Post on 09-Jan-2023
Electronic copy available at: http://ssrn.com/abstract=1150968
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CAMEL(S) AND BANKS PERFORMANCE EVALUATION: THE WAY FORWARD.
BY
WIRNKAR A.D. AND TANKO M.
Electronic copy available at: http://ssrn.com/abstract=1150968
2
ABSTRACT
Despite the continuous use of financial ratios analysis on banks performance evaluation
by banks’ regulators, opposition to it skill thrive with opponents coming up with new
tools capable of flagging the over-all performance ( efficiency) of a bank. This research
paper was carried out; to find the adequacy of CAMEL in capturing the overall
performance of a bank; to find the relative weights of importance in all the factors in
CAMEL; and lastly to inform on the best ratios to always adopt by banks regulators in
evaluating banks’ efficiency. The data for the research work is secondary and was
collected from the annual reports of eleven commercial banks in Nigeria over a period of
nine years (1997 – 2005). The purposive sampling technique was used. The presentation
of data was in tables and analyzed via the Efficiency Measurement System (EMS) 1.30
software of Holger School and independent T-test equation. The findings revealed the
inability of each factor in CAMEL to capture the wholistic performance of a bank. Also
revealed, was the relative weight of importance of the factors in CAMEL which resulted
to a call for a change in the acronym of CAMEL to CLEAM. In addition, the best ratios
in each of the factors in CAMEL were identified. For example, the best ratio for Capital
Adequacy was found to be the ratio of total shareholders’ fund to total risk weighted
assets. The paper concluded that no one factor in CAMEL suffices to depict the overall
performance of a bank. Among other recommendations, banks’ regulators are called upon
to revert to the best identified ratios in CAMEL when evaluating banks performance.
Electronic copy available at: http://ssrn.com/abstract=1150968
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Introduction
It is usual to measure the performance of banks using financial ratios. Often, a
number of criteria such as profits, liquidity, asset quality, attitude towards risk, and
management strategies must be considered. In the early 1970s, federal regulators
in USA developed the CAMEL rating system to help structure the bank
examination process. In 1979, the Uniform Financial Institutions Rating System
was adopted to provide federal bank regulatory agencies with a framework for
rating financial condition and performance of individual banks (Siems and Barr;
1998). Since then, the use of the CAMEL factors in evaluating a bank’s financial
health has become widespread among regulators. Piyu (1992) notes “currently,
financial ratios are often used to measure the overall financial soundness of a bank
and the quality of it management. Bank regulators, for example, use financial
ratios to help evaluate a bank’s performance as part of the CAMEL system”. The
evaluation factors are as follows;
C → Capital adequacy
A → Asset quality
M → Management quality
E → Earnings ability
L → Liquidity.
Each of the five factors is scored from one to five, with one being the strongest
rating. An overall composite CAMEL rating, also ranging from one to five, is then
developed from this evaluation. As a whole, the CAMEL rating, which is
determined after an on-site examination, provides a means to categorize banks
based on their overall health, financial status, and management. The Commercial
Bank Examination Manual produced by the Board of Governors of the Federal
Reserve System in U.S describes the five composite rating levels as follows
(Siems and Barr, 1998).
CAMEL = 1 an institution that is basically sound in every respect.
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CAMEL = 2 an institution that is fundamentally sound but has modest
weaknesses.
CAMEL = 3 an institution with financial, operational, or compliance
weaknesses that give cause for supervisory concern.
CAMEL = 4 an institution with serious financial weaknesses that could
impair future viability.
CAMEL = 5 an institution with critical financial weaknesses that render
the probability of failure extremely high in the near term.
In Nigeria, commercial banks are examined annually for safety and
soundness by the Banking Supervision Department of the Central Bank of Nigeria
(CBN).
Statement of the Problem
Bank’s performance or rather solvency or insolvency has been given much
attention both at the local and international level. Financial ratios are often used to
measure the overall financial soundness of a bank and the quality of its
management. Banks’ regulators, for example, use financial ratios to help evaluate
a bank’s performance as part of the CAMEL system (YUE, 1992). Despite
continuous use of ratios analysis in banks performance appraisal by regulators, opponents
to it still thrive. Financial ratios are somewhat limited in scope, that is, simple gap
analysis are one dimensional views of a service, product, or process that ignore
any interactions, substitutions or trade-offs between key variables (Siems and
Barr, 1998).
According to David A. and Vlad M. (2002), “Studies on productivity growth in
the banking sector usually base their analysis on cost ratio comparisons. There are
several cost ratios to be used and each one of them refers to a particular aspect of
bank activity. Since the banking industry uses multiple inputs to produce multiple
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outputs, a consistent aggregation may be problematic. Some attempts have been
made to estimate average practice cost functions. While these approaches were
successful in identifying the average practice productivity growth, they failed to
take into account the productivity of the best practice banks. These problems
associated with the classical approach to productivity led to the emergence of
other approaches which incorporate multiple inputs/outputs and take into account
the relative performance of banks”.
Hypotheses
HO1: There is no significant difference between banks’ efficiency and capital adequacy.
HO2: There is no significant difference between banks’ efficiency and asset quality.
HO3: There is no significant difference between bank’s efficiency and management
quality.
HO4: There is no significant difference between bank’s efficiency and earnings ability.
HO5: There is no significant difference between bank’s efficiency and liquidity.
Literature Review
Bank’s performance or rather solvency or insolvency has been given much
attention both at the local and international level. Financial ratios are often used to
measure the overall financial soundness of a bank and the quality of its
management. Banks’ regulators, for example, use financial ratios to help evaluate
a bank’s performance as part of the CAMEL system (YUE, 1992). Empirical
evidence on the use of ratios for banks’ performance appraisal include; Beaver
(1966), Altman (1968), Maishanu (2004), Mous (2005).
Beaver (1966) was the first person to use financial ratios for predicting bankruptcy
his study was limited to looking at only one ratio at a time. Altman (1968)
changed this by using a multiple discriminant analysis (MDA). His analysis
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combined the information from several financial ratios in a single prediction
model. Altman’s z- score model was the result of this multiple discriminant
analysis and has been popular for a number of decades as it was easy to use and
highly accurate. But there was critique on the MDA model. Altman treated
businesses from different sectors as the same, ignoring the fact that there should be
different values for a healthy indication by the financial ratios of the different
kinds of businesses.
Maishanu (2004) identified eight financial ratios that could serve in
informing financial analysts on the financial state of a bank. As such, he put forth
a univariate model for predicting failure in commercial banks.
In comparing two bankruptcy predicting models using financial ratios,
(Mous, 2005) found that the decision tree approach performed better than the
multiple discriminant analysis (MDA) with decision tree correctly classifying 89%
of bankrupt banks within two years while multiple discriminant analysis (MDA)
got 81%. The financial ratios used had variables; profitability, liquidity, leverage,
turnover and total assets.
The changing nature of the banking industry has made such evaluations
even more difficult, gingering the need for more flexible alternative forms of
financial analysis. These are the parametric methods of; the stochastic frontier
approach (SFA), the thick frontier approach (TFA) and the distribution freehall
approach (DFA); and the non parametric method of data envelopment analysis
(DEA).
The empirical evidence of the parametric approaches are; Asaftei (2003),
Limam (2002) while the empirical evidence of the non parametric of data
envelopment analysis (DEA) are; Cinca and Molinero (2001), Cinca et al (2002),
Sathye (2001), Yue (1992), Grigorian and Manole (2002), Su (2004) and Tanko
(2006), Wirnkar ( 2007), Wirnkar and Tanko (2007).
7
In selecting DEA specifications and ranking units via PCA, Cinca and
Molinero (2000) were able to identify and rank the 18 Chinese cities in terms of
efficiency in utilizing inputs to produce outputs. Maverick cities were also
identified that could ignite any directed economic reform by the Chinese
government.
In evaluating the performance of 60 Missouri Commercial Banks between
(1984 – 1990) using DEA with an intermediary approach of inputs (interest
expenses, non-interest expenses, transaction deposits, and non-transaction
deposits) with outputs (interest income, non-interest income and total loans), Yue
(1992) found that while five of the Missouri banks were technologically efficient,
they were not operating at the most efficient scale of operation.
Measuring Inputs and Outputs of Banks
In the banking literature, there has been some disagreement on the
definition of banks’ inputs and outputs and how they could be measured. Su
(2004), Mlima and Hjalmarsson (2002), Sathye (2001). These terms from the
quantum of services banks provide as well as the different views regarding the
treatment of such services as inputs and/or outputs. Banks mostly provide
customers with low risk assets, credit and payment services, and play an important
role as intermediaries in directing funds from savers to borrowers. They also
perform non-monetary services such as protection of valuables, accounting
services and running of investment portfolios (Colwell and Davis, 1992) in
(Mlima and Hjalmarrson; 2002).
Ahmed (1999) identifies these services to include the following:
1) Deposit collection through savings account, current account and fixed deposit
account.
2) Provision of credit to customers in form of loans, overdraft, advance, bill
discounting, leasing, acceptance of bills, bonds and guarantees.
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3) Money transmission services such as cheque, mail transfer, telegraphic transfer
etc.
4) Provision of financial services such as tax administration stock exchange
services, insurance services, investment advisory services, business advisory
services, status enquiry, safe custody, administration of Wills etc.
5) Foreign services, such as travelers’ cheque, foreign currency, foreign draft,
mail transfer, telegraphic transfer, letter of credit, bills of collection and
international settlement.
Despite the disagreement as to the definition of inputs and outputs in the
banking industry, there is a general agreement in the literature among authors on
two main approaches that could be used to define the input and output variables in
the spectrum of services that banks provide. These two approaches are the
production approach and the intermediation approach (Berger and Humphrey
1997), (Piyu, 1992), (Sathye 2001), (Su Wu 2004), (Mlima and Hjalmarrson,
2002).
Some authors call the production approach, Service Provision or Value
Added Approach (Grigorian and Manole, 2002).
Methodology
The purposive sampling method is used to select eleven (11) of the 25 mega banks
in Nigeria. The bases used were as follows:
(1) They are the ten largest banks in terms of assets values in 2005 except Access
Bank (Banking Supervision Annual Report 2005).
(2) They rank first from market price per share indices in the banking industry.
(3) They successfully emerged from the just concluded consolidation in the
banking industry with originality of their names. That is, they either acquired
other bank (s) to meet the required capitalization base of 25 billion naira or
they individually met the required amount. The only exception is UBA group
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that merged with Standard Trust Bank (STB) plc but, this bank satisfied the
other two bases.
The data for this research work is secondary and will be extracted from the Annual
Reports of the banks for a period of nine (9) years (1997-2005). The data except
number of employees is measured in monetary units. The description of the data will be
as follows:
Input A: Number of employees
Input B: Fixed Assets
Input C: Deposits
Output 1: Operating Income
Output 2: Deposits
Output 3: Loans
In this way, specifications are defined as inputs mapping onto outputs. As such, a
specification whose employees (input A) take deposits (output 2) and place loans in the
market (output 3) would be labeled A23. If this specification is augmented with fixed
assets (input B) and operating income (output 1) the specification becomes AB123.
Specification AB123 treats a deposit bank as a production unit that employs manpower
(A) and plant (B) in order to generate income, deposits, and loans. An intermediation
model would be described by a specification such as AC13 in which deposits (C) are
treated as an input. Under this specification, a deposit bank is an institution whose
employees collect deposits in order to make loans and generate income.
Other possible ways in which a commercial bank operates give rise to different
specifications. In all, there are 33 specifications as follows:
Specification Input Output
A1 Employees Income
A12 Employees Income, Deposit
A123 Employees Income, Deposit, Loans
A13 Employees Income, Loans
A23 Employees Deposit, Loans
‘’ ‘’ ‘’
10
‘’ ‘’ ‘’
‘’ ‘’ ‘’
ABC Employees Loans
This is followed by the calculation of the efficiency scores using DEA for all
specifications. Efficiency scores are on a scale of 0% to 100% for all commercial banks
for the period under constant return to scale at maximum average with the Efficiency
Measurement System (EMS) 1.30 of Holger Scheel. See www.wiso.uni-
dortmund.de/lsfg/or/scheel/ems/.
Next is followed by the calculation of average Capital Adequacy ratios and
average Sub-Capital Adequacy ratios for the test of the main hypothesis and Sub-
hypotheses under Capital Adequacy. Then the calculation of the of average Asset Quality
ratios and average Sub-Asset Quality ratios for the test of the main hypothesis and Sub-
hypotheses under Asset Quality up until the calculation of average liquidity ratios and
average sub-liquidity ratios for the test of the last (5th
) hypothesis and Sub-hypotheses.
The various CAMEL ratios are as follows:
CAPADR 1 – A ratio of total assets to total shareholders’ funds. It shows the
extent to which total assets are supported by shareholders’ funds. The lower the
value of this ratio, the better the financial health of a bank.
CAPADR 2 – A ratio of total shareholders’ funds to total assets. It shows the
proportion of a unit naira of equity to a unit naira of asset. The higher the value of
this ratio, the better the financial health of a bank.
CAPADR 3 A ratio of total shareholders’ funds to total net loans. It shows the
proportion of shareholders funds in granting loans. The higher the value of this
ratio, the better the financial health of a bank.
CAPADR 4 – A ratio of total shareholders’ funds to total deposits. It shows the
capacity of shareholders’ funds to withstand sudden withdrawals. The higher the
value of this ratio, the better the financial health of the company.
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CAPADR 5 – A ratio of shareholders’ funds to contingency liabilities. This
measures the extent to which a bank carries off-balance sheet risks. The higher the
value of this ratio, the better the financial health of a bank.
CAPADR 6 – A ratio of total shareholders’ funds to total risk weighted assets (non
performing loans). It measures the ability of a bank in absorbing losses arising
from risk assets. The higher the value of this ratio, the better the financial health of
a bank.
The Asset Quality ratios are divided into two. The mean of these ratios will
be used.
ASSETQR 1 – A ratio of loan loss provision to total net loans. This ratio shows
the ability of a bank to meet further losses on total net loans. The higher the value
of this ratio, the worsening the financial health of a bank.
ASSETQR2 – A ratio of loan loss provision to gross loans. It measures the ability
of a bank to meet further losses on gross loans. The higher the value of this ratio,
the worsening the financial health of a bank.
The management quality ratio is defined from the perspective of risk in
Asset portfolio (mix). The only ratio here is a ratio of total of risk weighted assets
to total assets. The higher the value of this ratio, the worsening the financial health
of a bank.
The Earnings Ability ratios are two. The average of these ratios will be
used. These are:
EargAR1 (ROA) – A ratio of net profit after tax to total assets. It measures a unit
yield of profit to a unit value of assets. The higher the value of this ratio, the better
the financial health of a bank.
EargAR2 (ROE) – A ratio of net profit after tax to total shareholders’ funds. It
measures a unit yield of profit to a unit value of total shareholders’ funds. The
higher the value of this ratio, the better the financial health of a bank.
The Liquidity ratios are three. The average of these ratios will be used.
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Liq R1 – A ratio of total net loans to total deposits. It shows how far a bank has
tied up its deposits in less liquid assets. The higher the value of this ratio, the
weaker the financial health of a bank.
Liq R2 – A ratio of demand liabilities to total deposits. This shows the portion of
total deposits that is in the risk of sudden withdrawals.
Liq R3 – A ratio of gross loans to total deposit. It shows how a bank has tied its
deposit in less liquid assets. The higher the value of this ratio, the weaker the
financial health of a bank.
From the above breakdown of CAMEL ratios, we shall have altogether
fourteen hypotheses (five main hypotheses and thirteen sub-hypotheses).
The main hypotheses will inform on the relative weight of each factor in
CAMEL to capture the wholistic efficiency of a bank.
The Sub-hypotheses will inform us on the best ratios to be used for each of
the factors in CAMEL.
All the hypotheses will be tested using the Independent T-test equation for
testing the difference between means (x).see Chambers and Crawshaw, (1990)
/ Z / = (x1 – x2) – (µ1 – µ2)
+
n1 n2
Where / Z / = magnitude of
(1) Capital adequacy
(2) Asset quality
(3) Management quality
(4) Earning ability
(5) Liquidity
X1 = mean of:
(6) Capital adequacy
(7) Asset quality
δ12 δ2
2
13
(8) Management quality
(9) Earnings ability
(10) Liquidity
X2 = mean of gross efficiency scores (performance).
µ1 = µ2 = mean of Population (number of banks)
= Variance of;
(1) Capital adequacy
(2) Asset quality
(3) Management quality
(4) Earnings ability
(5) Liquidity
= Variance of gross efficiency scores (performance)
n1 = numerical number of;
(1) Capital adequacy
(2) Asset quality
(3) Management quality
(4) Earnings ability
(5) Liquidity
n2 = numerical number of gross efficiency scores (performance)
Decision rule:
The Null hypothesis is accepted if the magnitude / Z / falls within / Z /> 1.96 that
is
-1.96 <Z< +1.96 at 5% level of significance otherwise the Alternative hypothesis
is accepted. The two tailed test will be used.
This will be followed by the interpretation of the findings, conclusion,
recommendations, bibliography and lastly appendices.
δ12
δ22
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The Data Envelopment Analysis (DEA)
DEA is a multi-factor productivity analysis model for measuring the relative efficiencies
of a homogenous set of decision making units (DMUs). Talluri (2000) states that the
efficiency score in the presence of multiple input and output factors is defined as:
Efficiency = Weighted sum of outputs
Weighed sum of inputs
The procedure for finding the efficiency scores of decision making units (DMU)
is formulated as a linear programming problem. Assuming that there are n DMUs, each
with M inputs and S outputs, the relative efficiency score of a test DMU p is obtained by
solving the following model proposed by Charnes et al (1978).
max
s.t
Where
k = l to s,
j = l to m,
I = l to n,
Yki = amount of output K produced by DMUi,
k = 1
s
Vk Ykp
j = 1
m
Uj Xjp
k = 1
s
Vk Ykp
j = 1
m
Uj Xjp
< 1 i
Vk, Uj > 0 k, j,
15
Xji = amount of input j utilized by DMUi,
Vk = weight given to output K,
Uj = weight given to input j.
The fractional problem shown as (2) can be converted to a linear program as
shown in (3). For more details on model development, see Charnes et al (1978).
max
s.t
4.0 Data Presentation, Analysis and Interpretation
This section presents analyses and interprets the data.
TABLE I: AVERAGES OF KEY VARIABLES PER BANK PER PERIOD ( 1997-2005)
(please see the last table in landscape)
k = 1
s
Vk Ykp
j = 1
m
Uj Xjp = 1
k = 1
s
Vk Ykp < 0 i
Vk, Uj > 0 k, j,
j = 1
m
Uj Xjp
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TABLE II: RESULTS OF THE TEST OF HYPOTHESES CAMEL RATIOS CAMEL
RATIOS EFFICIENCY
SCORES (X) /Z/ DECISION
RULE SUB CAMEL
RANkING CAMEL
RANKING AV CAPAR 3.3658 90.52 -12.5620 Reject Ho 1
st
AV CAPAR 1 12.4293 90.52 -9.3464 Reject Ho 2nd
AV CAPAR 2 0.09866 90.52 -13.2321 Reject Ho 6
th
AV CAPAR 3 0.4441 90.52 -13.1688 Reject Ho 4th
AV CAPAR 4 0.1535 90.52 -13.2238 Reject Ho 5
th
AV CAPAR 5 0.8068 90.52 -12.7329 Reject Ho 3rd
AV CAPAR 6 6.3081 90.52 -8.3320 Reject Ho 1
st
AV ASSET QR 0.1094 90.52 -13.2297 Reject Ho 4th
AV ASSET QR 1 0.1738 90.52 -13.2189 Reject Ho 1
st
AV ASSET QR 2 0.0450 90.52 -13.2399 Reject Ho 2nd
AV MGQR 9.0518 90.52 -13.2389 Reject Ho 5
th
AV EARGAR 0.1941 90.52 -13.2129 Reject Ho 3rd
AV EARGAR 1 0.0270 90.52 -13.2427 Reject Ho 2
nd
AV EARGAR 2 0.3611 90.52 -13.1736 Reject Ho 1st
AV LI QR 0.5122 90.52 -13.1708 Reject Ho 2nd
AV LI QR 1 0.4212 90.52 -13.1831 Reject Ho 3
rd
AV LI QR 2 0.638690.5
2 90.52 -13.1519 Reject Ho 1
st
AV LI QR 3 0.4769 90.52 -13.1753 Reject Ho 2nd
SOURCE: AUTHOR’S COMPUTATION. SEE APPENDIX F
From the above table, the following findings are unveiled:
That no factor in CAMEL is able to capture the wholistic efficiency of a bank. This is
evidenced by the rejection of the null hypotheses in all the main and sub-hypotheses. The
proximal weights or ability of each factor in CAMEL to capture the wholistic
performance of a bank are ascertained. This yielded an order in ranking the factors in
CAMEL to CLEAM. As such, giving us a new acronym for CAMEL as CLEAM to
reflect the ability of each of the factors to capture a wholistic performance of a bank.
In consideration of sub-hypotheses under capital adequacy, the best ratio is CAPAR 6
which is a ratio of total shareholders fund to total risk weighted assets. The other five
capital adequacy ratios are rated accordingly. In consideration of the asset quality ratios,
Asset quality ratio1 comes first. This is a ratio of Loan Loss provision to total net loans.
And lastly, Assets Quality ratio 2.
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We also found that the best Earnings Ability ratio is the ratio of net profit after tax to total
shareholders fund. The best liquidity ratio is liquidity ratio 2, a ratio of demand liabilities
to total deposit.
Conclusions
The following table summarizes our findings:
TABLE 3: CAMEL RATINGS OF THE BEST RATIO UNDER EACH FACTOR
C CAPITAL ADEQUACY RATIO The ratio of total shareholders’ fund to total
risk weighted assets.
L LIQUIDITY RATIO The ratio of demand liabilities to total deposit
E EARNING ABILITY RATIO The ratio of net profit after tax to total
shareholders’ fund
A ASSET QUALITY RATIO The ratio of loan loss provision to total net
loans
M MANAGEMENT QUALITY RATIO The ratio of risk weighted assets to total assets
Source: Author’s Tabulation from the Test of Hypotheses. See Results In Table Ii
The way forward is to stick to the above acronym (CLEAM) and consider in particular
the identified ratios under each factor.
The following recommendations are made.
♦First, the acronym of CAMEL should be changed to CLEAM in order to reflect the
weight in importance in each of the factors.
♦The best capital adequacy ratio to be used by banks’ regulators should be the ratio of
total shareholders’ fund to total risk weighted assets.
♦The best liquidity ratio to be used by banks’ regulators should be the ratio of demand
liabilities to total deposit.
♦The best Earnings ability ratio to be used by banks’ regulators should be the ratio of net
profit after tax to total shareholders’ fund.
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♦More so, the best Asset Quality ratio is the ratio of Loan Loss Provision to total net
loans.
♦And lastly, the best management quality ratio is the ratio of risk weighted assets to total
assets.
♦It is also recommended that more research be conducted in this area of banks’
performance evaluation. Other versions of DEA such as the DEA solver pro, Frontier
Analyst, Onfront, Warwick DEA, DEA Excel Solver, DEAP and Pioneer should be
explored in application in the banking industry.
Bibliography
Altman I. Edward (1968) : Financial Ratios, Discriminant Analysis and Prediction
of Corporate Bankruptcy in Journal of Finance,
September, 1968, New York University.
Annual Reports and Accounts of the banks (1997-2006)
Berger A.N, and Humphrey, D.B., (1997): Efficiency of financial
Institutions: International Survey and Directions for
Future research, European Journal of Operational
Research, University of Pennsylvania
Cinca et al (2002): Behind DEA Efficiency in Financial Institutions:
Discussion Paper in Accounting and Finance, Number
AFO2-7, School of Management, University of
Southampton
Crawshaw J.and Chambers J. (1984): A Concise Course in A-Level Statistics,
Second Edition, Bath Press, Avon, Britain.
David A. Grigorian and Vlad Monola (2002:5&6) Determinants of Commercial
Bank
Performance in Transition: An Application of Data
Envelopment Analysis, World Bank Policy Research
Working Paper 2850, June 2002.
19
Efficiency Measurement System (EMS) 130 of Holger Scheel at www.wiso.uni-
dortmund.de/lsfg/or/scheel/ems/
Gabriel Asaftei (1995) and Kumbhakar(1995) @ www.fma-
org/siena/papers/720495.pdf Regulation and
Efficiency of banking in a transition
economy,Department of Economics, University of
Richmond, VA, 23179, USA.
Imed Limam (2002): Measuring Technical Efficiency of Kuwaiti Banks:
Arab Planning Institute, Kuwait.
Kuru Lawan Ahmed (1999):The Marketing of Banking Services in the Current
Competitive Environment: A Case Study of Habib
Nigeria Bank Ltd
Lonneke Mous (2005): Predicting bankruptcy with discriminant analysis and
decision tree using financial ratios, University of
Rotterdam.
Malami M. Maishanu (2004:76): “ A univariate Approach to Predicting failure in
the Commercial Banking Sub-Sector” in Nigerian
Journal of Accounting Research, Volume 1, Number 1,
Department of Accounting, Ahmadu Bello University,
Zaria.
Milind Sathye (2001): Efficiency of Banks in a Developing Economy: School
of Accounting and Finance, University of Canberra.
Mlima and Hjalmarrsom (2002): Measurement of Inputs and Outputs in Banking
Industry: Tanzanet Journal (2002), Volume 3(1),
University of Gothenburg.
Muhammad Tanko (2004): “A Data Envelopment Analysis of Banks Performance
in Nigeria.” In Nigerian Journal of Accounting
20
Research, Volume 1, Number 4, Department of
Accounting, Ahmadu Bello University, Zaria
Piyu Yue (1992:31): Data Envelopment Analysis and Commercial Bank
Performance: A Primer with Applications to Missouri
Banks, IC2
Institute, University of Texas at Austin.
Serrono C. et al (2001): Selecting DEA Specifications and Ranking Units via
PCA: Discussion Papers in Management, Number
MO1-3, and University of Southampton.
Thomas F. Siems and Richard S. Barr (1998): Benchmarking the Productive
Efficiency of U.S. Banks: Financial Industry Studies,
Federal Reserve Bank of Dallas.
Wirnkar A.D. and Tanko M (2007): “A post consolidation Appraisal of Commercial
Banks Efficiency in Nigeria”. In Nigerian Journal of
Accounting Research, Volume , Number , Department
of Accounting, Ahmadu Bello University, Zaria
21
TABLE 7
SUMMARY OF AVERAGE KEY VARIABLES PER BANK PER PERIOD
S
/
N
BAN
KS
AV
CAPA
R I
AV
CAPA
R II
AV
CAPA
R III
AV
CAPA
R IV
AV
CAPA
R V
AV
CAPA
R VI
AV
CAPA
R
AV
ASET
QR I
AV
ASET
QR II
AV
ASET
QR
AV
MGA
R
AV
EAR
I
AV
EAR
II
AV
EAR
AV
LIQR
I
AV
LIQR
II
AV
LIQR
III
AV
LIQ
R
AV.EFFI
CIENCY
SCORES
1 ACB 8.2504 0.1310 0.4120 0.2397 0.9089 3.0856 2.1713 0.1229 0.0416 0.0823 0.0514 0.0127 0.1162 0.0645 0.6084 0.6093 0.6830 0.63
36
83.84
2 AFB 19.254
8
0.718 0.2844 0.0980 0.6326 0.9788 3.5534 0.3395 0.0877 0.2136 0.0670 0.0080 0.1285 0.0683 0.3521 0.6956 0.4644 0.50
40
81.67
3 FBN 11.376
0
0.0915 0.3894 0.1361 0.7215 1.0261 2.2901 0.3850 0.0875 0.2363 0.0965 0.0250 0.2750 0.1500 0..3515 0.7410 0.4365 0.50
97
87.52
4 GTB 8.6227 0.1221 0.3453 0.2131 0.6824 15.118
6
4.1840 0.0370 0.0133 0.0252 0.0090 0.0405 0.3477 0.1941 0.6190 0..5680 0.5676 0.58
49
100
5 ICB 7.4299 0.1409 1.3836 0.2070 1.7374 4.1031 2.5003 0.0665 0.0177 0.0421 0.0503 0.0407 0.2984 0.1696 0..3475 0..3865 0.3725 0.36
88
94.57
6 OCB 23.274
6
0.0568 0.2724 0.0790 -3.7283 4.9293 4.1473 0.2506 0.0684 0.1595 0.0831 0.0538 1.5304 0.7921 0.3412 0.7216 0.4327 0.49
85
100
7 UBA 16.217
1
0.0655 0.3197 0.0918 0.4477 4.2836 3.5709 0.1729 0.0359 0.1044 0.0299 0.0133 0.2046 0.1090 0.2959 0.6553 0.3463 0.43
25
100
8 UB 11.540
1
0.0891 0.4498 0.1312 0.8830 1.3502 2.4072 0.3248 0.0684 0.1966 0.0778 0.0202 0.2334 0.1268 0.3003 0.6176 0.3979 0.43
86
83.90
9 ZB 9.6517 0.1049 0.3937 0.1859 1.9710 26.538
6
6.4743 0.0272 0.0074 0.0173 0.0042 0.0388 0.3727 0.2058 0.4782 0.7098 0.4913 0.55
98
89.90
1
0
DB 10.503
6
0.0991 0.3522 0.1493 0.7409 6.2513 3.0161 0.0404 0.0109 0.0257 0.0264 0.0259 0.2753 0.1506 0.3958 0.6466 0.4342 0.49
22
90.99
1
1
WB 10.101
5
0.1118 0.2820 0.1572 3.8776 1.7240 2.7090 0.1453 0.0557 0.1005 0.0746 0.0180 0.1896 0.1038 0.5432 0.6733 0.6198 0.61
21
83.36
∑AV
KV
136.72
24
1.0845 4.8845 1.6883 8.8747 69.389
2
37.023
9
1.9121 0.4945 1.2035 0.5702 0.2969 3.9718 2.1346 4.6331 7.0246 5.2462 5.63
47
995.75
X
12.429
3
0.0986 0.4441 0.1535 0.8068 6.3081 3.3658 0.1738 0.0450 0.1094 0.0518 0.0270 0.3611 0.1941 0.4212 0.6386 0.4769 0.51
22
90.75
SOURCE: RESEARCHER’S COMPUTATION ∑ = Summation, KV = Key Variation , AV = Average, X = Mean per key variable