipated Challenge'. This factor shows a significant positive

relationship with Basel III preparedness.

The study on the compliance of Basel III ratios attempts to find whether banks are able to

meet the minimum requirements of ratios prescribed by the RBI. The result of one sample

T-test shows that, private sector banks commenced disclosure towards complying with

minimum common tier I ratio in 2018 onwards, rather public sector banks started

compliance from 2014 onwards. In the case of the minimum common equity ratio, Tier 1

capital ratio and CAR the private, as well as the public banks, showed a higher compliance

compared to a limit prescribed by Basel III norms; further the private banks mean ratio is


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still higher than the public sector banks. Both the type of banks started complying to

leverage ratio from 2015 onwards and showed higher compliance compared to a limit

prescribed by Basel III norms, however, private banks mean ratio is still higher than the

public sector banks.

A present study also compared the Tier I, Tier II and Capital Adequacy ratio in the pre and

post-implementation phase of Basel III. The result of the paired sample T-test showed that

banks were maintaining sufficient capital in the pre-implementation phase of Basel III

norms.

The overall conclusion can be drawn that the Indian banking industry is in a comfortable

zone to meet and comply with Basel III norms. The Public sector banks, as well as private

banks, have complied with the Tier I, CAR, and leverage ratio at a much higher level than

the minimum requirements prescribed by RBI. On an average basis, both public and private

banks have CAR between 14 % - 16%, which signals that the implementation of Basel III

will not pose any difficulties for Indian banks. However, in the future, the implementation

of other norms of Basel III, such as capital buffer, advanced approaches will be a challenge

to the Indian banks.

Chapter VIII Summary and Conclusion

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Chapter VIII

Summary and Conclusion

8.1 Introduction

Banks play an important role in the financial system of every country. It offers prominent

and different services to consumers, small-medium enterprises, large corporate houses, and

government, which aid them in conducting their regular business smoothly. The banks also

play a fundamental role in the payment system as well as facilitating credit and economic

growth (Kaur and Kapoor 2016). The banking system affects the economic performance of

the different countries, as the failure of one bank has a spillover effect on the other banks

and throughout the world economy. Therefore a strong and resilient banking system is

needed for the growth and economic development of the nation.

The banking sector in an Indian economy is undergoing a process of change, driving them

to pose a higher credit risk. Therefore, there is a critical need for credit risk management in

this sector. The credit risk management includes the identification of determinants affecting

credit risk and measurements of risk using quantitative variables. Further, excessive credit

risk may lead to bank failure, demanding the prediction of bankruptcy using scoring

models. Moreover, the effective credit risk management is possible due to control by

authorities in terms of regulations and supervision. Considering this fact, the present study

covers credit risk management practices, bankruptcy models and banking regulations such

as Basel III norms.

The extensive literature review brought to the notice of the researcher certain gaps. Past

studies on determinants of risk management practices reveal the use of three or five

determinants, while other studies used six or seven determinants of Risk Management

Practices. These studies were conducted in different countries such as Pakistan, Kenya, and

the UAE. The determinants applicable in some context/nations may not apply to Indian

banks due to the difference in the economic background and the Central Bank's policies of

the country. Thus recognizing of exact determinants of risk management practices in the

Indian banking sector persists a research gap. Therefore present study follows a scientific


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procedure (Principal Component Analysis) for the identification of determinants rather than

determining them a priori. Similarly, studies are available covering the determinants of risk

management practices in general, but studies are very few on determinants of credit risk

management practices. Therefore, the present study focuses on the determinants of Credit

Risk Management Practices (CRMP).

Though a great deal of literature is available on credit risk measuring tools with other

parameters, studies measuring the credit risk based on lending ratios and financial ratios as

a parameter are lacking. Studies that investigate the difference in the lending and financial

ratios of public and private banks are also limited in nature. The novelty of the present

study highlights the trend analysis through cross-sectional and time-series data analysis, for

seeking the bank and the year respectively in which liquidity, profitability and leverage

position is poor or strong. The proxy to the variable used in the present study is different, as

it is used based on bankruptcy models used in this study. The relationship between liquidity

profitability and leverage has been studied in other countries, but no similar studies carried

out in the Indian banking sector. Considering this fact, the present study measures and

compares credit risk using lending and financial ratios.

Many researchers used one or two bankruptcy models in their studies, which may not give

relevant results, according to Kleinert (2014). Further, some studies have used a limited

sample of banks that may not give appropriate results, this directs to do a study on a larger

sample size. Though ample literature is available on bankruptcy prediction covering only

one sector of banking, either public or private sector, but the work on comparative analysis

on public and private banks on this count, are lacking. Ample literature is available

covering a data period of three to five years; however, literature needs to be developed

covering a data period of more than five years. Studies are available covering a data period

of 2001- 2012, studies on recent period left and need to be examined. In previous studies,

bankruptcy models were applied for non-banking companies, but very few cover the

application of bankruptcy models on banks. The original models may not be relevant in the

current economic environment; thus, literature needs to be developed on recalibrations of

the original model.


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The novel approach of present study focuses on using multiple models, using larger sample

size, applying the model in the service sector, covering a recent data period, recalibration of

models, seeking the accuracy of models, ranking banks based on model's score and

comparison of bankruptcy score between public and private banks.

Many studies are available on identifying the factors affecting the Basel II preparations,

those concerning Basel III preparations found to be lacking. Further, even though some of

the studies used six factors affecting Basel III preparedness, but some of these factors were

not significant towards Basel III preparedness. Therefore present study focuses on

identifying the significant factors affecting Basel III preparedness for banks in the Indian

context and extended to comparing between public and private banks.

Previous studies examined the challenges of Basel III norms, whereas literature is limited

on compliance of Basel III norms as per the requirements of RBI in the Indian banking

sector. Though some studies do available on comparing the CAR, Tier I and Tier II capital

ratio before and after the adoption of Basel II Norms, but the same concerning Basel III

norms left to be examined.

Based on these research gaps, the present study entitled "Credit Risk Management

Practices: A Comparative Study of Public and Private Banks in India" is undertaken to

identify the factors (determinants) affecting credit risk management practices, measure its

individual impact and compare them between public and private banks. Further, a study

measure and compare the credit risk management practices of public and private banks

using lending ratio and financial ratios. Also, this study analyses the risk of bankruptcy of

public and private banks using the bankruptcy models, judge their accuracy and rank the

banks based on its score. Lastly, the study assesses the preparedness and compliance of the

Basel III accord of public and private banks in India.

The data with respect to credit risk determinants was collected through primary sources.

The collected data was tested through content validity and reliability test. The identification

of determinants was made using principal component analysis. The data on determinants of

credit risk management practices are described in terms of mean, median, standard

deviation, kurtosis, skewness, range, minimum and maximum. Pearson’s Correlation


196

analysis was conducted on the determinants to check the multicollinearity. The ordinary

least square regression is run to determine the statistical impact of the determinants on the

credit risk management practices of banks. Finally, parametric test such as independent

sample t-test was used to compare the difference in the determinants of credit risk

management practices of public and private banks.

The data on lending and financial ratios were collected using a secondary source. Eight

lending ratios and nine financial ratios were used to measure credit risk. This study used

independent sample t-test and ANOVA on the lending ratios to find a significant difference

between and amongst public and private banks. Further post hoc test was conducted to find

the bank, which reveals the statistical significant difference in the group. ‘Financial Ratios’

are used to measure the liquidity, profitability, and leverage position of the public and

private banks in India. It used independent sample T-test and Mann-Whitney U test to

compare ‘Financial Ratios’ between public and private banks in India. Further, Pearson’s

correlation coefficient is used to find the relationship between liquidity, profitability, and

leverage.

The data required to compute the bankruptcy score was collected from secondary sources.

Four models, such as Altman Z-score, Springate, Grover, and Zmijewski's model, were

used to assess the Indian public and private bank’s bankruptcy. The accuracy of this

bankruptcy model was found out by detecting Type I and Type II errors. Multiple linear

regressions were used to recalibrate the bankruptcy models. The robust test is conducted to

measure the accuracy between recalibrated and the original model. The robust test was

conducted using profitability and asset quality as a variable. Finally, an Independent sample

T-test is used to compare the bankruptcy score of public and private banks in India.

The data relating to the preparedness of Basel III norms were collected from the primary

source and compliance of Basel III accord from the secondary source. Multiple linear

regressions were used to measure the impact of factors on Basel III preparedness.

Independent sample T-test is used to compare the Basel III preparedness between public

and private banks. For the assessment of compliance of Basel III accord, the one-sample t-

test is used to compares the implemented Basel III requirement ratios with minimum

requirements of ratios as prescribed by RBI. Paired T-test is run to determine whether there


197

was a statistically significant mean difference in the mean of tier I cap ratio, tier II cap

ratio, and the capital adequacy ratio in the pre-implementation and post-implementation

phase of Basel III and further between public and private banks using independent sample

t-test.

8.2 Summary of Major Findings

The Summary of findings consists of findings from the determinants of credit risk

management practices, measurement credit risk management practices, application and

recalibration of models and lastly, preparations and compliance of Basel III norms.

8.2.1 Findings from the Determinants of Credit Risk Management

Practices

The first objective of this study is to identify the factors (determinants) affecting credit risk

management practices, measure its individual impact and compare them between public

and private banks. In the identification and classification of the determinants of CRMP, the

results of the principal component analysis show the retention of four components as the

credit risk determinants of CRMP. In measuring the impact of the credit risk determinants

on their respective CRMP for public and private banks, regression results reveal that

explanatory variable Credit Risk Understanding and Credit Risk Assessment and Analysis

(CRAA) among the four components are most influential in the contribution of CRMP of

private banks. The regression model fits the data well for the private banks compared to

public banks in India and the results for private banks are consistent with previous studies

like that by Al-Tamimi and Al-Mazrooei (2007), and Hussain and Al-Ajmi (2012).

In comparing the credit risk determinants of Credit Risk Management Practices (CRMP)

between the public and the private banks in India, the results of the Independent sample t-

test shows a significant difference between public and private banks. Considering this fact,

it may be concluded that there is a difference in the CRMP between public and private

banks. These contradictory results between the public and the private banks violate the

assumption of uniformity of the Institutional theory.


198

8.2.2 Findings from the Measurement of Credit Risk Management

Practices

The second objective of the present study is to measure and compare the credit risk

management practices of public and private banks using lending and financial ratios. In

measuring the CRMP using lending ratios, it is observed that the CAR and ROA are higher

for private banks, indicating a better profitability position and credit risk management

strategy of these banks. The credit deposit ratio of both the banks has crossed the margin of

70 percent, indicating low liquidity and increased dependency on deposits for lending. The

lending ratios such as, ‘Gross NPA to Gross Advance ratio’ and ‘Total Loan to Total

Deposit ratio’ is high in the case of public banks. Given this, the public sector banks are

facing an unfavourable scenario of lending ratios compared to private banks and these

findings are in line with Thiagarajan et al. (2011) and Kattel (2016).

In measuring the CRMP using financial ratios, the result states that both public and private

banks experience low liquidity ratios in the past. This trend has improved in the recent two

to three years. In the case of the leverage ratio, the result shows a stable leverage position

of public sector banks and fluctuating trends in the case of private banks. Similarly, the

leverage ratios are lower for public banks and higher for private banks. The findings of the

profitability ratios of private banks show a fluctuating trend, and public banks show a

decreasing trend. These findings of profitability ratios are in line with Thakarshibai (2014),

Chintala (2016), Balaji and Kumar (2016), Patel and Bhanushali (2017) Katti and Vadrale

(2018). The result of correlation analysis shows an inverse relationship between liquidity

and profitability and no relationship between profitability and leverage and also between

liquidity and leverage.

In comparing the lending and financial variables between public and private banks, findings

reveal that there is a statistical significant difference in the majority of lending ratios. This

means that there is a significant difference in the credit risk management practices of public

and private banks. The financial variables results show a significant difference in the

profitability and leverage position of public and private banks; however, the same test does

not shows a significant difference in the liquidity position of a public and private bank.


199

8.2.3 Findings from the Application and Recalibration of the Bankruptcy

Model

On analysing the risk of bankruptcy of public and private banks, by applying bankruptcy

models such as Altman Z-score, Springate, Grover, and Zmijewski's in the original form, it

is found that the results were not appropriate. The review of critics and the error rates

present in the applied models triggered the need for recalibrations of models. The results of

the recalibrated Altman Z-score model and recalibrated Grover model showed an improved

accuracy of predicting business failure, while the performance of the original Springate

model and the original Zmijewski model outperformed the recalibrated model.

The final credit-risk efficiency ranks to the banks were based on the bankruptcy scores of

four models such as recalibrated Altman Z-score model, original Springate model, and

original Zmijewski model and the recalibrated Grover model. These four models are

selected based on their model accuracy rates. The ranking results for the public sector

banks show that Bank of Baroda is at the first position (most efficient and credit-risk safe)

and Allahabad bank is at the second position, while the IDBI and Vijaya Bank is at the last

position in terms of their performance. Similarly, in the case of private banks, Nainital

Bank is at the first position, HDFC is at the second position, and DCB and Karnataka Bank

Limited are at the last position. In case of comparison of bankruptcy score between public

and private banks using original and recalibrated models, the result of the independent

sample t-test shows a significant difference. This depicts a difference in the profitability

and leverage ratios of the public and private banks as these ratios are used to arrive at a

bankruptcy score. Considering the above facts, it may be concluded that there is a

difference in the CRMP between public and private banks.

8.2.4 Findings from the Preparations and Compliance of Basel III norms

In measuring the statistical impact of factors such as Anticipated Benefit, Anticipated Cost,

Anticipated Impact and Expected Challenges on the Basel III Preparedness for public and

private banks in India, the results show that the factor ‘Anticipated Benefit’ is the most


200

crucial factor followed by ‘Anticipated Impact’ in Basel III preparedness. Similar results

were arrived by Al-Tamimi and Al Mazoorie (2015) and Boora and Jangra (2018). The

public sector banks are found to be ready to bear the cost of Basel III implementations as it

shows a positive relationship with the Basel III preparedness, while private banks show a

negative relationship with Basel III preparedness. Similar results were arrived by Kaur and

Kapoor (2016). The results of private banks with respect to the 'Anticipated Cost' factor

support the Legal Theory of Finance. In comparing the factors of Basel III preparedness

between public banks and private banks in India, the result states that there is no difference

between them in their preparedness for the implementation of Basel III norms.

In case of compliance of actual Basel III ratios of banks with the minimum ratio prescribed

by RBI, the result of one-sample t-test shows that private sector banks commenced

disclosure towards compliance with respect to minimum common equity Tier I ratio in

2018 onwards, while the public sector banks started compliance from 2014 onwards. In the

case of the Tier I capital ratio and capital adequacy ratio, private as well as public banks

showed higher compliance compared to a limit prescribed by Basel III norms. The private

banks mean ratio is still higher than the public sector banks even though both the type of

banks started complying with the leverage ratio from 2015 onwards, and showed higher

compliance compared to a limit prescribed by RBI in Basel III norms.

The comparison of Tier I, Tier II and the Capital Adequacy Ratio reveals that there was a

significant difference in the above ratios between public and private banks in the pre and

post-implementation phase of Basel III. The result of the paired sample t-test shows that

both types of banks maintained sufficient capital in the pre-implementation phase of Basel

III norms and that the capital maintained by private banks is much higher than the public

banks.


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8.3 Conclusion

Based on the findings, a broad conclusion can be drawn that private banks are generally

more efficient in terms of following the credit risk management practices. The impact of

the credit risk determinants on CRMP reveals a more influential effect in the contribution

of CRMP of private banks and also the regression model fits the data well for the private

banks. The lending ratios show a better financial position of private banks in terms of the

low NPA to Advance ratio, Loan to Deposit ratio and high CAR and ROA. Similarly, the

findings of the profitability ratios of private banks show a fluctuating trend, and public

banks show a decreasing trend.

The bankruptcy study concludes that the recalibrated Altman and the Grover model

perform better than the original model. In contrast, the original Springate and the

Zmijewski model showed improved accuracy over the recalibrated model. The factors of

Basel III preparations show that ‘Anticipated Benefit’ is the most significant and

motivating factor for the banks for Basel III preparations. Thus it can be concluded that

Indian banks are well prepared to implement Basel III norms and the Basel III ratios are

maintained by the public and private banks are at a much higher level than prescribed by

RBI. This also shows that the Indian banking industry is in a comfortable zone to meet and

comply with Basel III norms. The private banks with enhanced Tier I capital ratio and

higher CAR, along with the advanced financial skill of personnel, are well placed to

comply with Basel III norms. However, public sector banks may face challenges for the

implementation of Basel III norms as the capital ratios of these banks are lower than the

private banks. According to Barua et al. (2016), raising capital from the equity market

would be difficult for public sector units because of the discounted prices of shares and

other structural issues.

Finally, it can be concluded that there is a difference in the credit risk management

practices between public and private banks, due to differences in the credit risk

determinants, the difference in the lending ratios, the difference in the leverage and

profitability ratios and difference in the bankruptcy scores.


202

8.4 Recommendations

1. The findings lead to an important suggestion for the banks to improve their Credit

Risk Management Practices (CRMP). Each bank needs to have a well-crafted policy

(policy should be suitable for the market it operates in), procedure, and manual,

which are tailored for the individual bank by qualified and experienced officers. The

banks should improve upon their loan monitoring, as loan defaults could be

minimized through regular monitoring and supervision of credit granted to the

customer. The credit risk strategy of banks should provide continuity in approach

and need to take into account the cyclical aspects of the economy. The banks also

need to have in place comprehensive risk management and reporting process to

identify measure, monitor, and report credit risk in the spirit of study by Rehman

(2016). Indian banks should take into consideration that the cost associated with the

failure of banks is much higher, hence they should think about their survival under

regulatory regimes by revising their strategies and credit policy, improving

profitability, controlling cost, protecting the asset quality and capital budgeting, etc.

In a nutshell, banks should strengthen their risk management system for maintaining

a strong portfolio.

2. Every bank should look into its profitability liquidity and the leverage position

before sanctioning of the heavy loan, due to the occurrence of a lot of frauds and

scams in recent years. A successful banker will always develop harmony between

liquidity and profitability. A study of liquidity, profitability and leverage position is

not only helpful for bankers alone to frame strategies, but it also aids investors and

regulators. Therefore it is expected from banks to improve the knowledge of

financial management and contribute to the development of the financial activity of

banks by dealing with the problem of liquidity, profitability, and leverage.

3. The findings present vital suggestions to public sector banks. All the lending ratios

present an unfavourable scenario, especially the ROA of public banks has worsened

over thirteen years, and individual banks have negative returns, too as per the

present study. Therefore these banks need to take precautionary measures to


203

discontinue the increasing trend of NPA. These banks need to focus on profitability

for financial stability, monitor NPA and ROA to avoid upcoming financial distress

carefully, and underused or unutilized assets must be considered for diversification

purposes. Further, these banks must adopt a professionalized management scenario

and branch wise, product wise and employee wise targets must be fixed to ensure

adequate performance.

4. The study has recommendations for the RBI to revise its policies and updates its

guidelines to respond to the changing environment. The NPA ratio shows a rising

trend in the year 2009 ( 2.31%), 2013 (4.27%) and 2018 (9.1%) as per the RBI data.

It is found that the existing monitoring procedures, such as internal audits and

concurrent audits, are not sufficient to verify the functioning of the NPA

mechanism. Further, the RBI till today follows a CAMEL model to give its opinion

about the bank’s merger. Although the CAMEL model is a good indicator to

highlights the bank’s health, however, it has its limitations. Hence it is suggested

that the RBI should make a policy of using recent models such as MDA technique,

logit, Probit models along with the CAMEL model for the financial health

assessment of the banks.

5. The findings also present relevant suggestions to the government. The study shows

a significant difference in the mean bankruptcy score of public and private banks.

This difference is on account of the liquidity, profitability and leverage ratios used

to arrive at a score. The financial position of public banks is weak compared to

private banks. According to Balaji and Kumar (2016), the rate of growth of

profitability is higher for private banks. Also, the CAMEL study conducted by

Khan (2018) highlights the fact that the performance of private banks is better than

public banks. This could be on account of also suggests excessive government

interference and control of public sector banks, whereby they inspite of poor

liquidity and profitability face the challenge of implementing government schemes.

Hence the government should look into the financial position of the banks and


204

suitably should decide to allow the implementation of schemes for the respective

banks.

6. In the future, the bankruptcy models should include macroeconomic variables. The

accounting ratios used in the presently used models is poor predictors. The market-

driven variables such as past stock returns, high exchange rate, and interest rate are

strongly related to bankruptcy probability, which are neglected in these currently

adhered models. Thus combining these market-driven variables with some

accounting ratios may result in better development of these models.

7. The study presents a suggestion to the prospective researcher. The researcher

should understand the limitations of the model, such as missing data bias, period

bias, distress indicator choice bias, industry range bias and a sample bias of each

model before application. He should understand that each model has been derived

using a different estimation technique, different sample sets, in a different period

using different distress indicators. Every statistical technique calibrates a model that

is based upon a set of assumptions about the data. Violations of these underlying

assumptions may cause improper estimation of the model coefficient. Therefore, the

researcher should first conduct some preliminary data analysis to determine the

statistical properties of the predictor variables or should transform the data to

comply with the assumptions of a particular technique.

8. The banks need to consider Basel III as a catalyst to strengthen risk management

issues. Stringent compliance to Basel III norms will allow banks to show a better

position to the stakeholders and will act as a competitive advantage to enable them

to take the benefit of future opportunities. This will reduce the risk-taking behaviour

of the banks and will make the banking system more transparent. Finally, effective

implementation of Basel III norms demands a strong need for increased co-

operation and co-ordination among employees.


205

9. Although government interference drives the public banks to face more credit risk,

it also gives protection to public deposits in case the bank becomes bankrupt. This

may not be the case for private banks. Therefore private banks should be more

cautious about their bankruptcy scores. Also, the Reserve Bank of India must ensure

that the private sector follows strict adherence towards Social performance.

10. The Basel Committee on Banking Supervision (BCBS) plays a supervisory role in

Basel III implementation. BCBS work is not over merely framing the regulations,

but a follow-up or supervisory review reports need to be prepared from time to time

for the effective implementation and supervision of Basel III. Also, regulators and

bankers should organise timely seminars and workshops to train and upgrade the

skills of bank staff about risk management and revisions in the Basel Accord.

Further, implementation of Basel regulatory requirements involves huge investment

and many banks are not able to comply with these requirements due to a shortage of

capital. The regulators should also provide financial and technical assistance to

banks on important issues as and when required (Kaur and Kapoor, 2016).

11. Adequacy of capital is a vital indicator for deciding the strength and soundness of

the banking system. Therefore, Indian banks have to plan for more capital in the

years to come. Although Indian banks have sufficient resources for the

implementation of Basel III, there is still scope for improving their resources with

the aid of government and RBI, especially concerning public sector banks.

Moreover, the capital ratios of the public sector bank are lower than private banks;

hence the government could consider increasing its stake in the public sector banks.

8.5 Implications of the study

The present study makes valuable managerial contributions and theoretical contributions.

8.5.1 Managerial Implications

The present study will be of great use to the individual banks, bank regulators, government,

investors, potential investors, depositors, auditors, communities, employees and the other

stakeholders of banks.


206

A present study contributes to the credit risk management practices of public and private

banks in many ways. The findings of the study will help to improve the CRMP of banks

operating in India. This will build confidence in the minds of stakeholders and market

participants. Improved CRMP in banks will enhance their reputation and strengthen their

ratings and improve their profitability. The present study points out the weaknesses of

public sector banks in managing credit risk. Therefore, this study will help those banks to

improve their understanding of credit risk, techniques of identification of credit risk, the

process for credit risk assessment and the procedure for monitoring and controlling credit

risk. The research findings shall aid the regulatory bodies of the government in improving

existing policies and enforcement of the same to facilitate full implementation of policies.

A study of liquidity, profitability, and leverage gives a clear scenario to the bank about its

financial position, which will help the banker to improve its policies and strategies towards

credit risk management and helps the regulator to put control measures on banks. A study

on financial ratios aids potential investors. A potential investor will look into liquidity and

profitability ratios and can become a significant factor for them for investment decisions. It

will also assist the investors and creditors in predicting the earning capacity of the investing

company.

The bankruptcy scores study may help the depositors in a different context. A large deposit

holder (a holder whose deposit exceeds 1 lakh) does not have any protection under the

Deposit Insurance and Credit Guarantee Corporation Act, 1961. This deposit holder on the

precautionary side could look at the bankruptcy scores and take investment and divestment

decisions accordingly.

Predictions of bankruptcy help in the growth of the banking industry since the banking

industry is a significant contributor to the economic development of the country. Many

banks enter financial distress positions for different reasons. This may cause thousands of

employees to lose their jobs, a large number of investors to lose their savings, communities

will lose vital services, and the economy of the country will suffer. Much of these losses


207

can be avoided with predictions of the bankruptcy system, which allows adequate plans and

take contingency measures.

The study contributes to the policy-making decisions for investors, merchant bankers and

mutual funds, as they can assess the financial performance looking at the bankruptcy scores

developed by different models.

Indian banks should take into consideration that the implementation of Basel III

requirements will be an opportunity for the banks to enrich their risk management culture

and enhance their ability to serve the financial needs of the economy. Compliance with

Basel III norms will act as a competitive advantage to enable them to take the benefit of

future opportunities. It will make the banking system more transparent and ensure fair

disclosure requirements and will reduce the risk-taking behavior of the banks.

The relationship of Basel III preparedness with the dimensions of Basel III will help

bankers to identify cost and challenges they need to face for Basel III preparations and will

undertake strategic steps to deal with it. Effective implementation of Basel III would aid

banks to survive and grow in a new risk-sensitive environment. Finally, the literature on

capital and risk will help bankers to undertake precautionary steps to manage risk and

capital prudently.

8.5.2 Theoretical contributions

The present study makes significant contributions to the literature as it could act as a source

of reference for other researchers. The findings of the study may stimulate future research,

and will also act as a good background for further research. The study contributes to the

literature in terms of the determination of credit risk determinants of CRMP using the

Principal Component Analysis (PCA) technique and recalibration of bankruptcy models.

It is found that there is a difference in credit risk determinants (CRU, CRAA and CRMC)

of CRMP between public and private banks. The Institutional theory claims that the


208

homogeneity of the regulatory pressures, norms, and practices brings uniformity in the

credit risk determinants of all the banks. The uniformity assumption of this theory does not

apply in our study on account of difference in the understanding among the employees on

the different aspects of credit risk and in the assessment and monitoring and control

policies of credit risk followed by the public and private banks in India.

The private banks show a negative relationship of factor, ‘Anticipated Cost’ with Basel III

preparedness. These results support the Legal Theory of Finance. It is observed from the

theory that, if the stringent implementation of laws puts the financial institution in a dire

situation of survival, then the suspension of law is a priority for the survival of the

institution. Thus as per this theory, if the cost of enforcement of norms exceeds the benefit

it gains, then the cutting down of these norms acts better for the survival of the private

banks.

8.6 Limitations of the study

1. A genuine limitation of the present study is the subjectivity of the respondent’s

response, and difficulty to pinpoint the sources of error in the provided ratings.

Some of the potential errors could be due to individual factors like lack of

motivation, fatigue, and lack of clarity of the rating task.

2. A limited set of variables and ratios has been investigated in the current study due to

the non-accessibility of some data from published reports, documents, and websites

of selected banks, CMIE, and other databases.

3. There are many models available for bank failure predictions such as Standard and

Moody’s financial ratios, Beaver model, Altman Z- score model, Ohlson’s model

CAMEL model, Grover Model, Springate Model, Neural Network and Zmijewskis

Model to predict the financial health of the bank. Among many techniques available

for evaluating the financial performance of banks, a literature survey shows that the

majority of international failure prediction studies employ the Altman-Z score


209

model, Grover model, Springate model, and Zmijewski model. Therefore the study

is limited towards the use of these four models.

4. The current study covers the readiness of Basel III norms by the Indian banks after

the implementation of norms. This study could have been more meaningful and

useful if the readiness of norms could have been conducted before the

implementation of Basel III norms.

5. The present study used four capital ratios, such as the Minimum Common Equity

Tier 1 Capital ratio, Minimum Tier 1 Capital, Tier II capital, Capital Adequacy

Ratio (CAR) and Leverage ratio. The other ratios, such as Liquidity ratio and Net

Stable Funding ratio is not covered in this study due to lack of data about the same

in the annual statements.

8.7 Directions for Future Research

1. In the present study, the results of the principal component analysis show the

retention of four components as the risk determinants of credit risk management

practices. These four components do not present a good fit for the model in the case

of the public sector banks. As a follow-up, the study can be extended to cover some

more statements and find more risk determinants.

2. The present research has limited its questionnaire only to assess the relationship

between credit risk management practices and different determinants of credit risk

such as credit risk understanding, credit risk identification, credit risk assessment and

analysis, credit risk monitoring and controlling, However, some other aspects such

as identifying determinants of market risk, liquidity risk, and operational risk could

be investigated as well in further studies.

3. Based on the findings, the suggestions for subsequent research are to test a

bankruptcy position using data from non-banking companies. A further suggestion is

to investigate the model's performance in different environments and periods.


210

4. The present study has focused on only one pillar that is capital adequacy, and the

review of the other two essential pillars, i.e., supervisory review process and market

discipline have not been covered. Therefore further studies can cover the compliance

of Basel III norms concerning the other two pillars.

211

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223

APPENDIX A.1 Questionnaire- Content Validity

Kindly give your response on a four point ordinal scale

1 2 3 4

For

Relevancy

Not relevant Somewhat relevant and need

some revision

Quite relevant but needs

minor revision

Very Relevant

For Clarity Not relevant Somewhat relevant and need

some revision


minor revision

Very Relevant

For

Simplicity

Not relevant Somewhat relevant and need

some revision


minor revision

Very Relevant

Following are the objectives of my study pertaining to primary data

1) To identify the factors (determinants) affecting credit risk management practices, measure its

individual impact and compare them between public and private banks.

2) To assess the preparedness of the Basel III accord of public and private banks in India.

Please give your details

Name: _______________________________________________________________________________

Designation_______________________Organisation:________________________________________

PART A: General Information

1) Name of the Bank ---------------------------------------------

2) Name of the Respondent ----------------------------------

3) Designation please specify--------

4) Total work experience in banking Industry

0 to 5 years 5 to 10 years 10 to 15 years over 15 years

5) Banking Type: Nationalized Bank Private Bank

PART B: Credit Risk Management

1. Which is the main type of risk faced by your bank?

Please give a rank from 1 to 5: 1 = Most and 5= least

a) Credit risk ( )

b) Operational risk ( )

c) Market Risk ( )

d) Exchange rate risk ( )

e) Liquidity risk ( )

Sr.

No.

Statements Relevance Clarity Simplicity

1 2 3 4 1 2 3 4 1 2 3 4

1 Responsibility for credit risk management is clearly

set out and understood throughout the bank

A) Relevance B) Clarity C) Simplicity

1 2 3 4 1 2 3 4 1 2 3 4

224

2 Your bank analyses credit risks by prioritizing it and

selecting those that need active management.

3 Accountability for credit risk management is clearly


4 There is a common understanding about the bank of

advanced credit risk management techniques

5

Applications of credit risk management techniques

reduce costs or expected losses is clearly understood

by the staff of banks

6

Your bank has adopted a standard reporting system

about the credit risk management from bottom to top

management

7 Stress testing output is understood by senior

management and board

8

The bank carries out a comprehensive and systematic

procedure for identification of credit risk

9 The bank finds it difficult to identify, and classify its

main credit risks

10

The borrower’s business performance is regularly

observed by your bank following the extension of

financing

11

There is a common understanding that managing

credit risk is important to the performance and

success of the bank

12 Changes in credit risk are recognized and identified

easily by your bank

13 Your banks provides well defined training to the staff

for credit risk assessment and analysis

14 My bank assesses the likelihood of credit risk

occurring

15 Your bank assesses the credit risks using quantitative

analysis methods

16 There is committee at the head office and branch

level responsible for identifying credit risk

17

Your bank analyses the credit risks by assessment of

the costs and benefits of addressing credit risks

18

Monitoring the effectiveness of credit risk

management is an integral part of routine

management reporting

19

It is crucial for your bank to apply the most

sophisticated technique for credit risk identification

20 Credit risk management techniques are used for

regulatory purposes only

21 Level of control by the bank is appropriate for the

credit risk that it faces

22

There is a common understanding about the formal

credit risk management system in your bank

23 Your bank has a computer based support system for

credit risk assessment.

225

24 Your bank effectively monitors the credit limit of

everyone counterparty

25

The bank is aware of the strengths and weaknesses of

the credit risk management systems of other banks

26 Your bank follows standardized approach for credit

risk assessment and analysis

27

There is a common understanding about the

evaluation techniques used in credit risk management

28 Credit risk management is given great importance by

my bank

29 The internal auditor of your bank report it to

supervisory board or Board of Director

30 It is too risky to invest my bank’s funds in one

specific sector of the economy

31 Branch manager identifies risk based on financial

statement analysis and inspection

PART C: BASEL III PREPAREDNESS

This part deals with a set of statements relating to Basel III norms to be implemented in India in a

phased manner beginning from January 2013 till 2019.

1) Please indicate your level of for your bank about the perceived benefits of Basel III for each of the

statement on a scale from strongly agree to strongly disagree.

Sr.

No.

Statements Relevancy Clarity Simplicity

1 2 3 4 1 2 3 4 1 2 3 4

1 Better quality of capital improves loss absorption

capacity of banks

2 Basel III ensures better liquidity risk management

with increase in short term liquidity coverage

3 Market disclosures are more detailed and

transparent under Basel III

4 Basel III provides counter cyclical mechanism

(prevent enlargement of business cycles for banks)

5 Risk of excessive leverage is reduced through

introduction of leverage ratio under Basel III

2) Please indicate your level of for your bank about anticipated cost of Basel III implementation for each of

the statement on a scale from strongly agree to strongly disagree.

Sr.

No.


1 2 3 4 1 2 3 4 1 2 3 4

1 Substantial outlay is involved in data acquisition,

software and hardware development for Basel III

implementation

2 Expenditure on recruitment and training of personnel

required for Basel III implementation has increased

226

3 Cost of complying with multiple regulators and

disclosure requirements has increased under Basel III

4 There is increase in cost of raising additional capital to

meet Basel III requirements

5 Banks have to incur cost of hiring consultants having

experience in quantifying risk

6 Risk management model outsourcing for Basel III

involves huge expenditure

3) Please indicate your level of for your bank about the impact of Basel III implementation for each of the

statement on a scale from strongly agrees to strongly disagree.

Sr.

No.


1 2 3 4 1 2 3 4 1 2 3 4

1

Basel III will provide better foundation for future

development in risk management and reduction in

risk of banking crisis

2 Pressure on Indian banks will increase to raise

additional capital to meet new requirements

3

Basel III regulation may lead to reduction in pro-

cyclical (i.e. enlargement of business cycles)

behaviour of banks

4

Basel III implementation will put significant

pressure on banks’ profitability & Return on equity

5

There is significant increase in risk weighted assets

for different categories of risk under Basel III

6

Basel III implementation may increase dominance

by large institutions with well developed

infrastructure

7 There will be decrease in investors’ return with

Basel III adoption

8

Significant increase in capital and liquidity

requirements under Basel III may lead to reduced

lending capacity of banks

4) Please indicate your level of for your bank about the challenges of Basel III implementation for each of

the statement on a scale from strongly agrees to strongly disagree.

Sr.

No.


1 2 3 4 1 2 3 4 1 2 3 4

1 There is increase in risk weighted assets

calculations under Basel III

2

My Bank will have to design comprehensive

liquidity management framework and information

technology for Basel III implementation

3

Bank have to achieve closer integration of finance

and risk management functions to properly

implement Basel III

4 Maintaining data integrity within the bank is a

tough task while implementing Basel III

227

5 Banks have to recruit and retain skilled staff for risk

management to meet Basel III requirements

5) Please indicate level of your bank about preparedness in Basel III implementation for each of the

statement on a scale from strongly agree to strongly disagree

Sr.

No.


1 2 3 4 1 2 3 4 1 2 3 4

1 High priority is attributed for implementation of

Basel III by the management of my bank

2 There is availability of competent human resources

in my bank to implement Basel III

3 There is familiarity and awareness among my

bank’s staff regarding application of Basel III

4 My bank has access to up-to-date technologies for

Basel III implementation

A.2 Questionnaire in the Pre –PCA stage

This questionnaire is designed to obtain information regarding various parameters of Risk Management

Practices. Thirty one aspects are given below. Based on your experiences as a banking official, please indicate

your level of agreement or disagreement with each of the statements on the Scale from Strongly Agree to

Strongly Disagree, by ticking (√)

Sr.

No.

Statements Strongly

Agree

Agree Neutral Disagree Strongly

Disagree

1 Responsibility for credit risk management is clearly


2 Your bank analyses credit risks by prioritizing it

and selecting those that need active management.

3 Accountability for credit risk management is clearly


4 There is a common understanding about the bank of

advanced credit risk management techniques

5

Applications of credit risk management techniques

reduce costs or expected losses is clearly understood

by the staff of banks

6

Your bank has adopted a standard reporting system

about the credit risk management from bottom to

top management

7 Stress testing output is understood by senior

management and board

8 The bank carries out a comprehensive and

systematic procedure for identification of credit risk

9 The bank finds it difficult to identify, and classify

its main credit risks

10

The borrower’s business performance is regularly

observed by your bank following the extension of

financing

11

There is a common understanding that managing

credit risk is important to the performance and

success of the bank

12 Changes in credit risk are recognized and identified

easily by your bank

228

13 Your banks provides well defined training to the

staff for credit risk assessment and analysis

14 My bank assesses the likelihood of credit risk

occurring

15 Your bank assesses the credit risks using

quantitative analysis methods

16 There is committee at the head office and branch


17 Your bank analyses the credit risks by assessment

of the costs and benefits of addressing credit risks

18

Monitoring the effectiveness of credit risk

management is an integral part of routine

management reporting

19 It is crucial for your bank to apply the most

sophisticated technique for credit risk identification

20 Credit risk management techniques are used for

regulatory purposes only

21 Level of control by the bank is appropriate for the

credit risk that it faces

22 There is a common understanding about the formal

credit risk management system in your bank



24 Your bank effectively monitors the credit limit of

everyone counterparty

25

the bank is aware of the strengths and weaknesses

of the credit risk management systems of other

banks



27 There is a common understanding about the

evaluation techniques used in crdit risk management

28 Credit risk management is given great importance

by my bank



30 It is too risky to invest my bank’s funds in one

specific sector of the economy

31 Branch manager identifies risk based on financial

statement analysis and inspection

A.3 Questionnaire in the Post PCA stage

This questionnaire is designed to obtain information regarding various determinants of Risk Management

Practices. Thirty one aspects are given below. Based on your experiences as a banking official, please indicate

your level of agreement or disagreement with each of the statements on the Scale from Strongly Agree to

strongly disagree, by ticking (√)

a) Credit Risk Understanding: There is a common understanding among the employees of bank about the

following aspects

Sr.

No.

Statements Strongly

Agree


Disagree

1 Formal credit risk management system in your

229

bank

2 Importance of managing credit risk for the better

performance and success

3 The banks of advanced risk management techniques

4 Credit risk evaluation techniques used by your bank

5

Responsibility for credit risk management is clearly

set out and understood throughout the branch

employees.

b) Credit Risk Identification

Sr.

No.

Statements Strongly

Agree


Disagree

1

Your bank carries out comprehensive and

systematic procedure for identification of credit

risk

2

There is a committee at head office and at branch


3 Changes in credit risks are recognized and

identified easily by your bank

4 Branch managers identifies credit risk based on

financial statement analysis and inspection

5

It is crucial for your bank to apply the most

sophisticated techniques for credit risk

identification

c) Credit Risk Assessment and Analysis

Sr.

No.

Statements Strongly

Agree


Disagree



2 Your bank assesses the credit risks using

quantitative analysis methods

3

Your bank analyses the credit risks by assessment

of the costs and benefits of addressing risks

4 Your bank analyses credit risks by prioritizing it

and selecting those that need active management

5 Your banks provides well defined training to the

staff for credit risk assessment and analysis



d) Credit Risk Monitoring and Controlling

Sr.

No.

Statements Strongly

Agree


Disagree

1 The level of control by your bank is appropriate

for the credit risks that it faces

2

For your bank monitoring the effectiveness of

credit risk management is an integral part of

banks function

3

Your bank has adopted a standard reporting

system about the credit risk management from

bottom to top management

230

4 Your bank effectively monitors the credit limit

of everyone counterparty

5

The borrower’s business performance is

regularly observed by your bank following the

extension of financing



e) Credit Risk Management Practices

Sr.

No.

Statements Strongly

Agree


Disagree

1 Your bank has an appropriate credit risk

management department

2 Your bank makes Credit risk management policy

periodically

3

Your bank’s credit risk management procedures

and processes are documented and provide

guidance to staff about managing risks

4 Efficient credit risk management is one of the

bank’s objectives

5 Your bank emphasizes the recruitment of highly

qualified people in credit risk management

6

The application of Basel capital Accord has

improved the credit risk management

effectiveness in your bank

Table A.4: Gross NPA to Gross Advances of 21 Public Banks for the Thirteen years 2005-2017

Sr.

No. Public Banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Allahabad Bank 5.80 3.94 2.61 2.01 1.81 1.71 1.80 1.91 3.92 5.73 5.46 9.76 13.09 4.58

2 Andhra Bank 2.46 1.94 1.41 1.08 0.83 0.86 1.38 2.12 3.71 5.29 5.31 8.39 12.25 3.62

3 Bank Of Baroda 7.30 3.90 2.47 1.84 1.27 1.64 1.62 1.89 2.40 2.94 3.72 9.99 10.46 3.96

4 Bank Of India 5.48 3.72 2.42 1.68 1.71 3.31 2.64 2.91 2.99 3.15 5.39 13.07 13.32 4.75

5 Bank Of

Maharashtra 7.00 5.53 3.50 2.57 2.29 2.96 2.47 2.28 1.49 3.16 6.33 9.34 16.93 5.07

6 Canara Bank 3.89 2.25 1.51 1.32 1.56 1.53 1.47 1.75 2.57 2.49 3.89 9.40 9.63 3.33

7 Central Bank Of

India 9.01 6.85 4.81 3.16 2.67 2.32 1.82 4.83 4.80 6.27 6.09 11.95 17.81 6.34

8 Corporation Bank 3.41 2.56 2.05 1.67 1.14 1.02 0.91 1.26 1.72 3.42 4.81 9.98 11.70 3.51

9 Dena Bank 9.67 6.44 4.07 2.45 2.13 1.80 1.86 1.67 2.19 3.33 5.45 9.98 16.27 5.18

10 I D B I Bank 1.43 1.98 1.89 1.87 1.38 1.54 1.79 2.57 3.22 4.90 5.88 10.98 21.25 4.67

11 Indian Bank 4.15 2.91 1.85 1.21 0.89 0.76 0.99 1.94 3.33 3.67 4.40 6.66 7.47 3.09

12 Indian Overseas

Bank 5.28 3.43 2.34 1.63 2.54 4.71 2.71 2.79 4.02 4.98 8.33 17.40 22.39 6.35

13 Oriental Bank Of

Commerce 9.14 5.95 3.20 2.31 1.53 1.74 1.98 3.17 3.21 3.99 5.18 9.57 13.73 4.98

14 Punjab & Sind

Bank 9.41 9.61 2.44 0.74 0.65 0.63 0.99 1.65 2.96 4.41 4.76 6.48 10.45 4.24

15 Punjab National

Bank 5.96 4.10 3.45 2.74 1.77 1.71 1.79 3.15 4.27 5.25 6.55 12.90 12.53 5.09

16 Syndicate Bank 5.17 4.00 2.95 2.71 1.93 2.43 2.65 2.75 1.99 2.62 3.13 6.70 8.50 3.66

17 Uco Bank 4.96 3.27 3.17 2.97 2.21 2.15 3.31 3.73 5.42 4.32 6.76 16.09 17.12 5.81

231

18 Union Bank Of

India 5.01 3.84 2.94 2.18 1.96 2.25 2.37 3.16 2.98 4.08 4.96 8.70 11.16 4.28

19 United Bank Of

India 6.14 4.66 3.61 2.70 2.85 3.21 2.51 3.41 4.25 10.47 9.49 13.26 15.53 6.31

20 Vijaya Bank 2.94 3.70 2.29 1.60 1.95 2.37 2.56 2.93 2.17 2.41 2.79 6.64 6.59 3.15

21 State Bank Of

India 5.96 3.88 2.92 3.04 2.98 3.28 3.48 4.90 4.75 4.95 4.25 6.50 6.90 4.45

Mean 5.7 4.2 2.8 2.1 1.8 2.1 2.1 2.7 3.3 4.4 5.4 10.2 13.1

Source: Authors Calculations based on data from India.stat.com

Table A.5: Gross NPA to Gross Advances of 18 Private Banks for Thirteen years (2005-2017)

Sr.

No. Private Banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Axis Bank 1.98 1.67 1.11 0.8 1.08 1.39 1.28 1.18 1.19 1.29 1.36 1.71 5.21 1.63

2 Catholic Syrian

Bank 7.16 5.76 4.19 3.87 4.56 3.29 3.05 2.35 2.35 3.77 4.96 5.62 7.25 4.48

3 City Union Bank 5.88 4.32 2.57 1.81 1.8 1.36 1.21 1.01 1.13 1.81 1.86 2.41 2.83 2.31

4 D C B Bank 14.2 15 5.14 1.53 8.78 8.69 5.86 4.4 3.18 1.69 1.76 1.51 1.59 5.64

5 Federal Bank 7.29 4.62 2.95 2.43 2.57 2.97 3.49 3.35 3.44 2.46 2.04 2.84 2.33 3.29

6 H D F C Bank 1.65 1.4 1.36 1.41 1.98 1.44 1.06 0.95 0.85 0.91 0.89 0.92 1.04 1.22

7 I C I C I Bank 2.98 1.51 2.08 3.3 4.32 6.52 5.8 4.83 3.22 3.03 3.78 5.82 8.74 4.30

8 Indusind Bank 3.53 2.86 3.08 3.04 1.61 1.23 1.01 0.98 1.03 1.12 0.81 0.87 0.93 1.70

9 Jammu & Kashmir

Bank 2.72 2.51 2.89 2.53 2.64 1.97 1.95 1.54 1.62 1.66 5.97 8.32 11.2 3.66

10 Karnataka Bank 7.58 5.13 3.94 3.42 3.66 3.73 3.97 3.27 2.51 2.92 2.95 3.44 4.21 3.90

11 Karur Vysya Bank 5.1 3.91 2.82 2.03 1.95 1.72 1.26 1.33 0.96 0.82 1.85 1.29 3.58 2.20

12 Kotak Mahindra

Bank 0.73 0.63 2.57 2.88 4.31 3.62 2.03 1.56 1.55 1.98 1.85 2.36 2.59 2.20

13 Lakshmi Vilas

Bank 7.88 4.14 3.56 3.51 2.71 5.12 1.93 2.98 3.87 4.19 2.75 1.97 2.67 3.64

14 Nainital Bank 2.58 1.91 2 1.9 1.67 1.81 1.27 1.61 3.09 2.47 2.98 4.42 5.01 2.52

15 RBL Bank 10.3 7.59 6.81 6 2.13 2.33 1.12 0.8 0.4 6.79 0.77 0.98 1.2 3.63

16 South Indian Bank 6.64 4.99 3.94 1.77 2.18 1.32 1.11 0.97 1.36 1.19 0.71 3.77 2.45 2.49

17 Yes Bank ---- ----- -----

0.12 0.68 0.27 0.23 0.22 0.2 0.31 0.41 0.76 1.52 0.47

18 Dhanlaxmi Bank ---- ----- ----- ---- ---- ----- ----- ---- ----

5.98 7 6.36 4.78 6.03

Mean 5.2 4.0 3.0 2.5 2.9 2.9 2.2 2.0 1.9 2.5 2.5 3.1 3.8


Table A.6: Net NPA to Net Advances of 21 Public Banks for the Thirteen years 2005-2017

Sr.

No.

Public Sector

Banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 AVG

1 Allahabad Bank 1.28 0.84 1.07 0.8 0.72 0.66 0.79 0.98 3.19 4.15 3.99 6.76 8.92 2.63

2 Andhra Bank 0.28 0.24 0.17 0.15 0.18 0.17 0.38 0.91 2.45 3.11 2.93 4.61 7.57 1.78

3 Bank Of Baroda 1.45 0.87 0.6 0.47 0.31 0.34 0.35 0.54 1.28 1.52 1.89 5.06 4.72 1.49

4 Bank Of India 2.8 1.49 0.96 0.52 0.44 1.31 0.91 1.47 2.06 2 3.36 7.79 6.9 2.46

5 Bank Of

Maharashtra 2.15 2.03 1.21 0.87 0.79 1.64 1.32 0.84 0.52 2.03 4.19 6.35 11.8 2.75

232

6 Canara Bank 1.88 1.12 0.94 0.84 1.09 1.06 1.1 1.46 2.18 1.98 2.65 6.42 6.33 2.23

7 Central Bank Of

India 2.98 2.59 1.7 1.45 1.24 0.69 0.65 3.09 2.9 3.75 3.61 7.36 10.2 3.25

8 Corporation Bank 1.12 0.64 0.47 0.32 0.29 0.31 0.46 0.87 1.19 2.32 3.08 6.53 8.33 1.99

9 Dena Bank 5.23 3.04 1.99 0.94 1.09 1.21 1.22 1.01 1.39 2.35 3.82 6.35 10.7 3.10

10 I D B I Bank

1.12 1.3 0.92 1.02 1.06 1.61 1.58 2.48 2.88 6.78 13.2 2.61

11 Indian Bank 1.35 0.79 0.35 0.24 0.18 0.23 0.53 1.33 2.26 2.26 2.5 4.2 4.39 1.59

12 Indian Overseas

Bank 1.27 0.65 0.55 0.6 1.33 2.52 1.19 1.35 2.5 3.2 5.68 11.9 14 3.59

13 Oriental Bank Of

Commerce 1.29 0.49 0.49 0.99 0.65 0.87 0.98 2.21 2.27 2.82 3.34 6.7 8.96 2.47

14 Punjab & Sind

Bank 8.11 2.43 0.66 0.37 0.32 0.36 0.56 1.19 2.16 3.35 3.55 4.62 7.51 2.71

15 Punjab National

Bank 0.2 0.29 0.76 0.64 0.17 0.53 0.85 1.52 2.35 2.85 4.06 8.61 7.81 2.36

16 State Bank Of

India 2.65 1.88 1.56 1.78 1.79 1.72 1.63 1.82 2.1 2.57 2.12 3.81 3.71 2.24

17 Syndicate Bank 1.59 0.86 0.76 0.97 0.77 1.07 0.97 0.96 0.76 1.56 1.9 4.48 5.21 1.68

18 Uco Bank 2.93 2.1 2.14 1.98 1.18 1.17 1.84 1.96 3.17 2.38 4.3 9.09 8.94 3.32

19 Union Bank Of

India 2.64 1.56 0.96 0.17 0.34 0.81 1.19 1.7 1.61 2.33 2.71 5.25 6.57 2.14

20 United Bank Of

India 2.43 1.95 1.5 1.1 1.48 1.84 1.42 1.72 2.87 7.18 6.22 9.04 10 3.75

21 Vijaya Bank 0.59 0.85 0.59 0.57 0.82 1.4 1.52 1.72 1.3 1.55 1.92 4.8 4.36 1.69

Mean 2.21 1.34 0.98 0.81 0.77 1.00 1.00 1.44 2.00 2.75 3.37 6.50 8.10


Table A.7 Net NPA to Net Advances of 18 private Banks for the Thirteen years 2005-2017

Sr.

No. Bank 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Axis Bank 1.39 0.98 0.72 0.42 0.4 0.4 0.29 0.27 0.36 0.44 0.44 0.74 2.27 0.70

2 Catholic Syrian

Bank 3.8 2.78 1.98 1.61 2.39 1.58 1.74 1.1 1.12 2.22 3.85 4.4 5.51 2.62

3 City Union Bank 3.37 1.95 1.09 0.98 1.08 0.58 0.52 0.44 0.63 1.23 1.3 1.53 1.71 1.26

4 D C B Bank 6.34 4.5 1.64 0.66 3.88 3.11 0.96 0.57 0.75 0.91 1.01 0.75 0.79 1.99

5 Dhanlaxmi Bank 3.92 2.82 1.75 0.88 0.88 0.84 0.3 0.66 3.36 3.8 3.29 2.73 2.53 2.14

6 Federal Bank 2.21 0.95 0.44 0.23 0.3 0.48 0.6 0.53 0.98 0.74 0.73 1.64 1.28 0.85

7 H D F C Bank 0.24 0.44 0.43 0.47 0.63 0.31 0.19 0.18 0.2 0.27 0.25 0.28 0.33 0.32

8 I C I C I Bank 1.65 0.72 1.02 1.55 2.09 2.12 1.11 0.73 0.77 0.97 1.61 2.98 5.43 1.75

9 Indusind Bank 2.71 2.09 2.47 2.27 1.14 0.5 0.28 0.27 0.31 0.33 0.31 0.36 0.39 1.03

10 Jammu & Kashmir

Bank 1.41 0.92 1.13 1.07 1.92 1.30 1.24 0.28 0.14 0.22 2.77 4.31 4.87 1.30

11 Karnataka Bank 2.29 1.18 1.22 0.98 0.98 1.31 1.62 2.11 1.51 1.91 1.98 2.35 2.64 1.70

12 Karur Vysya Bank 1.66 0.81 0.23 0.18 0.25 0.23 0.07 0.33 0.37 0.41 0.78 0.55 2.53 0.65

13 Kotak Mahindra

Bank 0.37 0.24 1.98 1.78 2.39 1.73 0.72 0.61 0.64 1.08 0.92 1.06 1.26 1.14

233

14 Lakshmi Vilas Bank 4.98 1.89 1.58 1.55 1.24 4.11 0.9 1.74 2.43 3.44 1.85 1.18 1.76 2.20

15 Nainital Bank ---- ----- ----- ---- ---- ----- ----- ---- ---- ----- ----- ---- ---- -----

16 R B L Bank 5.54 2.61 1.92 0.99 0.68 0.97 0.36 0.2 0.11 0.31 0.27 0.59 0.64 1.17

17 South Indian Bank 3.81 1.86 0.98 0.33 1.13 0.39 0.29 0.28 0.78 0.78 0.96 2.89 1.45 1.23

18 Yes Bank 0.43 0.09 0.03 0.09 0.33 0.06 0.03 0.05 0.01 0.05 0.12 0.29 0.81 0.14

Mean 2.69 1.57 1.21 0.89 1.16 1.10 0.59 0.59 0.80 1.06 1.25 1.59 2.01


Table A.8: Total loan to Total Asset of 21 public banks for 13 years for 2005-2017

Sr.

No. Public Banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 AVG

1 Allahabad Bank 0.47 0.53 0.61 0.60 0.61 0.59 0.62 0.61 0.64 0.63 0.66 0.64 0.64 0.60

2 Andhra Bank 0.54 0.55 0.59 0.61 0.65 0.63 0.66 0.68 0.68 0.66 0.69 0.66 0.62 0.63

3 Bank Of Baroda 0.47 0.54 0.59 0.60 0.64 0.63 0.64 0.65 0.61 0.61 0.61 0.58 0.56 0.59

4 Bank Of India 0.60 0.60 0.61 0.64 0.64 0.62 0.62 0.66 0.65 0.66 0.66 0.59 0.58 0.62

5 Bank Of

Maharashtra 0.40 0.53 0.59 0.61 0.58 0.57 0.62 0.62 0.65 0.66 0.68 0.67 0.61 0.60

6 Canara Bank 0.55 0.60 0.60 0.59 0.63 0.64 0.63 0.63 0.59 0.62 0.61 0.59 0.59 0.61

7 Central Bank Of

India 0.40 0.52 0.57 0.60 0.59 0.59 0.63 0.65 0.65 0.62 0.61 0.60 0.43 0.57

8 Corporation

Bank 0.55 0.60 0.58 0.60 0.57 0.58 0.61 0.62 0.62 0.63 0.65 0.61 0.58 0.60

9 Dena Bank 0.49 0.54 0.59 0.60 0.60 0.62 0.64 0.66 0.59 0.63 0.62 0.63 0.57 0.60

10 I D B I Bank Ltd. 0.58 0.62 0.62 0.64 0.61 0.60 0.63 0.63 0.61 0.61 0.59 0.58 0.54 0.60

11 Indian Bank 0.46 0.47 0.52 0.57 0.61 0.61 0.62 0.64 0.66 0.66 0.66 0.65 0.60 0.60

12 Indian Overseas

Bank 0.50 0.59 0.58 0.60 0.62 0.61 0.63 0.65 0.66 0.65 0.60 0.59 0.57 0.60

13 Oriental Bank Of

Commerce 0.47 0.58 0.60 0.61 0.61 0.61 0.59 0.63 0.64 0.63 0.63 0.62 0.62 0.60

14 Punjab & Sind

Bank 0.42 0.49 0.55 0.60 0.60 0.58 0.63 0.64 0.64 0.61 0.66 0.62 0.60 0.59

15 Punjab National

Bank 0.48 0.52 0.60 0.61 0.63 0.63 0.64 0.64 0.65 0.64 0.63 0.62 0.58 0.61

16 State Bank Of

India 0.44 0.53 0.60 0.58 0.57 0.60 0.62 0.65 0.67 0.68 0.64 0.63 0.58 0.60

17 Syndicate Bank 0.52 0.60 0.59 0.60 0.63 0.65 0.69 0.68 0.69 0.70 0.68 0.66 0.68 0.64

18 Uco Bank 0.51 0.61 0.63 0.62 0.62 0.60 0.61 0.65 0.65 0.63 0.61 0.52 0.53 0.60

19 Union Bank Of

India 0.57 0.61 0.62 0.60 0.60 0.61 0.64 0.68 0.67 0.65 0.67 0.66 0.63 0.63

20 United Bank Of

India 0.38 0.47 0.53 0.52 0.58 0.56 0.60 0.62 0.61 0.53 0.55 0.53 0.48 0.53

21 Vijaya Bank 0.49 0.54 0.58 0.57 0.58 0.59 0.60 0.62 0.64 0.61 0.62 0.62 0.62 0.59

Mean 0.49 0.55 0.59 0.60 0.61 0.61 0.63 0.64 0.64 0.63 0.63 0.61 0.58


Table A.9: Total loan to total Asset of 18 Private Banks for 13 years for 2005-2017

Sr.

No.

Private

Banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 AVG

1 Axis Bank 0.42 0.45 0.50 0.54 0.55 0.58 0.59 0.59 0.58 0.60 0.64 0.66 0.65 0.57

234

2 Catholic

Syrian Bank 0.50 0.57 0.57 0.56 0.53 0.59 0.64 0.64 0.65 0.57 0.60 0.50 0.50 0.57

3 City Union

Bank 0.60 0.64 0.65 0.64 0.63 0.61 0.65 0.67 0.68 0.65 0.66 0.68 0.69 0.65

4 D C B Bank 0.48 0.52 0.52 0.55 0.57 0.58 0.59 0.62 0.59 0.64 0.65 0.68 0.66 0.59

5 Dhanlaxmi

Bank 0.54 0.57 0.54 0.53 0.57 0.62 0.64 0.60 0.57 0.54 0.54 0.56 0.52 0.56

6 Federal Bank 0.47 0.58 0.61 0.59 0.58 0.62 0.62 0.63 0.63 0.59 0.63 0.62 0.64 0.60

7 H D F C

Bank 0.50 0.48 0.52 0.48 0.56 0.57 0.59 0.59 0.61 0.62 0.62 0.63 0.65 0.57

8 I C I C I Bank 0.57 0.60 0.59 0.58 0.59 0.51 0.54 0.53 0.55 0.58 0.60 0.65 0.64 0.58

9 Indusind

Bank 0.59 0.54 0.54 0.56 0.58 0.59 0.58 0.61 0.61 0.64 0.62 0.62 0.64 0.59

10

Jammu &

Kashmir

Bank

0.47 0.55 0.60 0.58 0.56 0.54 0.52 0.55 0.55 0.59 0.59 0.63 0.61 0.56

11 Karnataka

Bank 0.51 0.53 0.60 0.57 0.52 0.55 0.56 0.58 0.61 0.61 0.63 0.62 0.59 0.57

12 Karur Vysya

Bank 0.60 0.63 0.64 0.65 0.61 0.62 0.63 0.64 0.63 0.66 0.68 0.67 0.66 0.64

13

Kotak

Mahindra

Bank

0.62 0.62 0.55 0.55 0.58 0.56 0.58 0.60 0.58 0.61 0.62 0.62 0.63 0.59

14 Lakshmi

Vilas Bank 0.54 0.62 0.64 0.61 0.65 0.62 0.63 0.65 0.67 0.63 0.66 0.69 0.68 0.64

15 Nainital Bank 0.35 0.46 0.47 0.48 0.46 0.45 0.51 0.48 0.50 0.46 0.43 0.44 0.42 0.45

16 R B L Bank 0.50 0.51 0.47 0.40 0.47 0.56 0.59 0.57 0.49 0.54 0.53 0.54 0.60 0.52

17 South Indian

Bank 0.57 0.59 0.58 0.61 0.58 0.62 0.63 0.68 0.64 0.66 0.64 0.65 0.63 0.62

18 Yes Bank 0.58 0.58 0.57 0.57 0.56 0.63 0.60 0.52 0.47 0.51 0.56 0.59 0.62 0.57

Mean 0.52 0.56 0.56 0.56 0.56 0.59 0.60 0.59 0.59 0.61 0.61 0.61


Table A.10: Total loan toTtotal Deposit of 21 public banks for 13 years for 2005-2017

Sr.


1 Allahabad

Bank 0.52 0.60 0.70 0.70 0.70 0.68 0.71 0.70 0.73 0.73 0.78 0.76 0.75 0.70

2 Andhra Bank 0.64 0.66 0.68 0.70 0.08 0.73 0.79 0.80 0.81 0.77 0.82 0.76 0.71 0.69

3 Bank Of

Baroda 0.54 0.65 0.68 0.71 0.72 0.73 0.75 0.75 0.70 0.71 0.70 0.68 0.64 0.68

4 Bank Of India 0.72 0.71 0.72 0.77 0.76 0.75 0.72 0.79 0.77 0.79 0.77 0.71 0.68 0.74

5 Bank Of

Maharashtra 0.45 0.62 0.68 0.71 0.66 0.64 0.71 0.74 0.81 0.77 0.82 0.78 0.70 0.70

6 Canara Bank 0.63 0.69 0.70 1.00 0.74 0.72 0.72 0.72 0.69 0.73 0.71 0.69 0.70 0.73

7 Central Bank

Of India 0.46 0.59 0.65 0.68 0.68 0.67 0.74 0.77 0.77 0.75 0.75 0.69 0.49 0.66

8 Corporation

Bank 0.69 0.74 0.72 0.72 0.67 0.70 0.75 0.75 0.72 0.72 0.74 0.70 0.65 0.71

10 Dena Bank 0.58 0.61 0.67 0.69 0.67 0.69 0.70 0.74 0.68 0.71 0.69 0.71 0.65 0.67

11 I D B I Bank 3.17 2.15 1.50 1.16 1.10 0.83 0.88 0.87 0.87 0.85 0.81 0.82 0.72 1.21

12 Indian Bank 0.53 0.55 0.62 0.65 0.71 0.70 0.73 0.75 0.75 0.77 0.76 0.74 0.72 0.69

235

13 Indian

Overseas Bank 0.57 0.69 0.69 0.75 0.76 0.72 0.78 0.80 0.80 0.78 0.70 0.72 0.67 0.73

14 Oriental Bank

Of Commerce 0.53 0.68 0.70 0.71 0.70 0.70 0.69 0.72 0.73 0.72 0.70 0.71 0.72 0.69

15 Punjab & Sind

Bank 0.46 0.55 0.60 0.75 0.72 0.70 0.72 0.74 0.73 0.68 0.74 0.70 0.68 0.67

16 Punjab

National Bank 0.59 0.63 0.70 0.73 0.74 0.75 0.78 0.78 0.79 0.78 0.76 0.75 0.68 0.73

17 State Bank Of

India 0.58 0.72 0.78 0.78 0.74 0.79 0.82 0.84 0.87 0.88 0.83 0.85 0.77 0.79

18 Syndicate

Bank 0.62 0.69 0.66 0.68 0.71 0.78 0.79 0.79 0.80 0.83 0.80 0.78 0.78 0.75

19 Uco Bank 0.59 0.72 0.79 0.69 0.69 0.68 0.69 0.76 0.75 0.76 0.70 0.62 0.61 0.70

20 Union Bank Of

India 0.66 0.74 0.75 0.72 0.70 0.70 0.75 0.80 0.79 0.77 0.77 0.78 0.76 0.74

21 United Bank

Of India 0.43 0.53 0.60 0.60 0.66 0.63 0.69 0.71 0.69 0.59 0.62 0.59 0.53 0.61

22 Vijaya Bank 0.57 0.94 0.65 0.67 0.66 0.67 0.67 0.71 0.73 0.67 0.70 0.73 0.73 0.70

Mean 0.69 0.74 0.73 0.74 0.69 0.71 0.74 0.74 0.76 0.76 0.75 0.73 0.68 Source: Authors Calculations based on data from India.stat.com

Table A.11: Total Loan to Total Deposit of 18 private banks for 13 years for 2005-2017

Sr.

no. Private Banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Axis Bank 0.50 0.56 0.63 0.68 0.70 0.74 0.75 0.77 0.78 0.82 0.92 0.99 0.94 0.75

2 Catholic

Syrian Bank 0.55 0.63 0.64 0.63 0.67 0.68 0.72 0.73 0.72 0.64 0.66 0.55 0.55 0.64

3 City Union

Bank 0.68 0.75 0.74 0.73 0.71 0.69 0.74 0.75 0.77 0.74 0.76 0.79 0.81 0.74

4 D C B Bank 0.58 0.63 0.62 0.68 0.72 0.74 0.78 0.85 0.80 0.80 0.83 0.87 0.82 0.75

5 Dhanlaxmi

Bank 0.61 0.64 0.60 0.59 0.65 0.71 0.73 0.75 0.70 0.66 0.62 0.62 0.57 0.65

6 Federal Bank 0.52 0.67 0.71 0.74 0.70 0.76 0.75 0.78 0.78 0.75 0.74 0.74 0.76 0.72

7 H D F C Bank 0.71 0.64 0.70 0.64 0.72 0.76 0.78 0.80 0.82 0.83 0.82 0.86 0.87 0.77

8 I C I C I Bank 1.00 0.92 0.88 0.95 1.03 0.92 0.98 1.01 1.01 1.04 1.08 1.11 1.01 1.00

9 Indusind Bank 0.72 0.66 0.66 0.69 0.72 0.78 0.77 0.83 0.82 0.92 0.93 0.96 0.90 0.80

10 Jammu &

Kashmir Bank 0.54 0.62 0.68 0.66 0.64 0.62 0.59 0.62 0.61 0.67 0.68 0.73 0.69 0.64

11 Karnataka

Bank 0.59 0.60 0.69 0.65 0.63 0.62 0.65 0.66 0.71 0.71 0.71 0.69 0.67 0.66

12 Karur Vysya

Bank 0.71 0.75 0.76 0.75 0.69 0.70 0.72 0.75 0.77 0.78 0.82 0.79 0.76 0.75

13

Kotak

Mahindra

Bank

0.93 0.97 0.99 0.95 1.06 0.87 1.00 1.01 0.95 0.90 0.88 0.86 0.86 0.94

14 Lakshmi Vilas

Bank 0.63 0.70 0.74 0.71 0.73 0.72 0.75 0.74 0.76 0.70 0.75 0.78 0.78 0.73

15 Nainital Bank 0.50 0.54 0.54 0.56 0.53 0.51 0.55 0.57 0.58 0.52 0.48 0.51 0.47 0.63

16 R B L Bank 0.50 0.57 0.61 0.53 0.61 0.74 0.80 0.87 0.77 0.87 0.86 0.89 0.86 0.73

17 South Indian

Bank 0.63 0.67 0.65 0.69 0.65 0.69 0.69 0.75 0.72 0.77 0.72 0.74 0.71 0.70

18 Yes Bank 1.11 0.84 0.77 0.73 0.79 0.85 0.77 0.77 0.70 0.75 0.83 0.88 0.93 0.82

Mean 0.67 0.69 0.70 0.70 0.72 0.73 0.75 0.78 0.77 0.77 0.78 0.80 0.78

236


Table A.12: Total Loan to Total Equity of 21 public banks for 13 years for 2005-2017

Sr.

No. Banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Allahabad

Bank 61 66 93 112 132 161 197 223 260 254 263 249 204 175

2 Andhra Bank 44 46 58 71 92 117 129 151 178 186 212 195 202 129

3 Bank Of

Baroda 151 168 233 295 396 484 587 704 787 936 980 844 838 570

4 Bank Of India 116 137 178 219 298 326 395 440 493 585 613 445 348 353

5 Bank Of

Maharashtra 30 39 54 69 80 95 98 97 115 107 94 93 83 81

6 Canara Bank 149 196 242 262 338 414 479 529 552 661 707 610 583 440

7 Central Bank

Of India 24 34 47 184 269 266 327 204 167 133 115 109 76 150

8 Corporation

Bank 131 170 212 277 346 452 592 688 787 830 880 698 625 514

9 Dena Bank 41 50 64 81 101 124 135 164 190 146 143 125 94 112

10 I D B I Bank 72 76 89 116 146 193 161 142 148 124 131 106 94 123

11 Indian Bank 80 85 87 93 120 145 175 211 249 267 267 274 272 178

12 Indian

Overseas Bank 46 64 87 112 139 147 182 179 175 144 140 89 57 120

13 Oriental Bank

Of Commerce 133 136 178 219 276 335 329 384 442 464 484 463 456 331

14 Punjab & Sind

Bank 99 105 140 155 131 180 192 199 204 209 160 160 146 160

15 Punjab

National Bank 193 239 309 383 494 597 767 869 875 967 969 975 987 663

16 State Bank Of

India 388 498 645 664 1038 1002 1201 1305 1536 1636 1754 1902 1981 1196

17 Syndicate

Bank 57 71 100 124 157 174 188 208 246 281 309 289 224 187

18 Uco Bank 35 47 59 69 87 151 159 176 172 149 139 119 78 111

19 Union Bank

Of India 89 108 126 148 192 237 288 323 349 363 402 389 417 264

20 United Bank

Of India 100 110 120 130 133 135 157 176 186 120 80 82 48 122

21 Vijaya Bank 33 39 57 74 83 96 104 119 144 97 103 98 97 88

mean 98 118 151 181 240 277 325 356 393 412 425 395 376


Table A.13: Total Loan to Total Equity of 18 private banks for 13 years for 2005-2017

Sr.

No.

Bank 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Axis Bank 57 80 133 167 290 258 347 411 421 490 625 746 815 372

2 Catholic

Syrian Bank 209 254 283 267 345 238 199 245 212 209 157 110 101 218

3 City Union

Bank 87 111 144 147 231 177 235 302 328 301 308 359 405 241

4 D C B Bank 35 26 36 24 23 18 22 22 27 33 37 45 56 31

237

5 Dhanlaxmi

Bank 45 51 58 66 100 79 107 104 92 63 43 39 31 68

6 Federal Bank 120 141 178 112 264 159 189 224 261 260 305 171 214 200

7 H D F C Bank 83 113 153 182 322 280 349 423 513 637 736 928 447

8 I C I C I Bank 130 170 228 209 250 168 192 224 256 298 338 402 425 253

9 Indusind Bank 32 33 39 41 50 51 57 75 85 105 131 149 190 80

10 Jammu &

Kashmir Bank 239 300 354 391 433 477 542 684 811 960 924 962 624

11 Karnataka

Bank 52 65 80 91 99 110 94 111 135 153 173 186 134 114

12 Karur Vysya

Bank 262 314 393 175 211 248 189 224 277 319 299 323 336 275

13

Kotak

Mahindra

Bank

33 21 35 45 51 60 80 106 130 138 171 130 148 88

14 Lakshmi Vilas

Bank 191 156 190 82 113 67 86 107 121 134 92 110 125 121

15 Nainital Bank 24 20 27 33 38 29 25 28 32 36 38 40 42 32

16 R B L Bank 22 18 19 6 11 11 9 19 25 36 49 65 78 28

17 South Indian

Bank 113 90 112 116 168 140 182 241 238 270 278 306 259 194

18 Yes Bank 4 9 24 33 46 67 102 108 154 181 234 290 96

mean 96 109 138 121 169 146 167 203 220 255 271 255


Table A.14: Provision for NPA to Gross NPA of 21 public banks for 13 years for 2005-2017

Sr.


1 Allahabad Bank 0.78 0.77 0.57 0.59 0.60 0.62 0.52 0.47 0.20 0.29 0.28 0.33 0.35 0.49

2 Andhra Bank 0.88 0.87 0.87 0.88 0.80 0.80 0.72 0.58 0.35 0.43 0.46 0.47 0.41 0.66

3 Bank Of Baroda 0.52 0.56 0.47 0.45 0.75 0.82 0.85 0.75 0.47 0.49 0.50 0.52 0.58 0.59

4 Bank Of India 0.45 0.54 0.64 0.76 0.56 0.49 0.51 0.28 0.22 0.35 0.37 0.41 0.49 0.47

5 Bank Of

Maharashtra 0.69 0.63 0.63 0.63 0.63 0.43 0.44 0.61 0.62 0.35 0.33 0.33 0.08 0.49

6 Canara Bank 0.48 0.43 0.30 0.03 0.30 0.31 0.25 0.15 0.15 0.21 0.33 0.30 0.36 0.28

7 Central Bank Of

India 0.68 0.63 0.65 0.53 0.52 0.49 0.45 0.31 0.35 0.39 0.40 0.26 0.44 0.47

8 Corporation

Bank 0.65 0.74 0.76 0.77 0.74 0.66 0.46 0.28 0.37 0.33 0.37 0.23 0.30 0.51

9 Dena Bank 0.47 0.53 0.50 0.61 0.48 0.32 0.34 0.40 0.36 0.30 0.31 0.39 0.39 0.41

10 I D B I Bank 0.00 0.00 0.41 0.31 1.12 0.03 0.40 0.39 0.50 0.50 0.52 0.41 0.44 0.39

11 Indian Bank 0.56 0.49 0.50 0.45 0.43 0.26 0.22 0.29 0.27 0.33 0.38 0.33 0.38 0.38

12 Indian Overseas

Bank 0.72 0.76 0.70 0.57 0.39 0.40 0.59 0.54 0.37 0.33 0.30 0.32 0.40 0.49

13 Oriental Bank Of

Commerce 0.85 0.92 0.84 0.56 0.56 0.50 0.51 0.31 0.31 0.30 0.36 0.32 0.38 0.52

14 Punjab & Sind

Bank 0.54 0.74 0.72 0.48 0.49 0.42 0.43 0.27 0.27 0.24 0.26 0.30 0.30 0.41

15 Punjab National

Bank 0.84 0.81 0.67 0.67 0.80 0.68 0.52 0.48 0.45 0.46 0.38 0.36 0.40 0.58

16 Syndicate Bank 4.34 3.13 3.04 3.06 3.79 4.32 5.01 7.82 9.81 6.62 4.52 3.06 3.07 4.74

238

17 Uco Bank 0.69 0.94 0.75 0.67 0.60 0.60 0.49 0.48 0.25 0.17 0.37 0.22 0.31 0.50

18 Union Bank Of

India 0.27 0.22 0.25 0.33 0.37 0.26 0.35 0.30 0.45 0.22 0.34 0.33 0.31 0.31

19 United Bank Of

India 1.89 1.68 1.55 1.99 1.53 1.20 1.31 1.08 1.00 0.64 0.86 1.07 1.36 1.32

20 Vijaya Bank 0.00 0.82 0.86 0.89 0.71 0.60 0.48 0.61 0.63 0.98 0.03 0.56 0.68 0.60

21 State Bank of

India 0.03 0.04 0.04 0.03 0.02 0.02 0.01 0.02 0.01 0.03 0.01 0.02 0.02 0.02

Mean 0.82 0.81 0.75 0.73 0.77 0.68 0.72 0.78 0.83 0.66 0.56 0.50 0.55


Table A.15: Provision for NPA to Gross NPA of 18 Private Banks for 13 years for 2005-2017

Sr.

No. Banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Axis Bank 0.30 0.42 0.37 0.51 0.64 0.69 0.74 0.73 0.68 0.70 0.70 0.58 0.61 0.59

2 Catholic

Syrian Bank 0.48 0.50 0.50 0.56 0.46 0.50 0.42 0.43 0.43 0.41 0.22 0.22 0.25 0.41

3 City Union

Bank 0.44 0.52 0.55 0.43 0.37 0.52 0.56 0.55 0.44 0.33 0.31 0.36 0.40 0.45

4 D C B Bank 0.54 0.71 0.66 0.49 0.53 0.66 0.83 0.85 0.74 0.45 0.42 0.49 0.50 0.61

5 Federal Bank 0.71 0.79 0.84 0.90 0.87 0.83 0.82 0.81 0.71 0.66 0.13 0.42 0.44 0.69

6 H D F C Bank 0.89 0.71 0.71 0.67 0.69 0.79 0.84 0.91 0.91 0.78 0.78 0.71 0.69 0.78

7 I C I C I Bank 0.45 0.51 0.50 0.52 0.53 0.61 0.78 0.82 0.77 0.69 0.59 0.51 0.40 0.59

8 Indusind Bank 0.24 0.27 0.20 0.26 0.30 0.60 0.73 0.73 0.70 0.70 0.63 0.59 0.58 0.50

9 Jammu &

Kashmir Bank 0.48 0.64 0.61 0.58 0.48 0.85 0.89 0.87 0.87 0.83 0.53 0.48 0.57 0.67

10 Karnataka

Bank 0.66 0.73 0.66 0.65 0.65 0.56 0.48 0.32 0.31 0.32 0.33 0.31 0.22 0.48

11 Karur Vysya

Bank 0.65 0.75 0.76 0.76 0.74 0.75 0.80 0.63 0.53 0.41 0.56 0.56 0.29 0.63

12 Kotak

Mahindra

Bank

0.49 0.62 0.23 0.39 0.40 0.53 0.65 0.61 0.59 0.46 0.51 0.56 0.52 0.50

13 Lakshmi Vilas

Bank 0.33 0.49 0.51 0.52 0.51 0.19 0.22 0.22 0.22 0.10 0.26 0.29 0.27 0.32

14 Nainital Bank 1.75 1.52 0.00 0.00 2.05 1.71 1.86 1.37 1.00 1.10 1.00 0.74 0.72 1.14

15 R B L Bank 0.49 1.37 0.73 0.85 0.68 0.59 0.68 0.75 0.73 0.61 0.65 0.40 0.44 0.69

16 South Indian

Bank 0.42 0.61 0.72 0.76 0.43 0.64 0.69 0.66 0.38 0.30 0.40 0.23 0.39 0.51

17 Yes Bank ---- ----- -----

0.19 0.52 0.41 0.89 0.79 0.93 0.85 0.72 0.62 0.47 0.64

18 Dhanalaxmi

Bank

---- ----- ----- ---- ---- ----- ----- ---- ---- 0.37 0.53 0.56 0.49 0.49

Mean 0.58 0.70 0.53 0.53 0.64 0.67 0.76 0.71 0.64 0.56 0.2 0.48 0.46


Table A.16: NPA to Equity of 21 public banks for 13 years for 2005-2017

Sr.

No. Public banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Allahabad

Bank 3.70 2.65 2.44 2.26 2.41 2.73 3.46 4.11 10.27 14.81 14.63 25.06 20.69 8.40

239

2 Andhra Bank 1.10 0.90 0.82 0.77 0.75 1.01 1.78 3.21 6.64 9.93 11.41 16.80 16.80 5.53

3 Bank Of

Baroda 11.33 6.56 5.74 5.44 5.06 6.03 7.12 9.44 18.95 27.66 36.77 87.93 87.93 24.30

4 Bank Of India 6.47 5.09 4.31 3.68 4.70 8.53 7.97 9.01 14.71 18.48 33.38 61.09 47.29 17.29

5 Bank Of

Maharashtra 2.23 2.19 1.90 1.78 1.85 2.81 2.44 2.20 1.72 3.41 6.02 8.89 8.89 3.56

6 Canara Bank 5.78 4.37 3.64 2.78 5.29 6.11 6.73 8.78 14.13 16.41 27.44 0.00 0.00 8.24

7 Central Bank

Of India 2.14 2.39 7.93 5.81 5.73 6.08 5.92 9.88 8.10 8.52 7.16 18.72 16.63 8.57

8 Corporation

Bank 4.51 4.36 4.36 4.07 3.90 4.54 5.33 8.60 13.39 28.27 42.42 16.2 99.04 18.57

9 Dena Bank 4.00 3.31 2.59 2.00 2.16 2.24 2.53 2.73 4.15 4.86 7.83 12.84 10.88 4.78

10 I D B I Bank 0.91 1.54 1.70 2.16 0.60 29.38 2.83 3.56 4.84 6.21 7.91 12.08 12.08 6.60

11 Indian Bank 2.91 2.4 1.27 1.13 1.07 1.07 1.68 3.89 8.30 9.81 11.81 18.38 18.38 6.40

12 Indian

Overseas 2.55 2.25 2.06 1.83 3.55 6.32 4.51 4.46 7.15 7.30 12.08 16.63 12.24 6.38

13 Oriental Bank

Of Commerce 13.36 8.45 5.80 5.11 4.22 5.86 6.58 12.27 14.34 18.74 25.57 45.74 42.47 16.04

14 Punjab & Sind

Bank 0.56 0.45 1.33 0.86 0.88 1.13 1.90 3.26 6.05 9.28 7.70 10.56 10.56 5.70

15 Punjab

National Bank 11.87 9.95 10.75 9.86 8.78 10.19 13.82 25.62 38.10 52.14 69.28 49.2 44.4 25.05

16 Syndicate Bank 3.04 2.89 2.99 3.39 3.05 3.84 4.52 5.07 4.95 7.38 9.73 19.67 15.29 6.60

17 Uco Bank 1.75 1.54 1.88 2.07 2.80 3.03 4.92 6.05 9.47 10.12 6.16 19.44 13.40 6.36

18 Union Bank Of

India 4.47 3.98 3.71 3.28 3.81 5.27 6.91 9.85 10.58 20.67 15.04 35.16 35.16 12.15

19 United Bank

Of India 3.67 0.98 1.23 0.87 0.66 4.34 3.94 6.03 7.91 11.81 8.48 11.28 6.79 6.80

20 Vijaya Bank 1.00 1.25 1.30 1.18 1.61 2.29 2.66 3.47 3.09 2.84 95.94 6.46 6.03 9.93

21 State Bank Of

India 23.67 21.23 19.00 19.0 45.8 28.09 97.02 55.37 74.84 30.91 75.98 32.9 4.1 50.61

Mean 6.36 3.75 4.43 2.87 3.14 6.71 9.27 9.37 13.41 15.22 25.37 25.10 26.70


Table A.17: NPA to Equity of 21 private banks for 13 years for 2005-2017

Sr.

No. Private banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Axis Bank 1.14 1.34 1.46 1.36 2.48 3.20 3.87 4.16 5.07 6.39 8.16 3.55 4.85 6.71

2 Catholic Syrian

Bank 6.89 14.93 11.98 7.82 9.10 7.89 6.14 5.82 5.04 7.97 7.87 6.23 7.41 8.22

3 City Union

Bank 5.07 4.70 3.45 2.59 3.19 2.34 2.78 3.03 3.65 5.40 5.63 8.56 11.35 5.14

4 D C B Bank 4.81 4.14 0.99 0.36 1.75 1.60 1.32 1.00 0.86 0.55 0.66 0.69 0.89 1.64

5 Federal Bank 5.53 6.58 5.27 2.74 3.45 4.80 6.71 7.60 9.08 6.36 29.52 4.85 5.01 7.66

6 H D F C Bank 1.38 1.59 2.02 2.55 4.66 3.95 3.57 3.87 4.30 5.78 6.51 8.50 11.37 5.00

7 I C I C I Bank 3.76 2.50 4.59 6.81 8.67 8.31 8.52 8.06 8.33 9.10 13.02 9.53 36.19 9.82

8 Indusind Bank 1.10 0.93 1.07 1.23 0.72 0.62 0.57 0.74 0.88 1.18 1.06 1.31 1.76 1.10

9 Jammu &

Kashmir Bank 6.54 7.64 10.35 8.82 11.54 9.54 10.70 9.87 13.28 16.16 5..02 14.5 11.5 7.96

240

10 Karnataka

Bank 4.14 3.42 3.19 3.13 3.65 4.10 3.73 3.64 3.39 4.44 5.01 6.26 5.60 4.47

11 Karur Vysya

Bank 10.82 12.41 4.10 3.60 3.82 4.32 2.42 2.99 2.67 2.60 5.57 4.19 12.18 5.07

12 Kotak

Mahindra Bank 0.24 0.13 0.86 1.31 2.11 2.20 1.64 1.66 2.03 2.75 3.20 3.09 3.89 2.09

13 Lakshmi Vilas

Bank 8.92 6.39 2.74 2.83 2.95 3.34 1.62 3.15 4.71 5.60 2.54 2.18 3.34 3.45

14 Nainital Bank 0.63 0.39 0.53 0.63 0.63 0.52 0.32 0.46 1.00 0.91 1.15 1.79 2.12 0.92

15 R B L Bank 2.37 0.68 0.54 0.35 0.17 0.26 0.10 0.15 0.10 0.29 0.38 0.64 0.95 0.58

16 South Indian

Bank 7.68 4.66 4.56 2.08 2.31 1.87 2.04 2.36 3.24 3.22 4.77 6.53 6.37 3.76

17 Yes Bank 0.00 0.00 0.00 0.04 0.29 0.34 0.23 0.24 0.26 0.48 0.75 1.78 4.42 0.74

18 Dhanlaxmi

Bank 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.86 3.15 2.59 1.50 2.77

Mean 3.24 4.53 3.21 2.11 3.62 3.48 3.31 2.88 3.99 6.93 8.67 12.93 9.19


Table A.18: CAR of 21 public banks for 13 years for 2005-2017

Sr.

No. Public Bank 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Allahabad

Bank 12.53 13.4 12.52 13 13.11 13.62 12.96 12.83 11.03 9.96 10.45 11.02 11.45 12

2 Andhra Bank 12.11 14 11.33 11.6 13.12 13.93 14.38 13.18 11.76 10.78 10.43 11.58 10.65 12

3 Bank Of

Baroda 12.8 13.7 11.8 12.9 14.05 14.36 14.52 14.67 13.3 12.28 12.61 13.18 12.24 14

4 Bank Of India 11.52 10.8 11.58 13 13.01 12.94 12.17 11.95 11.02 9.97 10.73 12.01 12.14 12

5 Bank Of

Maharashtra 12.68 11.3 12.06 10.9 12.05 12.88 13.35 12.43 12.59 10.79 11.94 11.2 11.18 12

6 Canara Bank 12.78 11.2 13.5 13.3 14.91 13.43 15.38 13.76 12.4 10.63 10.56 11.08 12.86 13

7 Central Bank

Of India 12.15 11 10.4 9.39 13.12 12.23 11.64 12.4 11.49 9.87 10.9 10.4 10.94 13

8 Corporation

Bank 16.23 13.9 12.76 12.1 13.61 15.37 14.11 13 12.33 11.65 11.09 10.56 11.32 14

9 Dena Bank 11.91 10.6 11.52 11.1 12.07 12.77 13.41 11.51 11.3 11.14 10.93 11 11.39 12

10 I D B I Bank

Ltd. 15.51 14.8 13.73

11.57 11.31 13.64 14.58 13.13 11.68 11.76 11.67 10.7 14

11 Indian Bank 14.14 13.2 14.14 12.7 13.98 12.71 13.56 13.47 13.08 12.64 12.86 13.2 13.64 13

12

Indian

Overseas

Bank

14.2 13 13.27 11.9 13.2 14.78 14.55 13.32 11.85 10.78 10.11 9.67 10.49 13

13 Oriental Bank

Of Commerce 9.21 11 12.51 12.1 12.98 12.54 14.23 12.69 12.04 11.01 11.41 11.76 11.64 12

14 Punjab & Sind

Bank 9.46 12.8 12.88 11.6 14.35 13.1 12.94 13.26 12.91 11.04 11.24 10.91 11.05 12

15 Punjab

National Bank 14.78 12 12.29 13 14.03 14.16 12.43 12.63 12.72 11.52 12.21 11.28 11.66 13

16 State Bank Of

India 12.45 11.9 12.34 13.5 14.25 13.39 11.98 13.86 12.92 12.44 11.56 13.12 13.11 13

17 Syndicate

Bank 10.7 11.7 11.74 11.8 12.68 12.7 13.04 12.24 12.59 11.41 10.54 11.16 12.03 12

18 Uco Bank 11.26 11.1 11.56 11 11.93 13.21 13.71 12.35 14.15 12.68 12.17 9.63 10.93 12

19 Union Bank 12.09 11.4 12.8 12.5 13.27 12.51 12.95 11.85 11.45 10.8 10.22 10.56 11.79 12

241

Of India

20 United Bank

Of India 18.16 13.1 12.02 12.30 13.28 12.8 13.05 12.69 11.66 9.81 10.57 10.08 11.14 12

21 Vijaya Bank 12.92 11.9 11.21 11.2 13.15 12.5 13.88 13.06 11.32 10.56 11.43 12.58 13.86 12

Mean 12.84 12.28 12.28 12.70 13.22 13.20 13.42 12.93 12.23 11.95 11.86 12.06 10.94


Table A.19: CAR of 18 private banks for 13 years for 2005-2017

Sr.

No. Private Bank 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Axis Bank 12.7 11.1 11.6 14 13.7 15.8 12.65 13.66 17 16.07 15.39 15.29 14.95 14

2 Catholic

Syrian Bank 11.4 11.3 9.6 11.2 12.3 10.8 11.22 11.08 12.29 11 11 10.55 12.15 12

3 City Union

Bank 12.2 12.3 12.6 12.5 12.7 13.5 12.75 12.57 13.98 15.01 16.52 15.58 15.83 14

4 D C B Bank 9.9 9.7 11.3 13.5 13.3 14.9 13.25 15.41 13.61 13.71 14.95 14.11 13.76 13

5 Dhanlaxmi

Bank 10.2 9.8 9.8 8.8 15.4 13 11.8 9.49 11.06 8.67 9.59 7.51 10.26 11

6 Federal Bank 11.3 13.8 13.4 12.5 20.2 18.4 16.79 16.64 14.73 15.28 15.46 13.93 12.39 11

7 H D F C Bank 12.2 11.4 13.1 12.9 15.7 17.4 16.22 16.52 16.8 16.07 16.79 15.53 14.55 15

8 I C I C I Bank 11.8 13.4 11.7 15.5 15.5 19.4 19.54 18.52 19.72 17.7 17.02 16.64 17.39 16

9 Indusind Bank 11.6 10.5 12.5 13.8 12.6 15.3 15.89 13.85 14.38 13.83 12.09 15.5 15.31 14

10 Jammu &

Kashmir Bank 15.2 13.5 13.2 13.9 14.5 15.9 13.33 13.36 12.83 12.69 12.57 11.85 10.8 14

11 Karnataka

Bank 14.2 11.8 11 12.2 13.5 12.4 13.33 12.84 13.22 13.2 12.41 12.03 13.3 13

12 Karur Vysya

Bank 16.1 14.8 14.5 12.6 14.9 14.5 14.41 14.41 14.41 12.59 14.62 12.17 12.54 14

13

Kotak

Mahindra

Bank

12.8 11.3 13.5 12.7 20 18.4 19.92 17.52 16.05 18.83 17.81 16.28 16.77 16

14 Lakshmi Vilas

Bank 11.3 10.8 12.4 12.7 10.3 14.8 16.35 13.1 12.32 10.9 11.34 10.67 10.38 12

15 Nainital Bank 14.9 13.9 12.9 12.3 13.1 15.7 16.35 15.09 14.43 15.13 14.86 15.69 12.78 14

16 R B L Bank 12 10.8 34.3 49.2 42.3 34.1 56.41 23.2 17.11 14.64 13.52 12.94 13.72 31

17 South Indian

Bank 9.9 13 11.1 13.1 14.8 15.4 14.01 14 13.91 12.42 11.92 11.82 12.37 13

18 Yes Bank 18.8 16.4 13.6 14.5 16.6 20.6 16.5 17.9 18.3 14.4 19.7 16.5 17 17

Mean 12.69 12.20 13.45 16.80 16.19 16.68 17.26 14.58 14.78 14.15 13.27 13.33 12.53


Table A.20: ROA of 21 public banks for 13 years for 2005-2017

Sr.

No. Public banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Allahabad

Bank 1.2 1.42 1.26 1.32 0.9 1.16 1.11 1.02 0.64 0.57 0.29 -0.3 -0.1 0.80

2 Andhra Bank 1.59 1.38 1.31 1.16 1.09 1.39 1.36 1.19 0.99 0.29 0.38 0.28 0.08 0.96

3 Bank Of

Baroda 0.75 0.79 0.8 0.89 1.09 1.21 1.33 1.24 0.9 0.75 0.49 -0.8 0.2 0.74

4 Bank Of India 0.38 0.68 0.88 1.25 1.49 0.7 0.82 0.72 0.65 0.51 0.27 -0.9 -0.2 0.55

5 Bank Of

Maharashtra 0.54 0.16 0.76 0.75 0.72 0.7 0.47 0.55 0.74 0.3 0.33 0.07 -0.9 0.40

6 Canara Bank 1.01 1.13 0.98 0.92 1.06 1.3 1.42 0.95 0.77 0.54 0.55 -0.5 0.2 0.79

242

7 Central Bank

Of India 0.53 0.37 0.62 0.54 0.45 0.66 0.7 0.26 0.44 -0.5 0.21 -0.5 -0.8 0.23

8 Corporation

Bank 1.12 1.24 1.17 1.29 1.24 1.28 1.21 1.06 0.88 0.29 0.28 -0.2 0.23 0.85

9 Dena Bank 0.26 0.29 0.71 1.06 1.02 1.01 1 1.08 0.86 0.51 0.22 -1 0.67 0.59

10 I D B I Bank 0.55 0.59 0.67 0.67 0.62 0.53 0.73 0.83 0.72 0.41 0.29 -1.1 1.37 0.53

11 Indian Bank 1.08 1.16 1.46 1.64 1.62 1.67 1.53 1.31 1.02 0.67 0.54 0.36 0.67 1.13

12 Indian

Overseas Bank 1.28 1.32 1.36 1.3 1.17 0.53 0.71 0.52 0.24 0.23 -0.2 -1 -1.2 0.49

13 Oriental Bank

Of Commerce 2.01 1.39 1.21 1.02 0.88 0.91 1.03 0.67 0.71 0.56 0.23 0.07 -0.5 0.79

14 Punjab & Sind

Bank -0.5 0.64 1.01 1.49 1.24 1.05 0.9 0.65 0.44 0.35 0.13 0.34 0.2 0.61

15 Punjab

National Bank 1.12 1.09 1.03 1.15 1.39 1.44 1.34 1.19 1 0.64 0.53 -0.6 0.19 0.88

16 State Bank Of

India 0.99 0.89 0.84 1.01 1.04 0.88 0.71 0.88 0.97 0.65 0.68 0.46 0.41 0.80

17 Syndicate

Bank 0.82 0.91 0.91 0.88 0.81 0.62 0.76 0.81 1.07 0.78 0.58 -0.6 0.12 0.65

18 Uco Bank 0.73 0.34 0.47 0.52 0.59 0.87 0.66 0.69 0.33 0.7 0.48 -1.3 -0.8 0.34

19 Union Bank

Of India 1.1 0.84 0.92 1.26 1.27 1.25 1.05 0.79 0.79 0.52 0.49 0.35 0.13 0.83

20 United Bank

Of India 1.04 0.66 0.73 0.68 0.34 0.45 0.66 0.7 0.38 -1 0.21 -0.2 0.16 0.37

21 Vijaya Bank 1.43 0.45 0.92 0.75 0.59 0.76 0.72 0.66 0.59 0.35 0.33 0.28 0.49 0.64

Mean 0.91 0.84 0.95 1.03 0.98 0.97 0.96 0.85 0.72 0.39 0.35 0.32 0.03


Table A.21: ROA of 18 Private Banks for 13 years for 2005-2017

Sr.

No. Bank 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

1 Axis Bank 1.21 1.18 1.1 1.24 1.44 1.67 1.68 1.68 1.7 1.78 1.83 1.72 0.65 1.45

2 Catholic

Syrian Bank 0.24 0.13 0.37 0.64 0.57 0.02 0.14 0.24 0.25 0.18 -34 -0.9 0.01 -2.47

3 City Union

Bank 1.33 1.46 1.57 1.6 1.5 1.52 1.67 1.71 1.58 1.44 1.49 1.5 1.5 1.53

4 D C B Bank -3.4 -2.01 0.17 0.48 -1.3 -1.3 0.3 0.68 1.06 1.31 1.37 1.14 0.93 -0.04

5 Dhanlaxmi

Bank -0.8 0.33 0.47 0.76 1.21 0.35 0.23 -0.7 0.02 -1.8 -1.8 -1.6 0.1 -0.25

6 Federal Bank 0.62 1.28 1.38 1.34 1.48 1.15 1.34 1.41 1.35 1.2 1.32 0.57 0.84 1.18

7 H D F C Bank 1.47 1.38 1.33 1.32 1.28 1.53 1.58 1.77 1.9 2 2.02 1.89 1.88 1.64

8 I C I C I Bank 1.48 1.3 1.09 1.12 0.98 1.13 1.35 1.5 1.7 1.78 1.86 1.49 1.35 1.39

9 Indusind Bank 1.5 0.22 0.34 0.34 0.58 1.14 1.46 1.57 1.63 1.81 1.9 1.91 1.86 1.25

10 Jammu &

Kashmir Bank 0.47 0.67 0.96 1.09 1.09 1.2 1.22 1.56 1.7 1.74 0.7 0.57 -2 0.84

11 Karnataka

Bank 1.27 1.28 1.15 1.37 1.25 0.67 0.72 0.73 0.89 0.71 0.91 0.78 0.74 0.96

12 Karur Vysya

Bank 1.45 1.65 1.53 1.63 1.49 1.76 1.71 1.56 1.35 0.86 0.88 1.03 1 1.38

13 Kotak

Mahindra 1.56 1.39 0.94 1.1 1.03 1.72 1.77 1.83 1.81 1.8 1.98 1.19 1.73 1.53

243

Bank

14 Lakshmi Vilas

Bank 0.08 0.53 0.33 0.41 0.71 0.33 0.91 0.73 0.54 0.32 0.61 0.69 0.83 0.54

15 Nainital Bank 1.25 1.06 1.26 1.67 1.68 1.72 1.56 1.75 1.3 1.48 1.26 0.8 0.73 1.35

16 R B L Bank -1.2 0.07 0.31 1.31 1.96 1.05 0.53 1.38 1.06 0.67 1.02 0.99 1.08 0.79

17 South Indian

Bank 0.09 0.53 0.88 1.01 1.09 1.07 1.05 1.12 1.17 1 0.56 0.55 0.57 0.82

18 Yes Bank -0.3 2.13 1.44 1.54 1.59 1.79 1.58 1.57 1.57 1.61 1.71 1.78 1.81 1.53

Mean 0.46 0.81 0.92 1.11 1.09 1.03 1.16 1.23 1.25 1.11 0.80 0.89 0.87


Table A.22: Liquidity Analysis of Indian banks using Proxy L1 (WC/TA) for time series data from

2005 2017

Public Sector Banks Private banks

Year Mean Max Min S.D. Year Mean Max Min S.D.

2005 0.07 0.15 0.01 0.03 2005 0.09 0.27 -0.04 0.07

2006 0.08 0.16 0.03 0.03 2006 0.08 0.20 -0.05 0.05

2007 0.10 0.17 0.06 0.03 2007 0.10 0.24 -0.04 0.05

2008 0.09 0.18 0.03 0.04 2008 0.11 0.42 -0.01 0.08

2009 0.10 0.22 0.05 0.05 2009 0.11 0.27 -0.01 0.06

2010 0.08 0.13 0.03 0.03 2010 0.08 0.27 -0.01 0.05

2011 0.08 0.19 0.01 0.04 2011 0.08 0.33 -0.02 0.06

2012 0.07 0.18 0.03 0.03 2012 0.07 0.22 -0.01 0.04

2013 0.07 0.16 0.04 0.03 2013 0.07 0.33 0.00 0.05

2014 0.07 0.20 0.02 0.04 2014 0.07 0.28 0.01 0.05

2015 0.09 0.21 0.05 0.04 2015 0.09 0.32 0.02 0.05

2016 0.11 0.23 0.05 0.04 2016 0.09 0.29 0.02 0.05

2017 0.13 0.28 0.06 0.05 2017 0.11 0.28 0.02 0.06

Mean

0.087 Mean 0.088

Result of Independent sample T-test --- T(24 ) = -117,P=0.908 (NSD)

Notes and Sources: Calculated by Author using the Annual Report of individual banks from 2005-2017

Table A.23: Liquidity Analysis of Indian banks using Proxy L1 (WC/TA) for Cross-sectional data from

2005-2017

Public Sector

Banks

Mean Max Min Std.

Dev

Private Bank Mean Max Min Std Dev

Allahabad Bank 0.07 0.10 0.04 0.02 Axis Bank Ltd. 0.07 0.12 0.03 0.03

Andhra Bank 0.09 0.13 0.06 0.02

Catholic Syrian

Bank Ltd. 0.14 0.22 0.07 0.04

Bank Of Baroda 0.17 0.28 0.08 0.06

City Union Bank

Ltd. 0.08 0.10 0.04 0.02

Bank Of India 0.10 0.16 0.05 0.03 D C B Bank Ltd. 0.04 0.13 -0.01 0.04

Bank Of

Maharashtra 0.08 0.17 0.04 0.03

Dhanalaxmi Bank

Ltd. 0.11 0.16 0.07 0.03

Canara Bank 0.09 0.13 0.06 0.02 Federal Bank Ltd. 0.11 0.22 0.03 0.05

Central Bank Of

India 0.08 0.19 0.03 0.04

H D F C Bank Ltd. 0.04 0.11 -0.03 0.04

Corporation Bank 0.08 0.12 0.02 0.03 I C I C I Bank Ltd. 0.07 0.10 0.03 0.02

Dena Bank 0.12 0.21 0.07 0.04 Indusind Bank Ltd. 0.07 0.13 0.01 0.04

I D B I Bank Ltd. 0.06 0.17 0.01 0.04

Jammu & Kashmir

Bank 0.11 0.23 0.03 0.06

Indian Bank 0.07 0.10 0.05 0.02

Karnataka Bank

Ltd. 0.09 0.27 0.04 0.06

244

Indian Overseas

Bank 0.09 0.14 0.05 0.02

Karur Vysya Bank

Ltd. 0.06 0.08 0.03 0.02

Oriental Bank Of

Commerce 0.09 0.15 0.04 0.03

Kotak Mahindra

Bank Ltd. 0.00 0.06 -0.05 0.03

Punjab & Sind Bank 0.07 0.15 0.02 0.04

Lakshmi Vilas

Bank Ltd. 0.08 0.16 0.02 0.04

Punjab National

Bank 0.09 0.16 0.03 0.04

Nainital Bank Ltd. 0.28 0.42 0.20 0.06

State Bank Of India 0.08 0.11 0.05 0.02 R B L Bank Ltd. 0.14 0.34 0.06 0.09

Syndicate Bank 0.07 0.15 0.02 0.04

South Indian Bank

Ltd. 0.09 0.12 0.05 0.02

Uco Bank 0.09 0.15 0.04 0.03 Yes Bank Ltd. 0.03 0.09 -0.01 0.03

Union Bank Of India 0.07 0.09 0.04 0.02

United Bank Of

India 0.10 0.15 0.08 0.02

Vijaya Bank 0.11 0.22 0.04 0.05

Mean 0.089 Mean 0.089

Result of Independent sample T-test --- T(37 ) = -0.028, P=0.978 (NSD)


Table A.24 Liquidity Analysis of Indian banks using Proxy L2 (Current Ratio) for time series data

from 2005-2017

Public banks Private banks


2005 2.749 6.320 1.130 1.185 2005 4.073 10.720 0.450 2.967

2006 2.953 4.920 1.530 0.819 2006 3.291 7.860 0.420 1.943

2007 3.605 5.950 1.730 1.095 2007 3.714 7.440 0.520 1.859

2008 3.266 5.500 1.520 0.981 2008 4.288 11.430 0.850 2.692

2009 3.966 6.720 2.020 1.305 2009 4.356 9.110 0.840 2.296

2010 3.524 5.990 1.700 1.078 2010 3.464 7.490 0.860 1.723

2011 3.841 8.250 1.530 1.680 2011 3.576 8.710 0.690 2.009

2012 3.781 8.230 1.960 1.398 2012 3.445 9.240 0.670 2.299

2013 3.789 7.130 2.050 1.396 2013 3.387 8.540 1.020 2.096

2014 3.923 8.390 1.700 1.657 2014 2.957 7.020 1.380 1.485

2015 4.308 7.730 2.130 1.476 2015 4.114 14.540 1.280 3.172

2016 5.206 8.690 2.330 1.669 2016 3.825 8.040 1.320 2.032

2017 5.946 9.630 2.760 2.036 2017 4.309 9.170 1.340 2.348

Mean 3.912 Mean 3.754

Result of Mann Whiteny U-test --- T(24 ) = -0.128, P=0.898 (NSD)


Table A.25: Liquidity Analysis of Indian banks using Proxy L2 (Current Ratio) for Cross-sectional

data from 2005-2017

Public Sector

Banks

Mean Max Min Std.

Dev


Allahabad Bank 3.611 4.770 2.390 0.687 Axis Bank 3.060 5.420 1.680 0.974

Andhra Bank 4.118 5.990 2.420 1.186

Catholic Syrian

Bank 6.335 8.900 4.370 1.310

Bank Of Baroda 6.247 9.630 2.640 2.254 City Union Bank 3.437 4.340 1.700 0.783

Bank Of India 4.638 8.000 2.300 1.603 D C B Bank 1.992 4.030 0.670 1.008

245

Bank Of

Maharashtra 3.420 6.610 2.260 1.222

Dhanalaxmi

Bank 4.893 6.430 3.470 0.987

Canara Bank 4.172 5.870 2.500 0.996 Federal Bank 5.389 10.720 2.110 2.195

Central Bank Of

India 3.239 7.660 1.810 1.521

H D F C Bank 1.475 2.150 0.660 0.450

Corporation Bank 3.202 4.470 1.530 0.990 I C I C I Bank 2.289 3.310 1.370 0.564

Dena Bank 5.300 8.240 2.900 1.651 Indusind Bank 2.799 4.490 1.280 1.177

I D B I Bank 3.068 6.690 1.130 1.488

Jammu &

Kashmir Bank 4.922 7.950 2.360 1.827

Indian Bank 2.932 3.780 1.940 0.561 Karnataka Bank 4.656 9.010 2.570 1.902

Indian Overseas

Bank 4.124 6.410 1.970 1.481

Karur Vysya

Bank 3.052 4.470 2.190 0.769

Oriental Bank Of

Commerce 4.267 8.710 2.710 1.681

Kotak Mahindra

Bank 1.077 2.450 0.420 0.609

Punjab & Sind

Bank 3.899 8.060 1.760 2.074

Lakshmi Vilas

Bank 3.304 5.360 1.580 0.888

Punjab National

Bank 3.698 7.800 1.390 1.558

Nainital Bank 8.132 14.540 4.470 2.420

State Bank Of

India 2.042 2.760 1.520 0.390

R B L Bank 4.991 11.430 2.690 2.654

Syndicate Bank 3.248 5.070 1.670 1.039

South Indian

Bank 4.164 6.590 2.250 1.287

Uco Bank 4.138 6.180 2.570 1.055 Yes Bank 1.602 3.140 0.530 0.705

Union Bank Of

India 3.427 5.000 2.120 0.823

United Bank Of

India 4.159 6.520 3.030 0.906

Vijaya Bank 5.205 8.690 2.230 1.929

Mean 3.91 Mean 3.75

Result of Mann Whitney U-test --- T(37 ) = -0.345, P=0.732 (NSD)


Table A.26: Profitability Analysis of Indian banks using Proxy P1 (EBIT/TA) for time series data from

2005-2017, N=273

Public sector Private sector


2005 0.06 0.08 0.04 0.01 2005 0.06 0.08 0.01 0.02

2006 0.06 0.09 0.05 0.01 2006 0.06 0.07 0.05 0.01

2007 0.06 0.07 0.06 0.01 2007 0.06 0.08 0.05 0.01

2008 0.07 0.08 0.06 0.01 2008 0.07 0.08 0.06 0.01

2009 0.07 0.09 0.06 0.01 2009 0.08 0.09 0.07 0.01

2010 0.07 0.08 0.06 0.01 2010 0.07 0.08 0.06 0.01

2011 0.07 0.08 0.06 0.01 2011 0.07 0.08 0.04 0.01

2012 0.08 0.09 0.07 0.01 2012 0.08 0.09 0.06 0.01

2013 0.08 0.09 0.06 0.01 2013 0.08 0.09 0.06 0.01

2014 0.08 0.10 0.06 0.01 2014 0.08 0.10 0.07 0.01

2015 0.08 0.10 0.06 0.01 2015 0.08 0.10 0.07 0.01

2016 0.08 0.09 0.06 0.01 2016 0.08 0.09 0.06 0.01

2017 0.07 0.09 0.06 0.01 2017 0.07 0.09 0.06 0.01

Mean 0.071 0.072

Result of Mann Whiteny U-test --- T(24 ) = 2.275, P=0.084 (NSD)

246


Table A.27: Profitability Analysis of Indian banks using Proxy P1 (EBIT/TA) for Cross-sectional data

from 2005-2017

Public Sector

Banks

Mean Max Min Std.

Dev



Andhra Bank

0.07 0.09 0.06 0.01

Catholic Syrian

Bank 0.07 0.08 0.06 0.01



Bank Of

Maharashtra 0.07 0.08 0.06 0.01

Dhanalaxmi Bank

0.07 0.08 0.05 0.01


Central Bank Of

India 0.07 0.08 0.06 0.01

H D F C Bank

0.07 0.08 0.06 0.01



I D B I Bank Ltd.

0.07 0.09 0.04 0.01

Jammu & Kashmir

Bank 0.07 0.08 0.06 0.01


Indian Overseas

Bank 0.07 0.08 0.06 0.01

Karur Vysya Bank

0.08 0.10 0.07 0.01

Oriental Bank Of

comm.. 0.08 0.09 0.06 0.01

Kotak Mahindra

Bank 0.07 0.09 0.05 0.01

Punjab & Sind Bank

0.07 0.09 0.05 0.01

Lakshmi Vilas

Bank 0.08 0.09 0.06 0.01

Punjab National

Bank 0.07 0.08 0.06 0.01

Nainital Bank

0.07 0.08 0.05 0.01

State Bank Of India 0.07 0.07 0.06 0.00 R B L Bank 0.06 0.07 0.04 0.01

Syndicate Bank 0.07 0.08 0.05 0.01 South Indian Bank 0.07 0.08 0.06 0.01

Uco Bank 0.07 0.08 0.06 0.01 Yes Bank 0.07 0.09 0.01 0.02

Union Bank Of

India 0.07 0.08 0.06 0.01

United Bank Of

India 0.07 0.08 0.06 0.01

Vijaya Bank 0.07 0.08 0.06 0.01

Mean 0.07 Mean 0.07

Result of Mann Whiteny U-test --- T(37 ) = 1.60, P=0.010 (NSD)


Table A.28: Profitability Analysis of Indian banks using Proxy P2 (ROA) for time series data from

2005-2017, N=273

Public Sector Private Sector


2005 0.911 2.010 -0.450 0.517 2005 0.464 1.560 -3.380 1.281

2006 0.847 1.420 0.160 0.394 2006 0.810 2.130 -2.010 0.912

2007 0.953 1.460 0.470 0.263 2007 0.923 1.570 0.170 0.473

2008 1.026 1.640 0.520 0.309 2008 1.109 1.670 0.340 0.425

2009 0.982 1.620 0.340 0.344 2009 1.093 1.960 -1.250 0.695

2010 0.970 1.670 0.450 0.344 2010 1.029 1.790 -1.300 0.788

247

2011 0.963 1.530 0.470 0.305 2011 1.156 1.770 0.140 0.553

2012 0.846 1.310 0.260 0.270 2012 1.226 1.830 -0.730 0.667

2013 0.720 1.070 0.240 0.244 2013 1.254 1.900 0.020 0.539

2014 0.389 0.780 -0.990 0.413 2014 1.103 2.000 -1.840 0.914

2015 0.350 0.680 -0.160 0.190 2015 -0.797 2.020 0.34 8.332

2016 -0.323 0.460 -1.250 0.555 2016 0.893 1.910 -1.610 0.919

2017 0.032 1.370 -1.210 0.597 2017 0.865 1.880 -2.040 0.917

Mean 0.666 0.856

Result of Mann Whiteny U-test --- T(24 ) = -1.9, P=0.044 (SD)


Table A.29: Profitability Analysis of Indian banks using Proxy P2 (ROA) for Cross-sectional data from

2005-2017

Public Sector

Banks

Mean Max Min Std.

Dev

Private Bank Mean Max Min Std

Dev

Allahabad Bank 0.802 1.420 -0.330 0.562 Axis Bank 1.452 1.830 0.650 0.351

Andhra Bank 0.961 1.590 0.080 0.514

Catholic Syrian

Bank -2.472 0.640 0.34 9.480

Bank Of Baroda 0.743 1.330 -0.780 0.552 City Union Bank 1.528 1.710 1.330 0.099

Bank Of India 0.552 1.490 -0.940 0.618 D C B Bank -0.038 1.370 -3.380 1.483

Bank Of

Maharashtra 0.402 0.760 -0.860 0.445

Dhanalaxmi Bank

-0.255 1.210 -1.840 1.002

Canara Bank 0.793 1.420 -0.520 0.513 Federal Bank 1.175 1.480 0.570 0.302

Central Bank Of

India 0.233 0.700 -0.800 0.493

H D F C Bank

1.642 2.020 1.280 0.277

Corporation Bank 0.851 1.290 -0.230 0.518 I C I C I Bank 1.395 1.860 0.980 0.275

Dena Bank 0.590 1.080 -1.020 0.579 Indusind Bank 1.251 1.910 0.220 0.650

I D B I Bank Ltd.

0.537 1.370 -1.090 0.550

Jammu & Kashmir

Bank 0.841 1.740 -2.040 0.959


Indian Overseas

Bank 0.486 1.360 -1.210 0.857

Karur Vysya Bank

1.377 1.760 0.860 0.323

Oriental Bank Of

Commerce 0.787 2.010 -0.460 0.621

Kotak Mahindra

Bank 1.527 1.980 0.940 0.353

Punjab & Sind

Bank 0.615 1.490 -0.450 0.525

Lakshmi Vilas

Bank 0.540 0.910 0.080 0.238

Punjab National

Bank 0.885 1.440 -0.610 0.575

Nainital Bank

1.348 1.750 0.730 0.338

State Bank Of

India 0.801 1.040 0.410 0.205

R B L Bank

0.789 1.960 -1.170 0.763

Syndicate Bank 0.655 1.070 -0.560 0.431 South Indian Bank 0.822 1.170 0.090 0.328

Uco Bank 0.337 0.870 -1.250 0.621 Yes Bank 1.525 2.130 -0.290 0.573

Union Bank Of

India 0.828 1.270 0.130 0.367

United Bank Of

India 0.369 1.040 -0.990 0.519

Vijaya Bank 0.640 1.430 0.280 0.304

Mean 0.667 Mean 0.855

Result of Mann Whiteny U-Test--- T(37 ) = -2.5, P=0.010 (SD)


248

Table A.30: Profitability Analysis of Indian banks using Proxy P3 (PBIT/CL) for time series data from

2005-2017, N=273

Public Sector Private Sector


2005 1.425 2.272 0.500 0.461 2005 1.554 2.941 0.333 0.686

2006 1.499 2.262 0.641 0.350 2006 1.544 3.060 0.530 0.616

2007 1.647 2.487 0.765 0.427 2007 1.590 2.850 0.600 0.626

2008 0.083 0.091 0.071 0.005 2008 0.091 0.110 0.079 0.008

2009 2.194 3.405 0.767 0.590 2009 2.167 3.580 0.920 0.669

2010 2.178 3.205 0.841 0.477 2010 1.982 3.030 0.730 0.701

2011 2.260 3.574 0.715 0.591 2011 1.985 3.060 0.610 0.650

2012 2.894 3.963 1.194 0.657 2012 2.573 3.940 0.680 0.927

2013 2.997 4.250 1.146 0.794 2013 2.838 4.250 0.900 0.983

2014 2.890 4.245 1.302 0.756 2014 2.593 4.160 0.920 0.994

2015 2.781 4.013 1.050 0.719 2015 2.675 3.850 1.360 0.914

2016 3.079 5.679 0.991 0.962 2016 2.530 4.520 1.160 1.026

2017 2.894 6.380 1.101 1.032 2017 2.551 4.550 1.110 1.083

Mean 2.217 2.052

Result of Independent Sample T-test--- T(24 ) = -0.521, P=0.607 (NSD)


Table A.31: Profitability Analysis of Indian banks using Proxy P3 (PBIT/CL) for Cross-sectional data

from 2005-2017

Public Sector

Banks

Mean Max Min Std.

Dev


Allahabad Bank 2.663 4.101 0.088 1.015

Axis Bank 1.891 2.519 0.080 0.651

Andhra Bank

2.635 4.235 0.086 1.304

Catholic Syrian

Bank 2.744 4.550 0.090 1.267

Bank Of Baroda 1.707 2.548 0.077 0.617

City Union Bank 2.463 3.940 0.090 1.115


Bank Of

Maharashtra 2.076 3.239 0.082 0.760

Dhanalaxmi

Bank 2.312 4.250 0.090 1.105

Canara Bank 2.410 3.411 0.091 0.897

Federal Bank 2.772 3.600 0.090 0.860

Central Bank Of

India 1.830 2.428 0.071 0.603

H D F C Bank

0.808 1.490 0.100 0.370

Corporation

Bank 2.214 3.912 0.080 1.140

I C I C I Bank

1.308 1.840 0.100 0.499

Dena Bank 2.582 4.125 0.085 1.203

Indusind Bank 1.977 3.170 0.090 0.777

I D B I Bank

Ltd. 2.079 3.381 0.076 0.970

Jammu &

Kashmir Bank 2.474 3.740 0.080 0.936

Indian Bank 1.893 2.716 0.091 0.711

Karnataka Bank 2.951 3.800 0.090 0.963

Indian Overseas

Bank 2.442 4.005 0.087 1.123

Karur Vysya

Bank 2.859 4.160 0.090 1.276

249

Oriental Bank

Of Commerce 2.670 3.999 0.084 1.088

Kotak Mahindra

Bank 1.368 2.560 0.110 0.743

Punjab & Sind

Bank 2.950 6.380 0.085 1.672

Lakshmi Vilas

Bank 2.351 3.510 0.090 0.952

Punjab National

Bank 2.002 3.127 0.082 0.916

Nainital Bank

1.667 3.241 0.088 0.769

State Bank Of

India 0.865 1.302 0.081 0.321

R B L Bank

1.678 3.242 0.079 0.870

Syndicate Bank

2.057 3.160 0.083 0.832

South Indian

Bank 2.609 4.000 0.080 1.099

Uco Bank 2.248 3.301 0.083 0.755 Yes Bank 1.269 1.850 0.100 0.535

Union Bank Of

India 2.415 3.778 0.085 1.063

United Bank Of

India 1.995 3.072 0.079 0.787

Vijaya Bank 2.751 4.250 0.079 1.325

Mean 2.21 Mean 2.5

Result of Independent sample T-test --- T(37 ) = -0.929, P=0.359 (NSD)


Table A.32: Profitability Analysis of Indian banks using Proxy P4 (TI/TA) for time series data from

2005-2017, N=273

Public sector-Profitability- N=273 –TI/TA Private Sector- Profitability- TI/TA


2005 0.083 0.097 0.044 0.011 2005 0.080 0.097 0.038 0.014

2006 0.081 0.090 0.073 0.005 2006 0.082 0.099 0.069 0.008

2007 0.080 0.091 0.073 0.005 2007 0.083 0.093 0.071 0.007

2008 0.083 0.091 0.071 0.005 2008 0.092 0.111 0.079 0.008

2009 0.086 0.096 0.077 0.006 2009 0.100 0.129 0.087 0.012

2010 0.080 0.090 0.072 0.005 2010 0.089 0.106 0.076 0.008

2011 0.079 0.088 0.069 0.005 2011 0.085 0.099 0.066 0.008

2012 0.090 0.098 0.075 0.005 2012 0.097 0.110 0.074 0.009

2013 0.090 0.098 0.071 0.006 2013 0.100 0.114 0.078 0.009

2014 0.088 0.098 0.066 0.007 2014 0.102 0.117 0.086 0.009

2015 0.091 0.098 0.067 0.008 2015 0.102 0.114 0.087 0.007

2016 0.089 0.099 0.075 0.007 2016 0.098 0.107 0.083 0.006

2017 0.086 0.094 0.071 0.006 2017 0.095 0.104 0.080 0.007

Mean 0.085 0.093

Result of Independent sample T-test --- T(24 ) = -3.01, P=0.006 (SD)


Table A.33: Profitability Analysis of Indian banks using Proxy P4 (TI/TA) for Cross-sectional data

from 2005-2017

Public Sector

Banks

Mean Max Min Std.

Dev



Andhra Bank

0.090 0.077 0.098 0.007

Catholic Syrian

Bank 0.096 0.108 0.085 0.009



250

Bank Of

Maharashtra 0.086 0.077 0.095 0.005

Dhanalaxmi Bank

0.090 0.105 0.074 0.011


Central Bank Of

India 0.084 0.071 0.099 0.008

H D F C Bank

0.094 0.108 0.074 0.010



I D B I Bank Ltd. 0.080 0.044 0.09 0.013 Jammu & Kashmir

Bank

0.084 0.101 0.067 0.010


Indian Overseas

Bank 0.090 0.075 0.098 0.006

Karur Vysya Bank

0.098 0.114 0.088 0.009

Oriental Bank Of

Commerce 0.088 0.076 0.097 0.007

Kotak Mahindra

Bank 0.105 0.127 0.086 0.012

Punjab & Sind

Bank 0.088 0.076 0.097 0.007

Lakshmi Vilas

Bank 0.095 0.112 0.079 0.010

Punjab National

Bank 0.086 0.079 0.098 0.005

Nainital Bank

0.087 0.099 0.078 0.007

State Bank Of

India 0.084 0.078 0.091 0.004

R B L Bank

0.080 0.092 0.066 0.008


Uco Bank 0.081 0.075 0.09 0.005 Yes Bank 0.088 0.107 0.038 0.020

Union Bank Of

India 0.085 0.075 0.093 0.005

United Bank Of

India 0.087 0.076 0.098 0.008

Vijaya Bank 0.086 0.077 0.096 0.006

Mean 0.084 Mean 0.092



Table A.34: Leverage Analysis of Indian banks using Proxy LV1 (RE/TA) for time series data from

2005-2017, N=273

Leverage Variable (RE/TA)- of public sector

banks

Leverage Variable (RE/TA)- of public sector banks

Year

Mean Max Min S.D. Year Mean Max Min S.D.

2005 0.04 0.09 -0.06 0.03 2005 0.05 0.10 0.01 0.02

2006 0.05 0.48 0.00 0.10 2006 0.53 3.72 0.02 0.99

2007 0.05 0.07 0.02 0.02 2007 0.06 0.09 0.03 0.02

2008 0.05 0.07 0.02 0.01 2008 0.08 0.15 0.04 0.03

2009 0.05 0.08 0.02 0.01 2009 0.08 0.14 0.05 0.03

2010 0.05 0.07 0.03 0.01 2010 0.08 0.14 0.05 0.03

2011 0.05 0.07 0.03 0.01 2011 0.09 0.27 0.05 0.05

2012 0.05 0.07 0.03 0.01 2012 0.08 0.13 0.04 0.03

2013 0.05 0.07 0.04 0.01 2013 0.08 0.12 0.05 0.02

2014 0.05 0.07 0.03 0.01 2014 0.08 0.14 0.04 0.03

2015 0.05 0.08 0.04 0.01 2015 0.08 0.13 0.04 0.02

2016 0.05 0.08 0.04 0.01 2016 0.08 0.12 0.03 0.03

2017 0.05 0.08 0.04 0.01 2017 0.08 0.13 0.04 0.03

Mean 0.049 0.111



251

Table A.35: Leverage Analysis of Indian banks using Proxy LV1 (RE/TA) for Cross-sectional data

from 2005-2017

Public Sector

Banks

Mean Max Min Std.

Dev



Andhra Bank

0.05 0.06 0.04 0.01

Catholic Syrian

Bank 0.08 0.48 0.04 0.12



Bank Of

Maharashtra 0.04 0.05 0.03 0.01

Dhanalaxmi Bank

0.07 0.41 0.03 0.10


Central Bank Of

India 0.04 0.05 0.01 0.01

H D F C Bank

0.09 0.10 0.07 0.01



I D B I Bank Ltd.

0.06 0.07 0.04 0.01

Jammu & Kashmir

Bank 0.07 0.08 0.04 0.01

Indian Bank 0.06 0.08 -0.06 0.04 Karnataka Bank 0.07 0.08 0.06 0.01

Indian Overseas

Bank 0.05 0.06 0.01 0.01

Karur Vysya Bank

0.08 0.09 0.06 0.01

Oriental Bank Of

Commerce 0.06 0.07 0.01 0.02

Kotak Mahindra

Bank 0.11 0.14 0.03 0.03

Punjab & Sind

Bank 0.08 0.48 0.01 0.12

Lakshmi Vilas

Bank 0.05 0.06 0.02 0.01

Punjab National

Bank 0.06 0.07 0.00 0.02

Nainital Bank

0.35 3.72 0.06 1.01



Uco Bank 0.04 0.07 0.02 0.01 Yes Bank 0.06 0.10 0.01 0.02

Union Bank Of

India 0.05 0.06 0.00 0.01

United Bank Of

India 0.03 0.04 -0.01 0.01

Vijaya Bank 0.04 0.05 0.01 0.01

0.050 Mean 0.110



Table A.36 Leverage Analysis of Indian banks using Proxy LV2 (MVE/TL) for time series data from

2005-2017, N=273

Public Sector-Efficiency- Private Sector-


2005 0.07 0.15 0.01 0.03 2005 0.12 0.64 0.01 0.15

2006 0.07 0.14 0.02 0.03 2006 0.17 0.85 0.01 0.23

2007 0.09 0.69 0.01 0.14 2007 0.14 0.56 0.02 0.14

2008 0.06 0.14 0.01 0.03 2008 0.17 0.77 0.03 0.17

2009 0.03 0.07 0.01 0.02 2009 0.08 0.34 0.01 0.08

2010 0.05 0.13 0.02 0.03 2010 0.17 0.7 0.01 0.17

2011 0.07 0.14 0.04 0.03 2011 0.17 0.66 0.05 0.16

2012 0.06 0.16 0.02 0.04 2012 0.17 0.68 0.05 0.16

252

2013 0.07 0.45 0.03 0.09 2013 0.15 0.48 0.03 0.12

2014 0.03 0.08 0.01 0.01 2014 0.15 0.68 0.03 0.17

2015 0.03 0.10 0.02 0.02 2015 0.24 0.96 0.04 0.25

2016 0.02 0.07 0.01 0.01 2016 0.16 0.65 0.03 0.16

2017 0.03 0.09 0.02 0.02 2017 0.22 0.75 0.05 0.19

Mean 0.052 0.162

Result of Mann Whiteny U-test --- T(24 ) = -0.4.313, P=0.00(SD)


Table A.37: Leverage Analysis of Indian banks using Proxy LV2 (MVE/TL) for Cross-sectional data

from 2005-2017

Public Sector

Banks

Mean Max Min Std.

Dev


Dev


Andhra Bank

0.06 0.13 0.02 0.04

Catholic Syrian

Bank 0.05 0.08 0.01 0.02



Bank Of

Maharashtra 0.03 0.05 0.02 0.01

Dhanalaxmi Bank

0.06 0.11 0.03 0.03


Central Bank Of

India 0.04 0.09 0.01 0.02

H D F C Bank

0.37 0.48 0.23 0.06



I D B I Bank Ltd. 0.04 0.07 0.02 0.02

Jammu & Kashmir

Bank

0.07 0.10 0.04 0.02


Indian Overseas

Bank 0.05 0.09 0.02 0.03

Karur Vysya Bank

0.11 0.16 0.06 0.03

Oriental Bank Of

Commerce 0.05 0.11 0.01 0.03

Kotak Mahindra

Bank 0.64 0.96 0.07 0.22

Punjab & Sind Bank 0.03 0.06 0.01 0.01

Lakshmi Vilas Bank 0.07 0.09 0.03 0.02

Punjab National

Bank 0.08 0.13 0.03 0.03

Nainital Bank

0.05 0.09 0.03 0.02

State Bank Of India 0.11 0.16 0.07 0.03

R B L Bank 0.12 0.65 0.01 0.19


Uco Bank 0.03 0.05 0.01 0.01 Yes Bank 0.24 0.65 0.07 0.15

Union Bank Of

India 0.05 0.08 0.02 0.02

United Bank Of

India 0.03 0.06 0.01 0.01

Vijaya Bank 0.04 0.10 0.02 0.02

Mean 0.053 Mean 0.162

Result of Mann Whiteny U -Test T(37 ) = -3.954, P=0.000 (SD)


253

Table A.38 Leverage Analysis of Indian banks using Proxy LV3 (TD/TA) for time series data from

2005-2017, N=273

Public sector-Leverage- N=273 –TD/TA Private Sector-Leverage TD/TA


2005 0.881 0.972 0.900 0.018 2005 0.957 0.978 0.899 0.021

2006 0.896 0.974 0.897 0.016 2006 0.954 0.978 0.893 0.022

2007 0.919 0.973 0.923 0.012 2007 0.954 0.974 0.886 0.022

2008 0.919 0.979 0.914 0.014 2008 0.954 0.971 0.902 0.018

2009 0.915 0.979 0.917 0.012 2009 0.958 0.976 0.911 0.018

2010 0.947 0.980 0.924 0.011 2010 0.959 0.975 0.907 0.019

2011 0.969 0.982 0.914 0.013 2011 0.962 0.981 0.895 0.019

2012 0.972 0.980 0.939 0.009 2012 0.963 0.983 0.889 0.024

2013 0.972 0.982 0.940 0.009 2013 0.966 0.981 0.913 0.018

2014 0.973 0.983 0.948 0.007 2014 0.963 0.979 0.916 0.017

2015 0.971 0.980 0.935 0.009 2015 0.965 0.979 0.943 0.013

2016 0.973 0.986 0.934 0.011 2016 0.965 0.982 0.935 0.013

2017 0.973 0.988 0.944 0.009 2017 0.965 0.983 0.934 0.015

Mean 0.944 0.960



Table A.39: Leverage Analysis of Indian banks using Proxy LV3 (TD/TA) for Cross-sectional data

from 2005-2017

Public Sector

Banks

Mean Max Min Std.

Dev


Dev


Andhra Bank

0.97 0.98 0.94 0.01

Catholic Syrian

Bank 0.97 0.98 0.96 0.01



Bank Of

Maharashtra 0.97 0.98 0.95 0.01

Dhanalaxmi Bank

0.97 0.98 0.96 0.01


Central Bank Of

India 0.97 0.97 0.96 0.01

H D F C Bank

0.91 0.95 0.89 0.02



I D B I Bank Ltd.

0.96 0.98 0.92 0.02

Jammu & Kashmir

Bank 0.97 0.98 0.97 0.00


Indian Overseas

Bank 0.97 0.98 0.94 0.02

Karur Vysya Bank

0.97 0.98 0.95 0.01

Oriental Bank Of

Commerce 0.97 0.98 0.96 0.01

Kotak Mahindra

Bank 0.94 0.97 0.91 0.02

Punjab & Sind

Bank 0.98 0.99 0.95 0.01

Lakshmi Vilas

Bank 0.97 0.98 0.95 0.01

Punjab National

Bank 0.96 0.98 0.92 0.02

Nainital Bank

0.96 0.98 0.94 0.01

254



Uco Bank 0.97 0.98 0.97 0.00 Yes Bank 0.95 0.98 0.92 0.02

Union Bank Of

India 0.97 0.98 0.96 0.01

United Bank Of

India 0.97 0.98 0.95 0.01

Vijaya Bank 0.97 0.98 0.96 0.01

Mean 0.943 Mean 0.960



Table A.40: Result of Altman Z-score of 21 public banks for 2005-2017

Sr.

No Public Banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Avg Result

1 Allahabad Bank

(2005) 1.07 1.34 1.02 1.00 1.09 1.12 1.07 1.23 1.19 1.11 1.19 1.29 1.27 1.15 Grey

2 Andhra Bank 1.22 1.25 1.11 1.30 1.20 1.52 1.47 1.37 1.21 1.31 1.53 1.39 1.52 1.34 Grey

3 Bank Of

Baroda 1.16 1.33 1.07 1.60 1.52 1.47 1.97 1.98 1.73 1.91 1.31 1.52 1.64 1.55 Grey

4 Bank Of India 0.86 1.20 1.34 1.24 1.33 1.41 1.23 1.36 1.36 1.29 1.33 1.27 1.43 1.28 Grey

5 Bank Of

Maharashtra 1.03 1.03 0.99 1.03 0.98 1.17 0.85 1.02 1.19 1.04 1.22 1.30 1.71 1.12 Grey

6 Canara Bank 1.06 1.18 1.75 1.24 1.25 1.24 1.37 1.34 1.32 1.35 1.40 1.50 1.51 1.35 Grey

7 Central Bank

Of India 1.19 0.99 1.15 1.11 0.98 1.17 0.98 0.98 1.00 0.87 1.03 1.08 1.92 1.11 Grey

8 Corporation

Bank 1.27 1.31 1.11 1.39 1.51 1.12 0.73 1.20 1.24 1.19 1.11 1.23 1.48 1.22 Grey

9 Dena Bank 1.06 1.28 1.57 1.62 1.96 1.28 1.20 1.18 1.86 1.15 1.43 1.18 1.29 1.39 Grey

10 I D B I Bank 0.61 1.07 1.06 1.07 1.04 0.81 1.19 1.19 1.06 1.06 1.21 1.34 1.24 1.07 Distres

s

11 Indian Bank 0.59 1.26 1.14 1.44 1.27 1.28 1.17 1.16 1.22 1.14 1.33 1.29 1.20 1.19 Grey

12 Indian Overseas

Bank 1.20 1.03 1.03 1.10 1.39 1.25 1.54 1.32 1.27 1.28 1.43 1.30 1.37 1.27 Grey

13 Oriental Bank

Of Commerce 1.66 1.32 1.25 1.30 1.40 1.34 1.15 1.14 1.08 1.27 1.29 1.35 1.67 1.32 Grey

14 Punjab & Sind

Bank 1.11 1.29 1.52 0.87 1.01 0.82 0.80 0.94 1.08 1.10 1.18 1.29 1.29 1.10

Distres

s

15 Punjab National

Bank 0.90 1.73 1.37 1.28 1.25 1.27 1.19 1.28 1.16 1.27 1.20 1.43 1.69 1.31 Grey

16 State Bank Of

India 1.28 1.15 0.88 1.09 1.25 1.11 1.41 1.24 1.23 1.05 1.45 1.37 1.38 1.22 Grey

17 Syndicate Bank 0.77 1.02 1.55 1.33 1.33 0.87 0.99 1.10 1.08 0.77 0.97 1.10 1.03 1.07 Grey

18 Uco Bank 1.35 0.80 1.01 1.14 1.27 0.99 1.52 1.28 1.18 1.00 1.41 1.14 1.13 1.17 Grey

19 Union Bank Of

India 1.16 0.92 0.98 1.20 1.28 1.16 1.22 1.03 0.99 1.08 1.16 1.23 1.12 1.04

Distres

s

20 United Bank Of

India 1.10 1.07 1.23 1.33 1.32 1.11 1.18 1.23 1.28 1.32 1.32 1.37 1.64 1.18 Grey

21 Vijaya Bank 0.94 1.13 1.45 1.77 2.14 1.03 1.08 1.21 1.11 1.31 1.38 1.67 1.46 1.25 Grey

Source: Authors Calculations based on the data from CMIE

255

Table A.41 Result of Altman Z- score of 18 Private Banks for 2005-2017

Sr.

No.

Private

Banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Avg Mean

1 Axis Bank 1.46 0.97 1.38 1.67 1.47 1.51 1.35 1.28 1.33 1.37 2.00 2.18 2.45 1.57 Grey

2 Catholic

Syrian Bank 1.93 1.41 1.64 1.75 2.18 1.62 1.41 1.64 1.71 1.23 1.46 1.44 1.66 1.62 Grey

3 City Union

Bank 1.20 1.05 1.11 1.53 1.45 1.48 1.44 1.35 1.55 1.58 1.84 1.67 1.74 1.46 Grey

4 D C B Bank 0.93 0.92 1.24 1.48 1.74 1.09 0.74 0.72 0.81 0.97 1.65 1.34 1.74 1.18 Grey

5 Dhanlaxmi

Bank 1.31 1.17 1.22 1.68 1.65 1.27 1.16 1.21 1.42 1.33 1.98 1.67 1.72 1.44 Grey

6 Federal Bank 2.02 1.54 1.22 1.76 1.63 1.23 1.72 1.53 1.46 1.56 1.56 1.55 1.83 1.58 Grey

7 H D F C Bank 0.77 0.94 1.04 1.80 1.27 1.54 1.46 1.25 1.47 1.65 1.45 1.90 1.58 1.40 Grey

8 I C I C I Bank 1.00 1.06 1.23 1.62 1.62 1.89 1.61 1.57 1.72 1.60 1.66 1.78 1.84 1.55 Grey

9 Indusind

Bank 1.09 1.25 1.40 1.38 1.59 1.39 1.79 1.27 1.60 1.34 1.41 1.58 1.87 1.46 Grey

10 Jammu &

Kashmir Bank 1.39 1.42 1.76 2.34 1.97 1.73 1.25 1.24 1.27 1.06 1.11 1.32 1.50 1.49 Grey

11 Karnataka

Bank 2.43 1.74 1.15 1.45 1.28 1.19 1.14 1.14 1.16 1.08 1.50 1.48 1.38 1.39 Grey

12 Karur Vysya

Bank 1.31 1.44 1.12 1.20 1.42 1.21 1.26 1.46 1.23 1.08 1.22 1.34 1.39 1.28 Grey

13

Kotak

Mahindra

Bank

1.11 1.15 0.32 1.60 1.25 1.53 1.46 1.64 1.02 1.86 2.13 1.85 2.06 1.46 Grey

14 Lakshmi

Vilas Bank 1.71 1.30 1.83 1.41 1.44 1.26 0.91 1.24 1.25 1.24 1.20 1.14 1.05 1.31 Grey

15 Nainital Bank 1.95 1.96 2.10 3.48 2.56 2.56 2.96 2.32 3.04 2.62 2.90 3.50 2.51 2.65 Safe

16 R B L Bank 1.50 1.54 1.93 3.20 2.78 1.66 1.84 1.55 1.26 1.26 1.33 1.10 1.69 1.74 Grey

17 South Indian

Bank 0.93 1.26 1.37 1.26 1.12 1.24 1.32 1.17 1.24 1.21 1.55 1.51 1.38 1.27 Grey

18 Yes Bank 0.12 1.23 1.60 1.09 1.17 1.09 0.97 0.94 1.11 1.48 1.31 1.7 1.06 Distress

Source: Authors calculations based on data from CMIE

Table A.42 Result of Altman Z-score of five NWB from 2005-2013

Sr.

No. Banks 2005 2006 2007 2008 2009 2010 2011 2012 2013 Avg Result

1 Bank Of Punjab Ltd.

[Merged] 1.42 ---- ----- ----- ---- ---- ----- ----- ---- 1.42 Grey

2 Bank Of Rajasthan

Ltd. [Merged] 2.04 2.50 1.50 1.71 1.24 0.84 ---- ----- ----- 1.52 Grey

3 Bharat Overseas

Bank Ltd. [Merged] 1.29 1.49 ---- ----- ----- ---- ---- ----- ----- 1.39 Grey

4 Centurion Bank Of

Punjab Ltd. [Merged] 1.15 1.38 0.57 ---- ----- ----- ---- ---- ----- 1.03 Distress

5 I N G Vysya Bank

Ltd. [Merged] 0.90 0.81 0.89 1.49 1.10 1.21 1.09 1.23 1.26 1.31 Grey


Table A.43: Result of Springate Model of 21 public banks for 2005-2017

Sr.

No. Bank 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Avg Result

1 Allahabad

Bank 1.82 1.50 1.97 2.00 1.99 2.03 2.04 2.99 3.08 2.60 2.23 2.49 2.31 2.23 Safe

256

2 Andhra Bank 1.21 0.95 1.47 1.57 2.00 2.11 2.40 2.83 3.15 3.20 2.71 2.90 2.16 2.20 Safe

3 Bank Of

Baroda 1.15 1.21 1.31 1.44 1.51 1.64 1.90 2.15 1.94 1.83 1.69 1.71 1.77 1.64 Safe

4 Bank Of India 1.31 1.31 1.36 1.63 2.09 1.77 1.37 1.92 2.31 1.95 2.20 2.26 2.21 1.82 Safe

5 Bank Of

Maharashtra 1.53 1.47 1.43 1.25 1.64 1.75 1.49 1.77 2.12 2.25 2.00 2.53 1.99 1.79 Safe

6 Canara Bank 1.36 1.33 1.65 1.65 2.07 2.21 2.35 2.64 2.35 2.38 2.24 2.33 2.22 2.06 Safe

7 Central Bank

Of India 1.32 1.44 1.45 1.31 1.53 1.82 1.61 1.78 1.92 1.61 1.52 1.68 2.08 1.62 Safe

8 Corporation

Bank 0.95 1.07 1.02 1.14 1.76 1.59 1.97 2.26 2.45 2.56 2.57 2.98 2.05 1.87 Safe

9 Dena Bank 1.34 1.54 1.44 1.56 1.73 1.57 2.70 2.66 3.13 2.79 2.57 2.81 2.58 2.19 Safe

10 I D B I Bank 0.48 1.44 1.36 1.52 1.62 1.60 2.19 2.60 2.30 2.20 2.28 2.16 1.75 1.81 Safe

11 Indian Bank 1.13 1.34 1.23 1.64 1.59 1.62 1.73 1.89 1.76 1.86 2.04 2.16 2.01 1.69 Safe

12 Indian

Overseas Bank 1.42 1.23 1.01 1.11 2.31 1.98 1.89 2.35 2.64 2.66 3.07 2.46 2.46 2.05 Safe

13 Oriental Bank

Of Commerce 1.90 1.53 1.86 1.57 1.88 2.00 1.67 2.64 2.95 2.59 2.42 2.91 3.10 2.23 Safe

14 Punjab & Sind

Bank 1.15 1.46 1.95 2.34 2.57 2.38 1.76 2.27 2.11 2.39 2.65 4.14 4.60 2.44 Safe

15 Punjab

National Bank 0.76 1.17 1.41 1.33 1.58 1.65 1.68 2.05 2.09 2.16 2.04 2.41 2.51 1.76 Safe

16 State Bank Of

India 0.79 0.74 0.79 0.78 0.85 0.86 0.83 1.12 1.09 1.16 1.07 1.02 1.08 0.94 Safe

17 Syndicate Bank 1.06 1.13 1.52 1.69 1.88 1.76 1.81 2.26 1.90 1.56 1.95 2.24 2.42 1.78 Safe

18 Uco Bank 1.52 1.76 1.77 2.14 2.57 2.02 1.74 2.23 2.12 2.01 2.01 1.98 1.83 1.98 Safe

19 Union Bank Of

India 1.36 1.21 1.47 1.78 1.95 1.90 1.58 2.11 2.44 2.67 2.50 2.87 2.64 2.04 Safe

20 United Bank

Of India 1.34 1.34 1.38 1.39 1.41 1.59 1.69 2.40 2.22 2.03 1.91 2.05 2.28 1.77 Safe

21 Vijaya Bank 1.57 1.52 1.47 1.60 1.97 1.50 2.04 2.91 3.16 3.20 3.07 3.04 2.86 2.30 Safe


Table A.44: Result of Springate Model of 18 private banks for 2005-2017

Sr.

No.

Bank (ZM –

Score) 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Avg Result

1 Axis Bank 1.52 1.09 1.52 1.46 1.99 1.65 1.54 1.99 1.99 1.82 1.90 1.70 1.49 1.67 Safe

2 Catholic

Syrian Bank 2.21 1.55 1.54 1.57 1.95 1.82 1.94 2.34 2.71 3.00 2.84 3.40 3.45 2.33 Safe

3 City Union

Bank 1.66 1.13 1.25 1.51 1.98 1.94 2.00 3.00 2.94 2.91 2.35 2.46 2.32 2.11 Safe

4 D C B Bank 0.97 1.22 1.33 1.59 1.92 1.33 1.14 1.31 1.57 1.39 1.36 1.06 1.18 1.34 Safe

5 Dhanlaxmi

Bank 1.25 1.31 1.49 1.50 1.94 1.60 2.09 2.05 3.23 1.83 2.77 1.95 2.82 1.99 Safe

6 Federal Bank 2.47 2.04 2.10 1.67 2.50 2.23 2.15 2.44 2.42 2.60 2.87 2.47 2.46 2.34 Safe

7 H D F C Bank

0.52 0.55 0.67 0.87 0.95 0.80 0.70 0.73 0.91 0.98 1.25 1.31 0.84 0.85 Distres

s

8 I C I C I Bank 0.77 0.90 1.17 1.37 1.63 1.59 1.43 1.02 1.17 1.21 1.40 1.44 1.57 1.28 Safe

9 Indusind Bank 1.63 1.68 1.67 1.76 1.92 1.64 1.72 2.21 2.45 2.30 1.31 1.40 1.41 1.78 Safe

10 Jammu &

Kashmir Bank 1.63 1.59 1.86 1.94 2.24 2.07 2.11 2.19 2.74 2.55 2.81 2.15 1.82 2.13 Safe

11 Karnataka 1.99 2.46 2.26 2.16 2.76 2.35 2.34 2.71 2.87 2.36 2.52 2.83 2.60 2.48 Safe

257

Bank

12 Karur Vysya

Bank 1.36 1.53 1.60 1.75 2.08 2.29 1.96 2.88 3.06 3.11 2.92 3.08 3.03 2.36 Safe

13

Kotak

Mahindra

Bank

0.64 0.63 0.63 0.84 0.97 0.90 1.00 1.71 2.00 1.89 1.53 1.37 1.58 1.21 Safe

14 Lakshmi Vilas

Bank 1.62 1.39 1.67 1.40 1.58 2.03 1.58 2.30 2.45 2.62 2.32 2.45 2.64 2.00 Safe


16 R B L Bank 1.32 1.21 0.98 1.90 2.11 0.93 1.10 2.48 2.18 1.50 1.81 1.60 1.66 1.60 Safe

17 South Indian

Bank 1.45 1.39 1.61 1.70 2.13 2.08 1.90 2.33 2.58 2.57 2.79 3.02 3.03 2.20 Safe

18 Yes Bank 0.24 1.40 0.77 1.71 1.34 1.22 1.33 1.03 1.31 1.39 1.45 1.44 1.33 1.23 Safe


Table A.45: Result of Springate Model NWB banks from 2005-2014

Sr.

No Bank (ZM –Score) 2005 2006 2007 2008 2009 2010 2011 Avg Result

1 Bank Of Punjab Ltd. [Merged] 1.42 ---- ----- ----- ---- ---- ----- 1.42 Safe

2 Bank Of Rajasthan Ltd. [Merged] 1.20 1.19 1.25 1.30 1.40 0.93 1.14 Safe

3 Bharat Overseas Bank Ltd. [Merged] 1.19 1.24 ---- ----- ----- ---- 1.81 Safe

4 Centurion Bank Of Punjab Ltd.

[Merged] 1.04 0.99 0.73 ---- ----- ----- ---- 0.92 Safe

5 I N G Vysya Bank Ltd. [Merged] 0.97 0.97 0.86 0.94 0.98 1.01 1.08 1.14 Safe


TableA.46: Result of Zmijewski model for 21 public banks from 2005-2017

Sr.


1 Allahabad Bank 1.82 1.50 1.97 2.00 1.99 2.03 2.04 2.99 3.08 2.60 2.23 2.49 2.31 2.23 Safe

2 Andhra Bank 1.21 0.95 1.47 1.57 2.00 2.11 2.40 2.83 3.15 3.20 2.71 2.90 2.16 2.20 Safe

3 Bank Of Baroda 1.15 1.21 1.31 1.44 1.51 1.64 1.90 2.15 1.94 1.83 1.69 1.71 1.77 1.64 Safe


5 Bank Of


6 Canara Bank 1.36 1.33 1.65 1.65 2.07 2.21 2.35 2.64 2.35 2.38 2.24 2.33 2.22 2.06 Safe

7 Central Bank Of

India 1.32 1.44 1.45 1.31 1.53 1.82 1.61 1.78 1.92 1.61 1.52 1.68 2.08 1.62 Safe

8 Corporation

Bank 0.95 1.07 1.02 1.14 1.76 1.59 1.97 2.26 2.45 2.56 2.57 2.98 2.05 1.87 Safe

9 Dena Bank 1.34 1.54 1.44 1.56 1.73 1.57 2.70 2.66 3.13 2.79 2.57 2.81 2.58 2.19 Safe

10 I D B I Bank

Ltd. 0.48 1.44 1.36 1.52 1.62 1.60 2.19 2.60 2.30 2.20 2.28 2.16 1.75 1.81 Safe

11 Indian Bank 1.13 1.34 1.23 1.64 1.59 1.62 1.73 1.89 1.76 1.86 2.04 2.16 2.01 1.69 Safe

12 Indian Overseas

Bank 1.42 1.23 1.01 1.11 2.31 1.98 1.89 2.35 2.64 2.66 3.07 2.46 2.46 2.05 Safe

13 Oriental Bank Of

Commerce 1.90 1.53 1.86 1.57 1.88 2.00 1.67 2.64 2.95 2.59 2.42 2.91 3.10 2.23 Safe

14 Punjab & Sind

Bank 1.15 1.46 1.95 2.34 2.57 2.38 1.76 2.27 2.11 2.39 2.65 4.14 4.60 2.44 Safe

15 Punjab National

Bank 0.76 1.17 1.41 1.33 1.58 1.65 1.68 2.05 2.09 2.16 2.04 2.41 2.51 1.76 Safe

258

16 State Bank Of

India 0.79 0.74 0.79 0.78 0.85 0.86 0.83 1.12 1.09 1.16 1.07 1.02 1.08 0.94 Safe


18 Uco Bank 1.52 1.76 1.77 2.14 2.57 2.02 1.74 2.23 2.12 2.01 2.01 1.98 1.83 1.98 Safe

19 Union Bank Of

India 1.36 1.21 1.47 1.78 1.95 1.90 1.58 2.11 2.44 2.67 2.50 2.87 2.64 2.04 Safe

20 United Bank Of

India 1.34 1.34 1.38 1.39 1.41 1.59 1.69 2.40 2.22 2.03 1.91 2.05 2.28 1.77 Safe

21 Vijaya Bank 1.57 1.52 1.47 1.60 1.97 1.50 2.04 2.91 3.16 3.20 3.07 3.04 2.86 2.30 Safe


TableA.47: Result of Zmijewski model for18 Private banks from 2005-2017

Sr,

No

Bank (ZM –

Score) 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Avg Result

1 Axis Bank 1.23 1.10 1.19 1.16 1.18 1.16 1.16 1.17 1.16 1.13 1.15 1.13 1.13 1.16 Safe

2 Catholic

Syrian Bank 1.26 1.24 1.19 1.16 1.23 1.26 1.28 1.27 1.28 1.29 1.31 1.37 1.34 1.27 Safe

3 City Union

Bank 1.13 1.00 1.05 1.07 1.14 1.15 1.16 1.22 1.22 1.21 1.16 1.19 1.18 1.14 Safe

4 D C B Bank 1.27 1.27 1.20 1.18 1.24 1.24 1.15 1.14 1.17 1.10 1.08 0.99 1.07 1.16 Safe

5 Dhanlaxmi

Bank 1.21 1.18 1.19 1.15 1.21 1.24 1.31 1.29 1.31 1.31 1.38 1.33 1.31 1.26 Safe


7 H D F C

Bank 0.77 0.74 0.70 0.80 0.85 0.82 0.75 0.70 0.83 0.85 1.02 1.05 0.96 0.83 Safe

8 I C I C I

Bank 0.91 1.02 1.06 1.05 1.09 1.12 1.13 0.96 1.00 1.00 1.05 1.08 1.10 1.04 Safe

9 Indusind

Bank 1.13 1.22 1.19 1.18 1.18 1.15 1.15 1.17 1.18 1.16 1.01 1.05 1.05 1.14 Safe

10 Jammu & K

Bank 1.23 1.21 1.22 1.19 1.22 1.21 1.22 1.20 1.22 1.21 1.25 1.24 1.33 1.23 Safe

11 Karnataka

Bank 1.19 1.25 1.22 1.19 1.23 1.25 1.26 1.26 1.24 1.23 1.23 1.26 1.26 1.24 Safe

12 Karur Vysya

Bank 1.09 1.11 1.12 1.15 1.16 1.20 1.17 1.23 1.24 1.24 1.23 1.25 1.25 1.19 Safe

13

Kotak

Mahindra

Bank

0.93 0.85 0.93 0.87 0.88 0.90 0.99 1.11 1.14 1.11 1.07 1.10 1.11 1.00 Safe

14 Lakshmi

Vilas Bank 1.21 1.18 1.21 1.11 1.15 1.22 1.16 1.21 1.21 1.25 1.22 1.23 1.25 1.20 Safe


16 R B L Bank 1.22 1.15 1.08 1.21 1.17 0.97 1.23 1.29 1.26 1.17 1.21 1.19 1.16 1.18 Safe

17 South Indian

Bank 1.18 1.18 1.18 1.18 1.21 1.22 1.21 1.21 1.23 1.23 1.27 1.29 1.29 1.22 Safe

18 Yes Bank 1.29 1.19 0.98 1.13 1.00 1.07 1.10 0.91 1.03 1.00 1.05 1,06 1.03 1.07 Safe


Table A.48: Results of Zmijewski Model for NWB from 2005-2014

Sr,

No Bank (ZM –Score) 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Avg Result

1 Bank Of Punjab (M) 1.23 ---- ----- ----- ---- ---- ----- ----- ---- ---- 1.23 safe

2 Bank Of Rajasth. (M) 1.17 1.18 1.14 1.14 1.15 1.12 ---- ----- ----- ---- 1.15 safe

259

3 Bharat Oves Bank Ltd.

(M) 1.13 1.18 ---- ----- ----- ---- ---- ----- ----- ---- 1.15 safe

4 Centurion Bank Of

Punjab Ltd. [Merged] 1.04 1.01 0.95 ---- ----- ----- ---- ---- ----- ----- 1.00 safe

5 I N G Vysya Bank

Ltd. (M) 1.12 1.12 1.01 0.98 1.01 1.04 1.07 1.11 1.11 1.13 1.07 safe


TableA.49: Result of Grover model of 21 Public Sector banks from 2005-2017

Sr.


1 Allahabad

Bank 0.37 0.41 0.38 0.36 0.40 0.39 0.36 0.42 0.43 0.42 0.44 0.47 0.45 0.41 Safe

2 Andhra Bank 0.39 0.37 0.38 0.43 0.42 0.48 0.46 0.46 0.43 0.47 0.44 0.45 0.49 0.44 Safe

3 Bank Of

Baroda 0.38 0.40 0.46 0.49 0.48 0.44 0.55 0.56 0.52 0.56 0.59 0.66 0.71 0.52 Safe


5 Bank Of


6 Canara Bank 0.36 0.38 0.40 0.43 0.43 0.41 0.43 0.45 0.46 0.46 0.48 0.52 0.50 0.44 Safe

7 Central Bank

Of India 0.43 0.37 0.42 0.39 0.38 0.41 0.36 0.38 0.37 0.37 0.38 0.42 0.61 0.41 Safe

8 Corporation

Bank 0.36 0.37 0.37 0.44 0.49 0.37 0.27 0.43 0.43 0.45 0.43 0.47 0.51 0.41 Safe

9 Dena Bank 0.40 0.44 0.45 0.52 0.61 0.43 0.40 0.42 0.48 0.42 0.55 0.49 0.56 0.47 Safe

10 I D B I Bank 0.21 0.41 0.39 0.37 0.38 0.33 0.41 0.42 0.39 0.39 0.49 0.62 0.51 0.41 Safe

11 Indian Bank 0.32 0.41 0.41 0.44 0.41 0.40 0.38 0.39 0.41 0.40 0.44 0.43 0.39 0.40 Safe

12 Indian Oves

Bank 0.41 0.35 0.37 0.38 0.47 0.43 0.49 0.46 0.45 0.46 0.51 0.49 0.51 0.45 Safe

13 Oriental Bank

Of Commerce 0.48 0.38 0.43 0.43 0.47 0.44 0.38 0.41 0.40 0.45 0.46 0.48 0.56 0.44 Safe

14 Punjab & Sind

Bank 0.43 0.42 0.51 0.31 0.37 0.31 0.31 0.38 0.41 0.42 0.44 0.46 0.46 0.40 Safe

15 Punjab

National Bank 0.29 0.50 0.45 0.40 0.42 0.39 0.38 0.41 0.40 0.43 0.41 0.50 0.54 0.42 Safe

16 State Bank Of

India 0.42 0.37 0.33 0.33 0.40 0.35 0.42 0.39 0.38 0.35 0.45 0.44 0.42 0.39 Safe


18 Uco Bank 0.46 0.32 0.40 0.43 0.46 0.38 0.50 0.47 0.45 0.38 0.49 0.45 0.43 0.43 Safe

19 Union Bank Of

India 0.39 0.32 0.38 0.40 0.43 0.38 0.39 0.37 0.37 0.40 0.43 0.44 0.41 0.39 Safe

20 United Bank Of

India 0.43 0.39 0.46 0.45 0.45 0.39 0.41 0.43 0.46 0.50 0.48 0.50 0.55 0.45 Safe

21 Vijaya Bank 0.33 0.40 0.54 0.56 0.69 0.39 0.38 0.45 0.42 0.47 0.50 0.56 0.50 0.48 Safe


Table A.50: Result of Grover model for 18 Private Banks from 2005-2017

Sr.


1 Axis Bank 0.41 0.29 0.36 0.43 0.45 0.39 0.35 0.36 0.37 0.38 0.39 0.38 0.43 0.38 Safe

2 Catholic Syrian 0.62 0.45 0.56 0.54 0.65 0.53 0.44 0.53 0.55 0.45 1.04 0.50 0.53 0.57 Safe

260

Bank

3 City Union

Bank 0.41 0.35 0.39 0.46 0.48 0.46 0.44 0.44 0.48 0.50 0.53 0.48 0.47 0.45 Safe

4 D C B Bank 0.40 0.39 0.34 0.44 0.60 0.38 0.26 0.25 0.27 0.32 0.36 0.33 0.32 0.36 Safe

5 Dhanlaxmi Bank 0.47 0.41 0.51 0.53 0.50 0.40 0.37 0.44 0.49 0.50 0.53 0.46 0.46 0.47 Safe


7 H D F C Bank 0.17 0.23 0.29 0.45 0.37 0.35 0.33 0.30 0.33 0.40 0.38 0.36 0.33 0.33 Safe

8 I C I C I Bank 0.29 0.28 0.39 0.44 0.47 0.46 0.36 0.39 0.39 0.40 0.38 0.41 0.47 0.39 Safe

9 Indusind Bank 0.39 0.43 0.49 0.47 0.54 0.41 0.47 0.38 0.37 0.36 0.34 0.36 0.41 0.42 Safe

10 Jammu &

Kashmir Bank 0.43 0.43 0.55 0.66 0.60 0.51 0.39 0.40 0.41 0.34 0.39 0.43 0.51 0.47 Safe

11 Karnataka Bank 0.72 0.51 0.43 0.45 0.44 0.41 0.37 0.40 0.41 0.40 0.50 0.49 0.44 0.46 Safe

12 Karur Vysya

Bank 0.39 0.41 0.38 0.37 0.46 0.37 0.37 0.44 0.40 0.40 0.42 0.44 0.44 0.41 Safe

13 Kotak Mahindra

Bank 0.15 0.16 0.17 0.27 0.31 0.27 0.23 0.31 0.32 0.36 0.35 0.36 0.37 0.28 Safe

14 Lakshmi Vilas

Bank 0.54 0.41 0.47 0.47 0.49 0.44 0.33 0.45 0.46 0.47 0.43 0.42 0.37 0.44 Safe


16 R B L Bank 0.52 0.49 0.57 0.80 0.72 0.44 0.34 0.40 0.36 0.39 0.41 0.35 0.42 0.48 Safe

17 South Indian

Bank 0.37 0.41 0.46 0.41 0.39 0.41 0.43 0.42 0.46 0.43 0.52 0.51 0.46 0.44 Safe

18 Yes Bank 0.07 0.19 0.27 0.44 0.39 0.31 0.33 0.32 0.32 0.38 0.41 0.37 0.42 0.33 Safe

Table A.51 Result of Grover model for NWB banks from 2005-2014

Sr.

No. Bank 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Avg Result

1 Bank Of Punjab Ltd.

[Merged] 0.53 ----- ----- ----- ----- ----- ---- ---- ---- ---- 0.53 Safe

2 Bank Of Rajasthan

Ltd. [Merged] 0.63 0.72 0.49 0.51 0.43 0.32 ----- ---- ---- ---- 0.51 safe

3 Centurion Bank Of

Punjab Ltd. [Merged] 0.38 0.36 0.26 ---- ----- ---- ---- ---- ----- ----- 0.33 safe

4 Bharat Overseas

Bank Ltd. [Merged] 0.41 0.46 ---- ----- ----- ------ ----- ---- ----- ---- 0.43 safe

5 I N G Vysya Bank

Ltd. [Merged] 0.33 0.29 0.36 0.44 0.38 0.37 0.34 0.38 0.39 0.46 0.43 safe


Table A.52: Result of Recalibrated Altman Model of 21 Public and 18 Private Banks

Sr. No.

Bank 2011 2012 2013 2014 2015 2016 2017 Mean Result

1 Allahabad Bank 0.36 0.42 0.42 0.41 0.43 0.46 0.44 0.42 Safe

2 Andhra Bank 0.46 0.45 0.42 0.46 0.43 0.45 0.48 0.45 Safe

3 Bank Of Baroda 0.56 0.56 0.52 0.56 0.59 0.66 0.71 0.59 Safe

4 Bank Of India 0.39 0.44 0.45 0.44 0.44 0.51 0.53 0.46 Safe

5 Bank Of Maharashtra 0.33 0.38 0.42 0.40 0.43 0.46 0.58 0.43 Safe

6 Canara Bank 0.43 0.44 0.45 0.45 0.47 0.51 0.49 0.46 Safe

7 Central Bank Of India 0.36 0.37 0.36 0.36 0.37 0.41 0.61 0.41 Safe

8 Corporation Bank 0.27 0.42 0.42 0.43 0.42 0.46 0.51 0.42 Safe

9 Dena Bank 0.40 0.41 0.47 0.41 0.54 0.48 0.56 0.47 Safe

10 I D B I Bank 0.41 0.41 0.38 0.38 0.48 0.61 0.51 0.45 Safe

261

11 Indian Bank 0.37 0.38 0.40 0.39 0.43 0.43 0.39 0.40 Safe

12 Indian Overseas Bank 0.49 0.46 0.44 0.45 0.50 0.48 0.50 0.47 Safe

13 Oriental Bank Of

Commerce 0.37 0.40 0.39 0.44 0.45 0.47 0.56 0.44 Safe

14 Punjab & Sind Bank 0.31 0.37 0.40 0.41 0.43 0.45 0.45 0.40 Safe

15 Punjab National Bank 0.37 0.40 0.39 0.42 0.40 0.49 0.54 0.43 Safe

16 State Bank Of India 0.42 0.38 0.38 0.35 0.44 0.44 0.42 0.40 Safe

17 Syndicate Bank 0.35 0.40 0.38 0.30 0.36 0.42 0.39 0.37 Safe

18 Uco Bank 0.50 0.46 0.44 0.37 0.48 0.44 0.42 0.44 Safe

19 Union Bank Of India 0.39 0.37 0.36 0.39 0.42 0.44 0.40 0.40 Safe

20 United Bank Of India 0.40 0.43 0.45 0.49 0.47 0.49 0.55 0.47 Safe

21 Vijaya Bank 0.38 0.44 0.41 0.46 0.49 0.56 0.49 0.46 Safe

22 Axis Bank 0.35 0.36 0.36 0.37 0.38 0.37 0.43 0.38 Safe

23 Catholic Syrian Bank 0.44 0.53 0.54 0.43 0.96 0.49 0.53 0.56 Safe

24 City Union Bank 0.44 0.43 0.47 0.49 0.51 0.47 0.46 0.47 Safe

25 D C B Bank 0.26 0.25 0.27 0.31 0.35 0.33 0.31 0.30 Safe

26 Dhanlaxmi Bank 0.37 0.43 0.48 0.49 0.52 0.46 0.45 0.46 Safe

27 Federal Bank 0.48 0.45 0.42 0.46 0.56 0.50 0.49 0.48 Safe

28 H D F C Bank 0.33 0.30 0.33 0.40 0.38 0.36 0.33 0.35 Safe

29 I C I C I Bank 0.36 0.38 0.38 0.39 0.37 0.40 0.46 0.39 Safe

30 Indusind Bank 0.47 0.37 0.36 0.35 0.33 0.35 0.41 0.38 Safe

31 Jammu & Kashmir Bank 0.39 0.40 0.40 0.34 0.38 0.43 0.51 0.40 Safe

32 Karnataka Bank 0.37 0.39 0.39 0.38 0.49 0.49 0.44 0.42 Safe

33 Karur Vysya Bank 0.37 0.44 0.39 0.39 0.40 0.43 0.44 0.41 Safe

34 Kotak Mahindra Bank 0.23 0.30 0.31 0.35 0.34 0.35 0.37 0.32 Safe

35 Lakshmi Vilas Bank 0.33 0.44 0.44 0.46 0.42 0.41 0.37 0.41 Safe

36 Nainital Bank 0.80 0.65 0.85 0.73 0.81 0.77 0.71 0.76 Safe

37 R B L Bank 0.34 0.40 0.35 0.38 0.40 0.35 0.42 0.38 Safe

38 South Indian Bank 0.42 0.41 0.45 0.42 0.50 0.50 0.45 0.45 Safe

39 Yes Bank 0.33 0.31 0.31 0.37 0.40 0.37 0.42 0.36 Safe


Table A.53: Result of Recalibrated Springate Model of 21 Public and 18 Private Banks

Sr. No.

Bank Springa

te 2011 2012 2013 2014 2015 2016 2017 Mean

1 Allahabad Bank 3.82 4.37 4.48 2.31 1.99 2.28 2.15 3.06

2 Andhra Bank 3.35 4.20 4.52 2.83 2.40 2.57 1.93 3.12

3 Axis Bank 3.10 4.23 4.48 1.95 2.03 1.86 1.65 2.76

4 Bank Of Baroda 2.79 3.41 3.28 1.82 1.70 1.72 1.79 2.36

5 Bank Of India 2.76 3.49 3.68 1.82 2.05 2.11 2.06 2.57

6 Bank Of Maharashtra 3.46 3.59 3.64 1.95 1.77 2.26 1.77 2.64

7 Bank Of Rajasthan

(NWB) ---- ---- ---- ----- ----- ----- ----- ----

8 Canara Bank 3.39 4.07 3.85 2.21 2.07 2.15 2.09 2.83

9 Catholic Syrian Bank 2.96 3.50 3.83 2.61 2.51 3.00 3.11 3.08

10 Central Bank Of India 3.14 3.93 4.13 1.42 1.39 1.51 1.90 2.49

11 City Union Bank 3.25 4.51 4.36 2.70 2.33 2.46 2.38 3.14

262

12 Corporation Bank 5.44 3.84 3.95 2.25 2.24 2.60 1.84 3.17

13 D C B Bank 2.94 -1.42 1.22 1.38 1.44 1.15 1.28 1.14

14 Dena Bank 3.68 4.04 4.25 2.53 2.34 2.54 2.32 3.10

15 Dhanlaxmi Bank 3.19 3.58 4.34 1.61 2.38 1.60 2.43 2.73

16 Federal Bank 3.15 4.17 4.20 2.57 2.83 2.47 2.44 3.12

17 H D F C Bank 4.58 17.50 7.49 1.15 1.52 1.53 1.33 5.01

18 I C I C I Bank 3.19 4.07 4.15 1.61 1.77 1.81 1.97 2.65

19 I D B I Bank 3.45 4.22 4.24 2.09 2.15 2.11 1.64 2.84

20 I N G Vysya Bank

(NWB) 3.28 3.92 4.20 1.94 ----- ----- ----- 3.34

21 Indian Bank 3.43 4.16 3.79 1.84 2.03 2.15 2.04 2.77

22 Indian Overseas Bank 2.90 3.74 4.04 2.41 2.74 2.19 2.18 2.88

23 Indusind Bank 2.88 5.02 5.67 2.29 1.41 1.72 1.69 2.95

24 Jammu & Kashmir

Bank 3.33 3.89 4.29 2.43 2.67 2.15 1.81 2.94

25 Karnataka Bank 3.61 4.43 4.65 2.15 2.30 2.59 2.52 3.18

26 Karur Vysya Bank 3.46 4.34 4.75 2.77 2.69 2.87 2.86 3.39

27 Kotak Mahindra Bank -2.45 -2.36 2.63 2.21 1.88 1.73 1.95 0.80

28 Lakshmi Vilas Bank 4.79 3.94 4.17 2.25 2.08 2.19 2.39 3.11

29 Nainital Bank 2.38 3.58 3.11 1.69 2.73 1.89 1.89 2.47

30 Oriental Bank Of

Commerce 3.30 4.46 4.69 2.36 2.20 2.67 2.82 3.21

31 Punjab & Sind Bank 4.43 4.64 4.20 2.13 2.38 3.71 4.15 3.66

32 Punjab National Bank 3.15 3.79 4.16 2.05 1.95 2.23 2.36 2.82

33 R B L Bank 2.53 4.33 4.15 1.67 1.83 1.63 1.74 2.55

34 South Indian Bank 3.09 3.96 3.98 2.32 2.53 2.74 2.81 3.06

35 State Bank Of India 2.88 3.84 3.70 1.20 1.12 1.07 1.20 2.15

36 Syndicate Bank 3.30 3.88 3.63 1.43 1.74 1.96 2.14 2.58

37 Uco Bank 2.78 3.53 3.62 1.79 1.80 1.76 1.66 2.42

38 Union Bank Of India 2.95 4.08 4.28 2.39 2.24 2.60 2.40 2.99

39 United Bank Of India 2.98 3.76 3.59 1.69 1.64 1.78 2.01 2.49

40 Vijaya Bank 3.25 4.13 4.39 2.79 2.64 2.69 2.55 3.21

41 Yes Bank 3.84 -31.92 25.38 1.26 1.50 1.49 1.56 0.45


Table A.54: Result of Recalibrated Zmijewski Model of 21 Public and 18 Private Banks

Sr. No.

Bank 2011 2012 G-2013 2014 2015 2016 2017 Mean

1 Allahabad Bank 0.36 0.42 0.42 0.41 0.43 0.46 0.44 0.42

2 Andhra Bank 0.46 0.45 0.42 0.46 0.43 0.45 0.48 0.45

3 Bank Of Baroda 0.56 0.56 0.52 0.56 0.59 0.66 0.71 0.59

4 Bank Of India 0.39 0.44 0.45 0.44 0.44 0.51 0.53 0.46


6 Canara Bank 0.43 0.44 0.45 0.45 0.47 0.51 0.49 0.46



9 Dena Bank 0.40 0.41 0.47 0.41 0.54 0.48 0.56 0.47

263

10 I D B I Bank Ltd. 0.41 0.41 0.38 0.38 0.48 0.61 0.51 0.45

11 Indian Bank 0.37 0.38 0.40 0.39 0.43 0.43 0.39 0.40


13 Oriental Bank Of

Commerce 0.37 0.40 0.39 0.44 0.45 0.47 0.56 0.44

14 Punjab & Sind Bank 0.31 0.37 0.40 0.41 0.43 0.45 0.45 0.40



17 Syndicate Bank 0.35 0.40 0.38 0.30 0.36 0.42 0.39 0.37

18 Uco Bank 0.50 0.46 0.44 0.37 0.48 0.44 0.42 0.44



21 Vijaya Bank 0.38 0.44 0.41 0.46 0.49 0.56 0.49 0.46

Private Banks

22 Axis Bank Ltd. 0.35 0.36 0.36 0.37 0.38 0.37 0.43 0.38

23 Catholic Syrian Bank

Ltd. 0.44 0.53 0.54 0.43 0.96 0.49 0.53 0.56

24 City Union Bank Ltd. 0.44 0.43 0.47 0.49 0.51 0.47 0.46 0.47

25 D C B Bank Ltd. 0.26 0.25 0.27 0.31 0.35 0.33 0.31 0.30

26 Dhanlaxmi Bank Ltd. 0.37 0.43 0.48 0.49 0.52 0.46 0.45 0.46

27 Federal Bank Ltd. 0.48 0.45 0.42 0.46 0.56 0.50 0.49 0.48

28 H D F C Bank Ltd. 0.33 0.30 0.33 0.40 0.38 0.36 0.33 0.35

29 I C I C I Bank Ltd. 0.36 0.38 0.38 0.39 0.37 0.40 0.46 0.39

30 Indusind Bank Ltd. 0.47 0.37 0.36 0.35 0.33 0.35 0.41 0.38

31 Jammu & Kashmir

Bank Ltd. 0.39 0.40 0.40 0.34 0.38 0.43 0.51 0.40

32 Karnataka Bank Ltd. 0.37 0.39 0.39 0.38 0.49 0.49 0.44 0.42

33 Karur Vysya Bank Ltd. 0.37 0.44 0.39 0.39 0.40 0.43 0.44 0.41

34 Kotak Mahindra Bank

Ltd. 0.23 0.30 0.31 0.35 0.34 0.35 0.37 0.32


Ltd. 0.33 0.44 0.44 0.46 0.42 0.41 0.37 0.41

36 Nainital Bank Ltd. 0.80 0.65 0.85 0.73 0.81 0.77 0.71 0.76

37 R B L Bank Ltd. 0.34 0.40 0.35 0.38 0.40 0.35 0.42 0.38

38 South Indian Bank Ltd. 0.42 0.41 0.45 0.42 0.50 0.50 0.45 0.45

39 Yes Bank Ltd. 0.33 0.31 0.31 0.37 0.40 0.37 0.42 0.36

11 12 13 14 15 16 17 Mean


Table A.55: Result of Recalibrated Grover Model of 21 Public and 18 Private Banks

Sr. No.

Bank 2011 2012 G-2013 2014 2015 2016 2017 Mean

1 Allahabad Bank -0.55 1.27 1.24 1.22 1.20 1.21 0.97 0.937

2 Andhra Bank 1.31 1.27 1.26 1.27 1.22 1.23 1.25 1.259

3 Axis Bank Ltd. 1.24 1.24 1.23 1.23 1.25 1.23 1.23 1.236

4 Bank Of Baroda 1.37 1.37 1.32 1.35 1.31 1.24 1.33 1.327

5 Bank Of India 1.22 1.23 1.27 1.24 1.26 1.24 1.25 1.244

264


7 Bank Of Rajasthan Ltd.

(NWB) ---- ---- ---- ---- ----- ---- ---- ----

8 Canara Bank 1.29 1.27 1.24 1.25 1.24 1.21 1.25 1.251

9 Catholic Syrian Bank

Ltd. 1.26 1.28 1.29 1.23 1.23 1.22 1.26 1.253


11 City Union Bank Ltd. 1.26 1.28 1.29 1.28 1.27 1.27 1.27 1.274


13 D C B Bank Ltd. 1.12 1.13 1.17 1.18 1.19 1.14 1.15 1.154

14 Dena Bank 1.30 1.27 1.33 1.24 1.29 1.24 1.28 1.278

15 Dhanlaxmi Bank Ltd. 1.26 1.15 1.26 1.11 1.18 1.12 1.19 1.182

16 Federal Bank Ltd. 1.29 1.27 1.26 1.27 1.35 1.29 1.29 1.289

17 H D F C Bank Ltd. 1.12 1.11 1.15 1.17 1.21 1.21 1.16 1.16

18 I C I C I Bank Ltd. 1.22 1.17 1.19 1.20 1.22 1.20 1.21 1.202

19 I D B I Bank Ltd. 1.23 1.23 1.20 1.18 1.23 1.21 1.20 1.211

20 I N G Vysya Bank Ltd.

(NWB) 1.16 1.19 1.19 1.23 ---- ---- ---- ----

21 Indian Bank 1.23 1.21 1.21 1.20 1.21 1.21 1.21 1.213


23 Indusind Bank Ltd. 1.26 1.21 1.22 1.22 1.18 1.20 1.21 1.214

24 Jammu & Kashmir

Bank Ltd. 1.27 1.26 1.29 1.24 1.20 1.23 1.23 1.246

25 Karnataka Bank Ltd. 1.23 1.22 1.22 1.20 1.27 1.29 1.24 1.24

26 Karur Vysya Bank Ltd. 1.25 1.29 1.25 1.21 1.20 1.26 1.25 1.243

27 Kotak Mahindra Bank

Ltd. 1.16 1.20 1.20 1.21 1.21 1.19 1.20 1.196


Ltd. 1.17 1.21 1.20 1.21 1.20 1.21 1.20 1.2

29 Nainital Bank Ltd. 1.37 1.41 1.36 1.32 1.53 1.33 1.38 1.385

30 Oriental Bank Of

Commerce 1.21 1.21 1.22 1.23 1.21 1.25 1.23 1.223

31 Punjab & Sind Bank 1.18 1.18 1.18 1.21 1.21 1.32 1.23 1.216


33 R B L Bank Ltd. 1.23 1.34 1.25 1.19 1.24 1.20 1.24 1.239

34 South Indian Bank Ltd. 1.25 1.23 1.27 1.24 1.28 1.30 1.27 1.263


36 Syndicate Bank 1.21 1.22 1.23 1.17 1.20 1.18 1.20 1.202

37 Uco Bank 1.26 1.25 1.20 1.20 1.23 1.13 1.20 1.211



40 Vijaya Bank 1.24 1.26 1.25 1.31 1.28 1.34 1.29 1.283

Yes Bank Ltd. 1.19 1.13 1.16 1.17 1.20 1.20 1.18 1.178


A.56 Questionnaire (part 2): PREPAREDENESS IN BASEL III IMPLEMENTATION

This part deals with a set of statements relating to Basel III norms to be implemented in India in a phased

manner beginning from January 2013 till 2019.

265

a) Please indicate your level for your bank about the perceived benefits of Basel III for each of the statement

on a scale from strongly agrees to strongly disagree.

Sr.

no Statements

Strongly

Agree Agree Neutral Disagree

Strongly

Disagree

1 Better quality of capital improves loss

absorption capacity of banks

2

Basel III ensures better liquidity risk

management with increase in short term

liquidity coverage

3 Market disclosures are more detailed and

transparent under Basel III

4

Basel III provides counter cyclical

mechanism (prevent enlargement of business

cycles for banks)

5 Risk of excessive leverage is reduced through

introduction of leverage ratio under Basel III

b) Please indicate your level for your bank about anticipated cost of Basel III implementation for each of the

statement on a scale from strongly agree to strongly disagree.

Sr.

No. Statements

Strongly


Strongly

Disagree

1

Substantial outlay is involved in data

acquisition, software and hardware

development for Basel III implementation

2

Expenditure on recruitment and training of

personnel required for Basel III

implementation has increased

3

Cost of complying with multiple regulators

and disclosure requirements has increased

under Basel III

4 There is increase in cost of raising additional

capital to meet Basel III requirements

5 Banks have to incur cost of hiring consultants

having experience in quantifying risk

6

Risk management model outsourcing for

Basel III involves hug

expenditure

3) Please indicate your level for your bank about the impact of Basel III implementation for each of the


Sr.

No. Statements

Strongly


Strongly

Disagree

1

Basel III will provide better foundation for

future development in risk management and

reduction in risk of banking crisis

2 Pressure on Indian banks will increase to raise

additional capital to meet new requirements

3

Basel III regulation may lead to reduction in

pro-cyclical (i.e. enlargement of business

cycles) behaviour of banks

4 Basel III implementation will put significant

266

pressure on banks’ profitability & Return on

equity

5

There is significant increase in risk weighted

assets for different categories of risk under

Basel III

6

Basel III implementation may increase

dominance by large institutions with well

developed infrastructure

7 There will be decrease in investors’ return

with Basel III adoption

8

Significant increase in capital and liquidity

requirements under Basel III may lead to

reduced lending capacity of banks

4) Please indicate your level for your bank about the challenges of Basel III implementation for each of the


Sr.

No. Statements

Strongly


Strongly

Disagree

1 There is increase in risk weighted assets

calculations under Basel III

2

The Bank will have to design comprehensive

liquidity management framework and

information technology for Basel III

implementation

3

Bank have to achieve closer integration of

finance and risk management functions to

properly implement Basel III

4 Maintaining data integrity within the bank is

a tough task while implementing Basel III

5

Banks have to recruit and retain skilled staff

for risk management to meet Basel III

requirements

5) Please indicate your level for your bank about the preparedness in Basel III implementation for each of the


Sr.

No. Statements

Strongly


Strongly

Disagree

1

High priority is attributed for

implementation of Basel III by the

management of my bank

2 There is availability of competent human

resources in my bank to implement Basel III

3

There is familiarity and awareness among

my bank’s staff regarding application of

Basel III

4 The bank has access to up-to-date

technologies for Basel III implementation

267

Table A.57: Tier I Capital Ratio from 2009-2018

Tier 1 capital ratio

Sr.

No. Public banks 2009 2010 2011 2012 2013 Mean 2014 2015 2016 2017 2018 Mean

1 Allahabad

Bank 8.01 8.12 8.57 9.13 8.05 8.38 7.51 7.71 8.41 8.49 6.69 7.76

2 Andhra bank 8.67 8.18 9.68 9.03 8.52 8.82 8.09 7.79 8.81 7.44 9.17 8.26

3 Bank of

Baroda 8.49 9.2 9.99 10.83 10.13 9.73 9.28 9.87 10.79 9.93 8.52 9.68

4 Bank Of India 8.91 8.48 8.33 8.59 8.2 8.50 7.24 8.17 9.03 8.9 10.36 8.74

5 Bank Of

Maharashtra 6.11 6.51 8.02 8.31 7.57 7.30 7.44 8.76 9.02 9.01 11.27 9.10

6 Canara Bank 8.01 8.54 10.87 10.35 9.77 9.51 7.68 8.02 8.8 9.77 10.4 8.93

7 Central Bank

Of India 6.97 6.83 6.31 7.79 8.09 7.20 7.37 8.05 8.2 8.62 7.01 7.85

8 Corporation

Bank 8.9 9.25 8.69 8.33 8.33 8.70 8.14 8.05 7.93 8.9 9.1 8.42

9 Dena Bank 6.76 8.16 9.77 8.86 7.26 8.16 7.43 7.67 8.59 9.05 8.97 8.34

10 I D B I Bank 6.81 6.24 8.03 8.38 7.68 7.43 7.79 8.18 8.89 7.81 7.73 8.08

11 Indian Bank 11.88 11.13 11.02 11.13 10.88 11.21 10.24 10.61 12.08 12.2 7.17 10.46

12 Indian

Overseas Bank 7.88 8.67 8.16 8.35 7.8 8.17 7.47 7.3 7.75 8.21 7.17 7.58

13 Oriental Bank

Of Comm. 9.1 9.28 11.21 10.12 9.18 9.78 8.86 8.73 9.1 8.88 7.61 8.64

14 Punjab & Sind

Bank 8.44 7.68 8.35 8.55 8.38 8.28 7.62 8.48 9.29 9.14 9.27 8.76

15 Punjab

National Bank 8.98 9.11 8.44 9.28 9.76 9.11 8.87 9.3 8.41 8.91 7.12 8.52

16 State Bank Of

India 9.38 9.45 7.77 9.79 9.49 9.18 9.72 9.16 9.92 10.35 10.36 9.90

17 Syndicate

Bank 7.85 8.24 9.31 8.94 8.96 8.66 8.68 7.84 7.75 9.26 9.41 8.59

18 Uco Bank 6.48 7.05 8.52 8.09 9.06 7.84 8.71 9.05 7.63 8.27 8.94 8.52

19 Union Bank

Of India 8.19 7.91 8.69 8.37 8.23 8.28 7.54 7.5 8.14 9.02 9.14 8.27

20 United Bank

Of India 7.56 8.16 8.9 8.79 8.4 8.36 6.54 7.52 7.93 8.94 9.87 8.16

21 Vijaya Bank 7.74 7.69 9.88 9.68 8.54 8.71 8.12 8.24 9.45 9.5 11.71 9.40

8.63 8.67

Sr.

No. Private banks

1 Axis Bank 9.26 11.18 9.41 9.45 12.23 10.31 12.62 12.07 12.51 11.87 13.11 12.44

2 Catholic

Syrian Bank 8.81 8.07 9.42 8.83 9.62 8.95 9.07 9.26 9.76 11.54 9.45 9.82

3 City Union

Bank 11.48 12.41 11.84 11.69 13.27 12.14 14.43 16.03 15.09 15.35 15.79 15.34

4 D C B Bank 11.5 11.93 11.1 13.81 12.62 12.19 12.86 14.21 12.79 11.87 11.93 12.73

5 Dhanalaxmi

Bank 13.75 8.8 9.41 7.42 8.05 9.49 6.93 7.42 6.12 9.01 10.6 8.02

6 Federal Bank 18.42 6.92 15.63 15.86 14.09 14.18 6.93 7.42 6.12 9.01 10.6 8.02

7 H D F C Bank 10.58 13.26 12.23 11.6 11.08 11.75 11.77 13.66 13.22 12.79 13.25 12.94

8 I C I C I Bank 11.84 13.96 13.17 12.68 13.78 13.09 12.78 12.78 13.09 14.36 15.92 13.79

268

9 Indusind Bank 7.65 9.65 12.29 11.37 12.8 10.75 12.71 11.22 14.92 14.72 14.58 13.63

10 Jammu &

Kashmir Bank 13.8 12.79 11.33 11.12 10.86 11.98 11.22 11.26 10.6 8.7 9.24 10.20

11 Karnataka

Bank 10.6 9.98 11.27 10.86 10.51 10.64 10.73 10.52 10.56 12.21 11.29 11.06

12 Karur Vysya

Bank 14.4 12.88 13.07 13.1 13.1 13.31 11.62 13.56 11.26 11.85 12.54 12.17

13

Kotak

Mahindra

Bank

16.13 15.42 17.99 15.74 14.71 16.00 17.77 16.82 15.28 15.9 17.6 16.67

14 Lakshmi Vilas

Bank 8.81 12.09 10.78 8.86 9.15 9.94 7.87 9.33 8.69 8.75 8.05 8.54

15 Nainital Bank 11.85 14.38 15.8 14.62 13.99 14.13 14.69 14.41 15.25 12.47 14.51 14.27

16 R B L Bank 41.69 33.53 55.93 22.83 16.82 34.16 14.33 13.13 11.1 11.39 13.61 12.71

17 South Indian

Bank 13.32 12.42 11.27 11.54 12.05 12.12 10.79 10.33 9.83 10.88 10.41 10.45

18 Yes Bank 9.5 12.9 9.7 9.9 9.5 10.30 9.8 15.6 10.7 13.3 13.2 12.52

Mean 13.07 11.96

Source: Secondary data from Indiastat.com

Table A.58: Result of Tier II Capital Ratio from 2009-2018

Tier II capital ratio

Sr. No.

Public banks 2009 2010 2011 2012 2013 Mean 2014 2015 2016 2017 2018 Mean

1 Allahabad

Bank 5.1 5.5 4.39 3.7 2.98 4.33 2.45 2.74 2.61 2.96 2 2.55

2 Andhra bank 4.45 5.75 4.7 4.15 3.24 4.46 2.69 2.64 2.77 3.21 3.56 2.97

3 Bank of

Baroda 5.56 5.16 4.53 3.84 3.17 4.45 3 2.74 2.39 2.31 1.6 2.41

4 Bank Of India 4.1 4.46 3.84 3.36 2.82 3.72 2.73 2.56 2.98 3.24 3.18 2.94

5 Bank Of

Maharashtra 5.94 6.37 5.33 4.12 5.02 5.36 3.35 3.18 2.18 2.17 2 2.58

6 Canara Bank 6.9 4.89 4.51 3.41 2.63 4.47 2.95 2.54 2.28 3.09 2.91 2.75

7 Central Bank

Of India 6.15 5.4 5.33 4.61 3.4 4.98 2.5 2.85 2.2 2.32 2.03 2.38

8 Corporation

Bank 4.71 6.12 5.42 4.67 4 4.98 3.51 3.04 2.63 2.42 2.02 2.72

9 Dena Bank 4.61 3.64 2.65 3.77 3.77 3.69 3.71 3.26 2.41 2.34 2.51 2.85

10 I D B I Bank 4.76 5.07 5.61 6.2 5.45 5.42 3.89 3.58 2.78 2.89 2.67 3.16

11 Indian Bank 2.1 1.58 2.54 2.34 2.2 2.15 2.4 2.25 1.12 1.44 2.08 1.86

12 Indian

Overseas

Bank

5.32 6.11 6.39 4.97 4.05 5.37 3.31 2.81 1.92 2.28 2.08 2.48

13 Oriental Bank

Of Commerce 3.88 3.26 3.02 2.57 2.86 3.12 2.15 2.68 2.66 2.76 2.89 2.63

14 Punjab & Sind

Bank 5.91 5.42 4.59 4.71 4.53 5.03 3.42 2.76 1.62 1.91 1.39 2.22

15 Punjab

National Bank 5.05 5.05 3.99 3.35 2.96 4.08 2.65 2.91 2.87 2.75 2 2.64

16 State Bank Of

India 4.87 3.94 4.21 4.07 3.43 4.10 2.72 2.4 3.2 2.76 2.24 2.66

17 Syndicate

Bank 4.83 4.46 3.73 3.3 3.63 3.99 2.73 2.7 3.41 2.77 2.83 2.89

269

18 Uco Bank 5.45 6.16 5.19 4.26 5.09 5.23 3.97 3.12 2 2.66 2 2.75

19 Union Bank

Of India 5.08 4.6 4.26 3.48 3.22 4.13 3.26 2.72 2.42 2.77 2.42 2.72

20 United Bank

Of India 5.72 4.64 4.15 3.9 3.26 4.33 3.27 3.05 2.15 2.2 2.75 2.68

21 Vijaya Bank 5.41 4.81 4 3.38 2.78 4.08 2.44 3.19 3.13 4.36 1.35 2.89

Mean 4.36 2.65

Sr.

No. Private banks

1 Axis Bank 4.43 4.62 3.24 4.21 4.77 4.25 3.45 3.32 2.78 3.08 3.47 3.22

2 Catholic

Syrian Bank 3.48 2.74 1.8 2.25 2.67 2.59 1.93 1.74 0.79 0.61 0.46 1.11

3 City Union

Bank 1.21 1.05 0.91 0.88 0.71 0.95 0.58 0.49 0.49 0.48 0.43 0.49

4 D C B Bank 1.8 2.92 2.15 1.6 0.99 1.89 0.85 0.74 1.32 1.89 3.52 1.66

5 Dhanalaxmi

Bank 1.63 4.19 2.39 2.07 3.01 2.66 1.74 2.17 1.39 1.25 3.27 1.96

6 Federal Bank 1.8 1.44 1.16 0.78 0.64 1.16 0.55 0.65 0.57 0.58 0.52 0.57

7 H D F C Bank 5.11 4.18 3.99 4.92 5.72 4.78 4.3 3.13 2.31 1.76 1.57 2.61

8 I C I C I Bank 3.69 5.45 6.37 5.84 5.94 5.46 4.92 4.24 3.55 3.03 2.5 3.65

9 Indusind Bank 4.9 5.68 3.6 2.48 1.58 3.65 1.12 0.87 0.58 0.59 0.45 0.72

10 Jammu &

Kashmir Bank 0.68 3.1 2.39 2.24 1.97 2.08 1.47 1.31 1.25 2.1 2.18 1.66

11 Karnataka

Bank 2.88 2.39 2.06 1.98 2.71 2.40 2.47 1.89 1.47 1.09 0.75 1.53

12 Karur Vysya

Bank 0.52 1.61 1.34 1.31 1.31 1.22 0.97 1.06 0.91 0.69 1.89 1.10

13

Kotak

Mahindra

Bank

3.88 2.93 1.93 1.78 1.34 2.37 1.06 0.99 1 0.87 0.6 0.90

14 Lakshmi Vilas

Bank 1.48 2.81 2.41 4.24 3.17 2.82 3.03 2.01 1.98 1.63 1.76 2.08

15 Nainital Bank 1.25 1.3 0.55 0.47 0.44 0.80 0.44 0.45 0.44 0.31 0.44 0.42

16 R B L Bank 0.61 0.54 0.48 0.37 0.29 0.46 0.31 0.39 1.84 2.33 1.72 1.32

17 South Indian

Bank 1.54 2.97 2.74 2.46 1.86 2.31 1.63 1.59 1.99 1.49 2.29 1.80

18 Yes Bank 7.1 7.7 6.8 8 8.8 7.68 4.6 4.1 5.8 3.7 5.2 4.68

Mean 2.75 1.75


Table A.59: CAR from 2009-2018

Capital Adequacy Ratio

Sr. No.

Public banks 2009 2010 2011 2012 2013 Mean 2014 2015 2016 2017 2018 Mean

1 Allahabad

Bank 13.11 13.62 12.96 12.83 11.03 12.71 9.96 10.45 11.02 11.45 8.69 10.31

2 Andhra bank 13.12 13.93 14.38 13.18 11.76 13.27 10.78 10.43 11.58 10.65 12.73 11.23

3 Bank of

Baroda 14.05 14.36 14.52 14.67 13.3 14.18 12.28 12.61 13.18 12.24 10.12 12.09

4 Bank Of India 13.01 12.94 12.17 11.95 11.02 12.22 9.97 10.73 12.01 12.14 13.54 11.68

270

5 Bank Of

Maharashtra 12.05 12.88 13.35 12.43 12.59 12.66 10.79 11.94 11.2 11.18 13.27 11.68

6 Canara Bank 14.91 13.43 15.38 13.76 12.4 13.98 10.63 10.56 11.08 12.86 13.31 11.69

7 Central Bank

Of India 13.12 12.23 11.64 12.4 11.49 12.18 9.87 10.9 10.4 10.94 9.04 10.23

8 Corporation

Bank 13.61 15.37 14.11 13 12.33 13.68 11.65 11.09 10.56 11.32 11.12 11.15

9 Dena Bank 12.07 12.77 13.41 11.51 11.3 12.21 11.14 10.93 11 11.39 11.48 11.19

10 I D B I Bank 11.57 11.31 13.64 14.58 13.13 12.85 11.68 11.76 11.67 10.7 10.4 11.24

11 Indian Bank 13.98 12.71 13.56 13.47 13.08 13.36 12.64 12.86 13.2 13.64 9.25 12.32

12 Indian

Overseas Bank 13.2 14.78 14.55 13.32 11.85 13.54 10.78 10.11 9.67 10.49 9.25 10.06

13 Oriental Bank

Of Commerce 12.98 12.54 14.23 12.69 12.04 12.90 11.01 11.41 11.76 11.64 10.5 11.26

14 Punjab & Sind

Bank 14.35 13.1 12.94 13.26 12.91 13.31 11.04 11.24 10.91 11.05 10.66 10.98

15 Punjab

National Bank 14.03 14.16 12.43 12.63 12.72 13.19 11.52 12.21 11.28 11.66 9.12 11.16

16 State Bank Of

India 14.25 13.39 11.98 13.86 12.92 13.28 12.44 11.56 13.12 13.11 12.6 12.57

17 Syndicate

Bank 12.68 12.7 13.04 12.24 12.59 12.65 11.41 10.54 11.16 12.03 12.24 11.48

18 Uco Bank 11.93 13.21 13.71 12.35 14.15 13.07 12.68 12.17 9.63 10.93 10.94 11.27

19 Union Bank

Of India 13.27 12.51 12.95 11.85 11.45 12.41 10.8 10.22 10.56 11.79 11.56 10.99

20 United Bank

Of India 13.28 12.8 13.05 12.69 11.66 12.70 9.81 10.57 10.08 11.14 12.62 10.84

21 Vijaya Bank 13.15 12.5 13.88 13.06 11.32 12.78 10.56 11.43 12.58 13.86 13.06 12.30

13.1 11.31

Sr.

No. Private banks

1 Axis Bank 13.69 15.8 12.65 13.66 17 14.56 16.07 15.39 15.29 14.95 16.58 15.66

2 Catholic

Syrian Bank 12.29 10.81 11.22 11.08 12.29 11.54 11 11 10.55 12.15 9.91 10.92

3 City Union

Bank 12.69 13.46 12.75 12.57 13.98 13.09 15.01 16.52 15.58 15.83 16.22 15.83

4 D C B Bank 13.3 14.85 13.25 15.41 13.61 14.08 13.71 14.95 14.11 13.76 15.45 14.40

5 Dhanalaxmi

Bank 15.38 12.99 11.8 9.49 11.06 12.14 8.67 9.59 7.51 10.26 13.87 9.98

6 Federal Bank 20.22 8.36 16.79 16.64 14.73 15.35 15.28 15.46 13.93 12.39 14.87 14.39

7 H D F C Bank 15.69 17.44 16.22 16.52 16.8 16.53 16.07 16.79 15.53 14.55 14.82 15.55

8 I C I C I Bank 15.53 19.41 19.54 18.52 19.72 18.54 17.7 17.02 16.64 17.39 18.42 17.43

9 Indusind Bank 12.55 15.33 15.89 13.85 14.38 14.40 13.83 12.09 15.5 15.31 15.03 14.35

10 Jammu &

Kashmir Bank 14.48 15.89 13.72 13.36 12.83 14.06 12.69 12.57 11.85 10.8 11.42 11.87

11 Karnataka

Bank 13.48 12.37 13.33 12.84 13.22 13.05 13.2 12.41 12.03 13.3 12.04 12.60

12 Karur Vysya

Bank 14.92 14.49 14.41 14.41 14.41 14.53 12.59 14.62 12.17 12.54 14.43 13.27

13

Kotak

Mahindra

Bank

20.01 18.35 19.92 17.52 16.05 18.37 18.83 17.81 16.28 16.77 18.2 17.58

14 Lakshmi Vilas 10.29 14.9 13.19 13.1 12.32 12.76 10.9 11.34 10.67 10.38 9.81 10.62

271

Bank

15 Nainital Bank 13.1 15.68 16.35 15.09 14.43 14.93 15.13 14.86 15.69 12.78 14.95 14.68

16 R B L Bank 42.3 34.07 56.41 23.2 17.11 34.62 14.64 13.52 12.94 13.72 15.33 14.03

17 South Indian

Bank 14.86 15.39 14.01 14 13.91 14.43 12.42 11.92 11.82 12.37 12.7 12.25

18 Yes Bank 16.6 20.6 16.5 17.9 18.3 17.98 14.4 19.7 16.5 17 18.4 17.20

15.83 14.03

Source: : Secondary data from Indiastat.com

A.60: Result of Leverage Ratio from 2009-2018

Minimum Common Equity Leverage ratio

Sr. No.

Public banks 2014 2015 2016 2017 2018 2016 2017 2018

1 Allahabad Bank 7.35 7.57 8.3 8.2 5.57 5.35 5.22 3.65

2 Andhra bank 7.96 7.55 7.83 7.69 5.61 5.52 5.9 4.45

3 Bank of Baroda 8.95 9.8 10.78 9.64 10.08 4.8 4.78 5

4 Bank Of India 6.99 7.59 8.31 7.71 8.52 5.83 5.65 5.8


6 Canara Bank 7.41 7.37 8.16 9.03 9.63 4.9 5.24 5.52


8 Corporation Bank 7.66 7.37 7.3 7.99 8.1 --- --- ---

9 Dena Bank 7.43 7.29 7.35 7.43 7.11 --- --- ---

10 I D B I Bank Ltd. 7.78 7.29 7.98 5.63 7.42 --- --- ---

11 Indian Bank 8.25 7.35 7.5 8.3 8.43 --- --- ---


13 Oriental Bank Of

Commerce 8.3 8.09 8.52 7.59 7.46 --- ---- ----

14 Punjab & Sind Bank 7.27 8.48 8.48 9.14 7.65 4.7 5.4 4.93

15 Punjab National Bank 9 9.14 8.48 8.17 5.95 5.88 5.09 3.85


17 Syndicate Bank 8.29 7.53 7.01 7.5 7.56 4.08 5.17 5.04

18 Uco Bank 8.5 8.94 7.52 7.64 8.23 3.9 4.11 4.18

19 Union Bank Of India 7.28 7.25 7.96 7.76 7.64 5 5.4 4.96


21 Vijaya Bank 8.12 7.6 8.31 8.44 10.36 5.3 5.36 6.26

Sr.

No. Private banks

1 Axis Bank ---- ----- ----- ---- 11.8 8.33 8.22 8.64

2 Catholic Syrian Bank ---- ----- ---- ----- 9.45 ---- ---- 4.32

3 City Union Bank ---- ----- ----- ---- 13.92 9.09 9.42 9.82

4 D C B Bank ---- ----- ---- ----- ---- --- ---- 7.22

5 Dhanalaxmi Bank ---- ----- ----- ---- ---- 3.13 4.26 4.92

6 Federal Bank ---- ----- ---- ----- 8.85 7.68 6.91 7.97

7 H D F C Bank ---- ----- ----- ---- 12.25 8.87 8.77 9.19

8 I C I C I Bank ---- ----- ---- ----- 14.43 9.06 9.79 9.83

272

9 Indusind Bank ---- ----- ----- ---- 13.42 9.33 9.26 9.02

10 Jammu & Kashmir Bank ---- ----- ---- ----- ----- ---- ---- 6.12

11 Karnataka Bank ---- ----- ----- ---- ---- 6 6.83 6.65

12 Karur Vysya Bank ---- ----- ---- ----- 11.85 6.54 7.15 8.49

13 Kotak Mahindra Bank ---- ----- ----- ---- 17.5 12.1 12.6 13.27

14 Lakshmi Vilas Bank ---- ----- ---- ----- 8.05 ---- ---- 9.81

15 Nainital Bank ---- ----- ----- ---- ----- 8.21 7.25 7.24

16 R B L Bank ---- ----- ---- ----- ----- 6.58 7.31 8.87

17 South Indian Bank ---- ----- ----- ---- ------ 5.55 5.99 5.78

Yes Bank ---- ----- ---- ----- 9.7 6.6 8.84 8.54


273

Publications

1) Verlekar R. (2015) “Challenges in Basel III Accord in the Risk Management for

Indian Banking Industry” International Journal of Multidisciplinary Research,

Vol – IV, Issue 6 (VI) ISSN No. 2277-9302.

2) Verlekar R. and Kamat M. (2015), “Credit Risk Management in Indian Banks: An

Exploratory Study” International Journal of Multidisciplinary Research, Vol IV

issue 6 (VIII) ISSN No. 2277-9302.

3) Verlekar R. and Kamat M. (2016), “Advancement in Performance of Basel

Regulatory Framework in Selected Developed and Developing Nation” Asian

Journal of Research in Banking and Finance, July 2016, ISSN No. 2249 -7323 pp

13-21 UGC approved S. No. 1259.

4) Verlekar R. and Kamat M. (2017), “Credit Risk Management Analysis Framework

of Indian Banks” An International Peer Reviewed Scholarly Research Journal for

Interdisciplinary Studies Oct-Dec 2017 Vol 6 Issue 34 Impact Factor 6.177 pp no

65-70 UGC approved S. No. 49366.

5) Verlekar R. and Kamat M. (2019),“Measurement and Comparison of Credit Risk

Management Practice of Public and Private Banks Using Lending Ratios”

International Journal of Management Studies, Vol –VI, Issue 2(1), April 2019. UGC

approved S.No. 4925 ISSN(Print)2249-0302ISSN(Online)2231-2528. pp 48-59.

6) Verlekar R. and Kamat M. (2019), “An Application and Comparison of

Bankruptcy Model in Indian Banking Sector” International Journal of Financial

Management, October 2019 Vol 9 Issue 4 pp 42-53.

7) Verlekar R. and Kamat M. (2019), “Recalibration and Application of Springate

Zmijewski and Grover bankruptcy Model” Peer Reviewed International Journal

of Business Analytics and Intelligence, UGC approved S.No. 47998- Vol 7- Issue

2, October 2019 pp 20-36.

8) Verlekar R. and Kamat M. (2019), “An Application and Recalibration of Altman

Z-Score Model for Forecasting the Banking Bankruptcy in India” Wealth

Publications- International Journal of Money Banking and Finance, ISSN 2277-

9388, Dec 2019 Vol 8 Issue 2,

file:///C:/Users/user/Documents/Intercontinental%20journal%20of%20finance%20and%20%20Research%20review.docx




274

9) Verlekar R., Kamat M. and Kamat M. (2020) “Basel III Requirements and

Compliance by the Indian Banking Sector” International Journal of Advanced

Science and Technology, Scopus Coverage Vol 129 - No 3, ISSN 2005 -4238,

August 2020 pp 1838-1847.

10) Verlekar R. And Kamat M.(2020) “Measurement of Credit Risk in Indian Banking

Sector using Financial Ratios” Aegaeum Journal- UGC Care Approved Group II,

Vol 8 – Issue 8 (Impact Factor 6.2) pp 1617-1640.

11) Verlekar R. and Kamat M.(2020), “Empirical Investagation on Preparedness of

Basel III Norms in the Indian Banking Sector” Zeichen Journal - UGC Care

Approved Group II, Scopus Coverage Vol 6 Issue 11 November 2020, ISSN

0932 – 4747 Impact factor 4.7 pp 33-47.

12) Verlekar R. and Kamat M. (2020), “Analysis of Relationship between Financial

Variables of Indian Banking Sector” Akshar Wangmay International Peer

Reviewed Research Journal UGC Care Approved Special issue Volume IV,

December 2020, ISSN 2229 – 4929 pp 141-145

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