Fundamental Analysis of Nepalese Stock Exchange Return using Fama- French

Three Factor Model

1. Introduction:

1.1 Background

After the adoption of economic liberalization in the early 90s, Nepalese

economy has witnessed sharp growth in both financial institution and service

sectors. Further, rise of Initial Public Offering and simultaneous increase in

trading in both volume and transaction at secondary market, has substantially

expanded the capital market with current total market capitalization worth

approximately Rs. 503,500 million. Today there are in total 228 companies

listed in Nepal Stock Exchange that encompasses several industries viz-

Commercial Banks, Finance Companies, Development Bank, Hotels, Hydropower and

Manufacturing Sector. Further 50 different brokers are involved in day to day

trading in stock exchange. In this context Nepalese Stock market has become a

source for large amount of data that can be test bed for analyzing and

verifying several established Portfolio Theories used in Fundamental Analysis

of Capital Market. Besides the Nepalese stock market being characterized by

mostly naïve investors, lack of information and market inefficiency provides

unique perspective for testing different financial models which hitherto has

been performed only in developed market scenario. On this regard this study is

an attempt to test and analyze Nepalese Stock Market using Fama-French Three

Factor Model, Fama and French (1992), and to determine its efficacy and

relevancy in Nepalese context.

1.2 Statement Of Problem

i. Does Nepalese Stock Exchange return can be explained using established

portfolio theories

ii. Can Fama-French Model be applicable in Nepalese Context

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iii. Whether Fundamental Analysis using three-factor model is more pertinent

for Nepalese Stock Market

1.3 Objective

Fama –French Model first devised by Eugene F.Fama and Kenneth R. French , Fama

and French (1992) ,explains the return in stock market based on three risk

factors viz- an overall market, book to market, and size/They found that the

new model was superior in explaining the average stock return in US market

than conventional Capital Asset Pricing Model. Outside of US however the model

has been sparsely tested. In India studies, Connor and Sehgal (2001) , have

shown that empirical result of three factor model appears to hold. Besides, a

strong evidence of its relevancy in global context has been established in

recent study which underlies the significance of local factors. In Nepalese

context, since the market is still fledgling there has been only one reported

study of its return done using the three factor model , Rana (2011). Further

as mutual funds have now entered the market it is important for portfolio

managers to evaluate the stock using latest and innovative model.

Thus the major objectives of this study are:

i. To Test the Fama-French Model in return of Nepalese Stock Exchange

ii. To Verify whether Nepalese stock return corresponds to established

portfolio theories

iii. To attempt to explain the variation and risk factor associated with

the return

iv. To address the seasonality issue in time series stock return

v. To carry out fundamental analysis of stock return using three factor

model

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2. Literature Review

Capital Asset Pricing Model by Sharpe (1964), Lintner (1965) and Mossin

(1968) provides a simple yet powerful framework to explain the relationship

between expected return on an asset and various components of risk

associated with it. The theory builds upon mean variance optimization of

portfolio first suggested by Markowitz (1952) and explains that return

premium of any financial asset over risk free return is directly

proportional to the systematic or non-diversifiable risk of given asset.

The quantum of which is measured by evaluating covariance of asset return

with overall market portfolio. The Sharpe-Lintner-Mossin CAPM equation can

be mathematically represented as:

E (Ri)=Rf+[E (Rm)−Rf ]βi .......................i)

βi= Cov(Ri,Rm ¿/σ2 ............................. ii)

where,

E (Ri)=¿ Expected Return on any Asset

Rf= Risk Free Rate

E (Rm) = Expected Return on Market Portfolio

βi= Asset's market beta that measures sensitivity of

asset's return to market return.

But despite its popularity its empirical validity was questioned since

early stages. For instance Miller and Scholes (1972) highlighted the

statistical errors associated in using individual securities for testing

CAPM. Later the problem was overcome in study by Black, Jensen and Scholes

(1972) by constructing portfolio of all the securities listed in Newyork

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stock exchange. Thus using portfolios rather than individual securities

greatly increased the precision of beta. However the CAPM model relied too

much in unrealistic assumption thus Roll (1977) claimed that CAPM cannot be

validly tested unless true market portfolio is known. According to him

following two scenarios may arise when resorting to market proxy while

evaluating CAPM:

i) Market proxy might be mean variance efficient when in actuality the

true market portfolio may be otherwise.

ii) Market proxy may be mean variance inefficient when in actuality the

true market is efficient.

Later on Fama and French (1992) showed that CAPM failed to explain the cross

section of average return in US stock from 1962- 1990. According to them the

risk premium of security is influenced by multifactor instead of single market

factor as proposed by CAPM. These factors includes size, value of the firm and

market risk. Thus exposure to market, size and value acts as proxy for

sensitivity to risk factors in the return. This assertion corroborated with

Banz (1981) which had indicated that size effect of stock as statistically

significant in explaining return alongside its betas. The study had shown that

over period of 1936-1977 on an average return from holding small stocks was

large. Similarly Rosenberg, Reid and Lanstein (1985) had shown that firms with

high ratios of book value to the market value of common equity have higher

returns than those with low book to market ratios. This was supported by Davis

(1994) that found the relationship between average return and BE/ME in US

stocks extends back till 1941. Fama and French (1995) provided an economic

foundation for the empirical relationship between average stock return and

size, and average stock return and book-to-market equity. The study explained

that the size and BE/ME proxy is directly correlated to profitability and

hence were able to better explain cross-section of average return as

described in Fama and French (1993).

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Mathematically the Fama-French three factor model can be represented by

equation below:

E (Ri)=Rf+[E (Rm)−Rf ]βi+E (SMB )si+E(HML)hi .......................iii)

where,

E(SMB): Small Minus Big represents risk premium associated with size

factor.

E(HML): High Minus Low represents risk premium associated with value

factor

si: sensitivity to size

hi: sensitivity to value

Several studies across various international market has since been carried

out that have either supported or contradicted the three factor model. For

instance Aksu and Onder (2003) showed that size and value effect were proxy

for firm specific risk in Istanbul Stock Exchange. Gaunt(2004) while

studying the application of three factor model in Australian market found

beta to be less than one and HML factor playing significant role in asset

pricing. Also Ajili (2005) suggests three factor model being superior to

CAPM in modeling stock returns in French market. Similarly Connor and

Sehgal (2001) found that all three Fama-French factors, market, size and

value have pervasive returns in Indian Stock market. Bahl (2006) on the

basis of adjusted coefficient of determination (R2) confirmed the efficacy

of three factor model over CAPM in explaining common variation in stock

returns in Indian Stock Exchange. The study showed that the adjusted R2 for

three factor model being 87% meanwhile CAPM being just 76%. Taneja (2010)

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indicated that though the efficiency of three factor model as being good

predictor cannot be ruled out in Indian context , there appears high degree

of correlation between the size and value factor returns.

In Nepal the capital market began back to 1936 when the shares of

Biratnagar Jute Mills Ltd was floated. As outlined in brief history of

Nepalese capital market in Gurung (2004) , the Security Exchange Centre

was established for promoting growth in capital market. It operated under

the Security Exchange Act that was enforced in 1984. In 1993 after the

establishment of multi-party democratic system the SEC was restructured and

policy level was divided into two distinct entities Security Board Of Nepal

(SEBON) and Nepal Stock Exchange (NEPSE). Since January 13, 1994 NEPSE

began its trading floor using "open out-cry" system for transactions. Thus

NEPSE being an emerging market having existed for only over one and half

decade, there hasn't been much study regarding the risk return relationship

among its trade. Dangol (2009) shows that Nepalese stock market is not

efficient in weak form. Besides Dangol (2008) indicates that swing in NEPSE

is highly correlated with political events and hence there is significant

linkage between common stock returns and political uncertainty. Further

study by Dangol (2009) shows that capital appreciation is principal

motivation for Nepsalese in general to invest in stock market. Meanwhile

according to (Chhatkuli 2010) while analyzing NEPSE in period of 1998-

2008, CAPM failed to predict the relationship between risk and return in

Nepalese market. Recently the study by Rana (2011) shows that though the

firm size does not explain the common stock return in context of Nepalese

market , the role of book-to-market equity and stock beta however appears

significant. Thus indicating the relevancy of three factor model in

Nepalese context.

3. Research Methodology

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The study type employed is correlation quantitative approach Walliman (2001)

and Clarke (2005), which attempts to explore and investigate possible

relationship of Nepalese stock exchange with market return, value factors and

size factor.

3.1 Method of Data Collection

Documentary method of data collection was employed for the study. All the

documents containing the data pertaining to Nepalese Stock market from 2002 to

2012 , available in the official website of Nepalese Stock Exchange were

collected. Meanwhile the risk free rate of return of the prevailing government

treasury bill for the period of 2007 Aug to 2012 Jul available in the

official website of central bank of Nepal (www.nrb.org.np) were also collected

in excel sheet.

Since the study requires both the stock returns and the risk free rate the

study period was thus selected from August 2007 to Jul 2012. The list of

enlisted companies in NEPSE for the study period were first enumerated from

the website in an excel sheet. From among them A listed companies with their

financial information regarding book value at the end of year were selected

for the study. Following table shows the number of company that were in

listed and number of company with available book value during each fiscal year

for which study was carried out.

Table 1 Data listed companies

2007-

2008

2008-

2009

2009-2010 2010-

2011

2010-

2012No. of Enlisted Company 142 159 176 207 216No. of A listed company

with available book value

67 71 62 116 116

The name of companies whose data were used in given fiscal years is listed in

Appendix A.

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The data included were monthly stock returns of the company, yearly market

capitalization, yearend book value of A listed companies and the risk free

rate of return for the period of 60 months.

3.2 Data Validation

For the purpose of data validation financial data such as book value was cross

check against the annual reports of the five random companies viz- Nabil Bank

Ltd, Nepal telecom, Laxmi Bank, SIddhartha Insurance and KIST bank. All the

figures in annual report were matched with the data recorded from Nepal Stock

Exchange. The validation showed consistent observation thus justifying the

validity of data. The excerpt of annual report containing financial data is

presented in Appendix B.

3.3 Construction of size and value portfolio:

As required by the Fama -French Model the Factor portfolios based on Size and

Value was constructed using following steps Bahl (2006):

Step1: For each financial year ( July of year 't' to June of year 't+1' )

of given sample period the stocks was split into two group on the basis of

size. - Small (S) and Big(B) . Where the small stocks comprises of those

whose market capitalization lie below the median value of included stock and

Big comprise those which are above the median.

Step2: For each financial year ( July of year 't' to June of year 't+1' )

of given sample period the stocks on the basis of value will be split into

three BE/ME groups- Low(L), Medium (M), High (H) ; where L represents lower

30%, M represents middle 40% and H represents upper 30% of the value of BE/ME

for the stocks of sample.

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Step3: The six different portfolios- S/L, S/M, S/H, B/L, B/M, B/H was

constructed by intersecting the stocks in two size and three value groups as

computed from Step1 and Step3. The intersection of both size and value stocks

for the period of Aug 2007 to Jul 2012 from the stock is shown in Appendix

A . From among them three companies were randomly selected from each

intersection set to construct sample portfolio of all six types. The selected

stocks are listed in Appendix C.

Step4: Small Minus Big (SMB) factor which is meant to mimic the risk

associated with the size was obtained by subtracting the average monthly

return of three Big portfolios by average return of three small portfolios as

given by following relation:

SMB = (S/L +S/M +S/H)/3 - (B/L+B/M+B/H)/3 ................v)

Step 5: High Minus Low (HML) factor which is meant to mimic the risk

associated with the value was be obtained by subtracting the average monthly

return of two Low Portfolio from average return of two High portfolio as given

by following relation:

HML = (S/H + B/H) /2 - (S/L+B/L)/2 .......................vi)

4. DISCUSSION

4.1 Descriptive analysis

After collecting the data the analysis was done using the methodology as

stated in section 3.2 and 3.4. Number of stocks selected, the median market

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cap and the median BE/ME value for each financial year is as tabulated below

in Table 2.

Table 2

Year No. of

Companies

Selected

Median Market Cap

BE/ME30th

percentile

70th

percentile

Aug 2007- Jul

2008

67 Rs. 379348000 0.119772 0.41848

Aug 2008- Jul

2009

71 Rs. 504900000 0.148514 0.369939

Aug 2009- Jul

2010

62 Rs. 401700000 0.321645 0.56899

Aug 2010- Jul

2011

116 Rs. 314825040 0.526424 0.84915

Aug 2011- Jul

2012

116 Rs. 191867550 0.676166 1.057019

The result shows that the due to downward trend in share prices median market

cap has been falling in last three years and similarly the book to market

ratio has been rising thus indicating the undervalue of stocks in general.

After determining the median parameters the stocks in each year were

independently assorted to intersection of six different portfolio of S/L, S/M

,S/H , B/L, B/M, B/H as shown in Appendix C (Panel a- Panel e).The

intersection was done by running inner join query in MS-Excel the specimen of

which is given in Appendix I. From among them the top three from Big stocks

and bottom three from small stocks were selected to compute the average SMB

and HML parameter for each year. It should be noted that no company assorted

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into S/L category for period of 2007 Aug - 2011 Jul. The selected stocks for

each portfolio is for different fiscal year is given in Appendix D.

The preliminary descriptive analysis of all the portfolios done using SPSS

17.0 showed result tabulated in following table:

Table 3 (Average Monthly Return of various portfolio between Aug 2007 - Jul

2012)

Portfolio

s

Mean Std. Deviation Skewness Kurtosis

B/L

return

-.0069542078 .09447987445 .141 .956

B/M

return

.0562649349 .27482881838 4.534 23.253

B/H

Return

-.0188578160 .13326598611 2.526 11.382

S/L

Return

.0004668171 .02866563761 3.853 27.450

S/M

return

.0121570987 .10035026969 2.564 9.787

S/H

Return

-.0065826018 .08674339150 .810 3.250

Rm-Rf -.0104198298 .08368890765 .591 .372SMB -.0095727053 .11035779470 -2.701 12.714HML -.0073232543 .08712751846 1.441 4.200

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The above result shows that in the given period the average return of big

portfolio is greater than that of small portfolio which is contrary to the

Fama and French (1992). But this result is largely skewed by the presence of

more than 5% positive return by B/M ratio. Besides the standard error of the

B/M portfolio is even large thus it is difficult to draw any conclusion on

this regard. Meanwhile in context of value stocks in both big and small group

it appears that the average return increases from the Low to Medium and then

fall in case of High return. This observation is again in contrary to Fama and

French (1992) but is very much in line with Bahl (2006) that reports similar

phenomena in context of Indian stocks.

Table also shows that the average excess market return (Rm-Rf) is negative but

the portfolio B/M , S/L and S/M has shown positive gains which indicates that

the market is not efficient.. Further the Kurtosis value is significantly

greater than zero which indicates that the returns are not normally

distributed.

Likewise the analysis of Pearson correlation as shown in Table 3 indicates

significant negative correlation among Excess market return (Rm-Rf) and Size

factor (SMB) meanwhile there is no correlation between HML with other two

factors. This result is also very much similar to that of Bahl(2006). Further

insignificant correlation between SMB and HML shows that the Size stocks are

free of BE/ME effect and Value stock is immune from size. This finding is

reflective of similar finding by Davis, Fama and French (1999).

Table 4 Correlation between three factors

    Rm-Rf SMB HMLRm-Rf Pearson

Correlation

1 -.488** .027

Sig. (2-tailed)   .000 .838

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N 60 60 60SMB Pearson

Correlation

-.488** 1 -.093

Sig. (2-tailed) .000   .478N 60 60 60

HML Pearson

Correlation

.027 -.093 1

Sig. (2-tailed) .838 .478  N 60 60 60

**. Correlation is significant at the 0.01 level (2-tailed).

4.2 Seasonality Check

Technical analysis of US stocks especially shows the so called January effect

where the small stocks outperform broader market as indicated by tax-loss

selling hypothesis by Keim (1983). This implies a general practice by which

investors sell stock at loss in order to offset gains from other profitable

venture in order to decrease the incomet ax liability. Similarly Connor and

Shegal (2001) indicates Diwali effect that correlates to selling stock during

festivities. In this context it was pertinent to perform seasonality check in

context of Nepalese stock as well. Generally there is intuition that in month

of October during time of national festival Dashain there may be seasonality.

The table 4 shows the regression result of return in all the six portfolio

and the three factor portfolio on dummy variable that is 1 in month off

October and 0 in other month. Seasonality is indicated if the coefficient of

b is significant. The result shows that none of the portfolio shows

significant seasonality.

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Table 5 Seasonality Check with Dashain Festival ( Rt = a + b October (t)]

Portfolios A b t(a) t(b) sig(a) sig(b)B/L -.006 -.008 -.492 -.171 .625 .864B/M .056 .008 1.489 .058 .142 .954B/H -.019 .004 -1.060 .069 .293 .945S/L .001 -.003 .185 -.225 .854 .823S/M .014 -.028 1.065 -.596 .291 .554S/H -.009 .033 -.798 .820 .428 .416Rm-Rf -.012 .016 -1.037 .415 .304 .680

SMB -.010 .001 -.645 .024 .522 .981HML -.009 .021 -.769 .519 .445 .606

Table 6 Seasonality Check with July ( Rt = a + b July (t)]

a B t(a) t(b) sig(a) sig(b)B/L -.020 .157 -1.758 3.974 .084 .000B/M .066 -.116 1.775 -.899 .081 .372B/H -.016 -.031 -.901 -.489 .371 .626S/L .000 .008 -.059 .622 .953 .536S/M .012 .006 .852 .137 .398 .892

S/H -.007 .006 -.603 .157 .549 .876Rm-Rf -.020 .109 -1.843 2.978 .071 .004SMB -.010 .002 -.646 .029 .521 .977HML .000 -.092 .028 -2.336 .978 .023Similarly in Nepal the fiscal year ends in month of July when all tax filing

needs to be done. Thus the seasonality check was done by regressing the

returns of portfolio on dummy variable of July as 1. The result is tabulated

in Table 5 which shows that the seasonality thus exist among Excess Market

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Return (Rm-Rf) , B/L and HML portfolio. This indicates possible tax-loss

hypothesis being applicable in Nepalese context as well.

4.4 Fitting the Fama French Model

The study tested Fama-French Model using the standard multivariate linear

regression model as explained in Campbell, Lo and Mackinlay (1997). The linear

regression model is given by following relation

Rjt−Rft=aj+mj (Rmt−Rft )+sjSMBt+hjHMLt+∈t ............... vii)

where Rjt−Rft: excess return on jth portfolio on time 't'

Rmt−Rft: excess market return on jth portfolio on time 't'

SMBt: return on size factor portfolio on time 't'

HMLt: return on value factor portfolio

mj,sj,hj: market, size and value factor exposure

aj: intercept that indicates abnormal return on portfolio

∈t: mean zero asset specific return of portfolio 'j'

The hypothesized model thus illustrated above requires that aj term be zero

and ∈t be independently and identically distributed as a normal distribution

with zero mean and constant variance. Therefore ∈t is a purely random or white

noise process , Gujrati (2007). Further by forcefully setting the factor

exposure parameters to zero several different variant of Fama French model can

be obtained. Appendix D provides the data used in fitting the regression

model.

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4.5 Checking Multicollinearity

But before fitting the data with the model it is essential to check whether

the data suffers from the multicollinearity or not whose presence will violate

basic assumption of ordinary least square . As a result our least square

calculation will produce over estimation. In order to test muticollinearity

Variance Inflating Factor (VIF) was computed for all the independent variables

which is illustrated in table 6. Since all the VIF are below 5 it is safe to

conclude that the data doesn't suffer from multicollinearity

Table 7 Multicollinearity test

Factors Rm-Rf SMB HMLVIF 1.314 1.325 1.009

4.6 Regression Model with Rm-Rf, SMB and HML as explanatory variables

The fitted regression model as suggested by relation vii) is illustrated in

table 7.

Table 8 Excess return on portfolio Vs (Rm-Rf, SMB, HML)

Dependent Portfolio Factors Coefficient t p-valueB/L (Constant) -.010 -1.166 .249

Rm-Rf .563 4.825 .000SMB -.245 -2.759 .008HML -.348 -3.538 .001R Square: .546Adj. R square .521Durbin-Watson 2.468

B/H (Constant) -.015 -1.644 .106Rm-Rf .303 2.410 .019SMB -.364 -3.809 .000HML 1.089 10.294 .000

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R Square: .735Adj. R square .721Durbin-Watson 1.906

B/M

(Constant) 0.028842 1.240551 0.219944Rm-Rf -0.3886 -1.24255 0.21921SMB -2.07231 -8.70273 5.47E-12HML 0.08312 0.315716 0.753392R Square: 0.614Adj. R square 0.593Durbin-Watson 1.361

S/M

(Constant) .014 1.059 .294Rm-Rf .186 1.062 .293SMB .178 1.334 .188HML .294 1.990 .051R Square: .090Adj. R square .042Durbin-Watson 1.490

S/H

(Constant) -.003 -.272 .786SMB .129 1.320 .192Rm-Rf .276 2.144 .036HML .547 5.044 .000R Square: .346Adj. R square .311Durbin-Watson 2.114

S/L No Estimates

The detail of the analysis from the observation are as follows

4.6.1 Analysis of Significance of factor coefficient

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The findings shows that different portfolios demonstrate varying degree of

response according to factors. For instance stock variation in both B/L and

B/H portfolio can be best explained by all three market, size and value

factors as they are significant p-value less than 0.05. Meanwhile B/M stock

shows significant contribution in its variation by Size factor. On the other

hand S/H portfolio can be best explained only by both Value factors and market

returns. Likewise the returns of S/M portfolio cannot be explained by any

factors at all. On the other hand S/L data wasn't estimated as there wasn't

enough data. Further in none of the portfolio the constant term is significant

indicating that there is no abnormal profit in any portfolio.

4.6.2 Test of Autocorrelation

The table also shows the test of Autocorrelation using Durbin-Watson

statistics, Durbin and Watson( 1951). It shows that portfolios B/H, B/L and

S/H has DW value between two critical values of 1.5 < d < 2.5 and therefore we

can assume that there is no first order linear auto-correlation in the data.

But the portfolio S/M with d= 1.490 and

portfolio B/M with d=1.361 indicates slight negative autocorrelation. This

suggest possible misspecification of the model that can be due to omission of

certain explanatory variable or inappropriate mathematical model.

4.6.3 Test of Heteroscesdasticity

The ordinary least square assumes that the underlying data be homoscesdastic

that is the variance of error term should be constant for all the observation.

The absence of homoscesdastic condition will result in heteroscesdasticity

which similar to multicollinearity and autocorrelation will cause the ordinary

least square estimates unbiased and inefficient. The basic method for

detecting heteroscesdasticty is the scatter plot between predicted value and

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the residuals. If pattern appears then it indicates presence of

heteroscesdasticty. The five panels in Apeendix B illustrates the scatter

plot between the predicted value and the residuals for all five portfolios.

All the plots so some inherent pattern thus justifying need for testing the

hetersoscesdasticity. For this purpose Breusch-Pagan and Koenker test were

employed with null hypothesis assumption of Homosecesdasticity. The test used

in SPSS is given in Apendix C.

(H0: homoscesdasticity)

Breusch_Pagan Koenker testVal p-value val p-value

B/L 30.239 .0000 9.423 .0242B/M 40.754 .0000 10.495 0.0148B/H 61.149 .0000 22.223 0.0001S/M 14.513 0.0023 2.941 0.4S/H 1.325 0.7232 0.703 0.8725

The result shows that except for S/H portfolio all other portfolio suffer from

the significant heteroscesdasticity with p-value less than 0.05. This

indicates that the result obtained by simple regression as tabulated in table

7 may not be accurate. Therefore heteroscesdasticity adjusted regression was

performed using the methods HC0, HC1, HC2, HC3 and HC4 method by Hayes and Cai

(2007) was used that produced result as tabulated in table 8.

Table 9 Heteroscedasticity-Consistent Regression Results

B/L Portfolio R-Square Coeff SE(HC) t 0.5458

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P>|t|Constant -.0101 .0109 -.9276 .3576Rm-Rf .5635 .1996 2.8235 .0066SMB -.2453 .2867 -.8556 .3959HML -.3478 .1834 -1.8968 .0630B/H Portfolio R-Square Coeff SE(HC) t

P>|t|

0.7354

Constant -.0154 .0113 -1.3628 .1784Rm-Rf .3029 .1587 1.9093 .0613SMB -.3644 .3856 -.9451 .

3487HML 1.0890 .2285 4.7663 .0000B/M Portfolio R-Square Coeff SE(HC) t

P>|t|

0.61640

Constant .0288 .0258 1.1198 .2676Rm-Rf -.3886 .3260 -1.1921 .2382SMB -2.0723 .9165 -2.2611 .0277HML .0831 .4941 .1682 .867

0S/H Portfolio R-Square Coeff SE(HC) t

P>|t|

0.3549

Constant -.0026 .0104 -.2516 .8023Rm-Rf .2761 .1445 1.9110 .0611SMB .1295 .1874 .6907 .492

6HML .5469 .1670

3.2747 .0018S/M portfolio R-Square Coeff SE(HC) t 0.0904

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P>|t|Constant .0138 .0155 .8930 .3757Rm-Rf .1861 .1910 .9741 .3342SMB .1781 .2179 .8174 .4171HML .2937 .2077 1.4140 .1629

The analysis shows that return on B/L portfolio variation can be explained by

market return and value factor. In the mean time the size factor has been

relegated to insignificant. Likewise B/H factor also is influenced only by

market return and value parameters. Meanwhile market factors influence on S/H

portfolio was removed in the adjustment. On the other hand B/M factor remained

dependent on Size while S/M portfolio was again independent from all three

factors. But it has to be noted that upon heteroscesdasity correction the

coefficient of determination remain unchanged. Thus the finding is very much

consistent with Rana (2011) with regard to significant influence of market

and value stock in cross-sectional return of Nepalese stock.

4.6.4 Analysis of Coefficient Of Determination

Coefficient of Determination is the most important measure for testing the

goodness of fit . It computes the percentage of total variation of dependent

variable that can be explained by the regression model. The analysis shows

that depending on the portfolio the coefficient of determination varies

considerably. From table 7 it is possible to discern that the B/H portfolio

can be best explained by the three factor model as 73.54 % of its excess

return variation can be explained by the model. Meanwhile only 9.04% of S/M

portfolio can be explained by the Fama-French model. This variation in finding

necessitates that all the cross section returns be regressed using single

factor CAPM model as well as inclusion of other factors independently so as to

identify which model best fits the stock returns.

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4.7 CAPM analysis.

Regressing the excess return on portfolio against excess market return using

the following relation

Rjt−Rft=aj+mj (Rmt−Rft )

we obtained following result as shown in table 9

Table 10 Coeffieicnt of Determination in CAPM

B/L B/H B/M S/H S/M

(constant) -0.04 -0.017 0.062 -0.009

0.0

09

p-value constant .704 0.301 0.078 0.447

0.5

04

(Rm-Rf) .712 .568 .949 0.208

0.0

80

p-value Rm-Rf 0.000 .005 0.025 0.125

0.6

15

R square 0.396 0.127 0.083 0.04 0.004Adjusted R square 0.385 0.122 0.068 0.024 -0.013

The result shows that the three portfolio (B/L, B/H and B/M) are consistent

with CAPM model with significant market exposure. Meanwhile S/H and S/M model

do not show significant market exposure. Comparing the result in table 9 with

table 8 in trms of coefficient of determination it is apparent that the Fama-

French model better explain the variation in portfolio returns than CAPM

model. For instance in B/H portfolio the R2 in three factor model is 73.54 %

while that of CAPM model is simply 12.7%. So the result shows the superiority

of Fama-French model over the conventional CAPM model in explaining cross-

sectional market return.

27

4.8 Fitting Fama-French Model with Excess Returns of Individual Stock

The discussion and findings in previous section shows that excess return on

different cross-sectional portfolios in Nepalese Stock Exchange constructed by

intersecting both value and size stocks is better explained by Fama-French

Model than CAPM. So it is intuitive to test the ability of three factor model

in explaining return of individual stock. For this purpose a hetersoscesdastic

adjusted regression was carried out on excess returns of three stocks selected

randomly using both Fama-French and CAPM model whose results are tabulated

below

Table 11 Fitting Fama-French and CAPM on individual stock

Himalayan Bank Ltd Fama-French Result CAPM result Coeff SE(HC) P>|t| Coeff SE(HC) P>|t|

Constant .0043 .0126 .7322

RmRf 1.3401 .2440 .0000

SMB .0181 .1745 .9176

HML .1806 .1386 .1979

0.0028 0.0122 0.8202

1.335 0.1663 0.000

R-sq 0.6380

P-value .0000

0.628

0.000Ace Development Bank Coeff SE(HC) P>|t| Coeff SE(HC) P>|t|

Constant .0065 .0200 .7461

RmRf 1.2045 .2995 .0002

SMB .1939 .3839 .6158

HML .7737 .3487 .0310

-.0023 .0207 .9137

1.1733 .30 .0004

28

R-sq 0.5245

P-value .0.0001

0.3531

0.0004

Chilime Hydropower Coeff SE(HC) P>|t| Coeff SE(HC) P>|t|Constant .0082 .0111 .4631

RmRf .9518 .1484 .0000

SMB -.0803 .1676 .6339

HML .0959 .1752 .5865

.0085 .0104 .4169

1.0054 .1314 .0000

R-sq 0.5790

P-value .0.0000

0.5681

0.0000

The result thus obtained shows that the excess return of all three stock

selected randomly from period between 2007 August to 2012 July has significant

market return factor whereas effect of size and value factor are negligible.

But despite this three factor model better explained the return variance than

the CAPM due to its superior R square value.

5. Limitation

The study since being done for partial fulfillment of MBA requirement may not

have been exhaustive enough for capturing the details of the Nepalese stock

exchange. Besides , since Nepalese stock exchange is characterized by

inefficiency and unavailability of data the research has not been able to

analyze the data at same depth as it has been carried out in US or Indian

Stock Market. For instance according to law and Right To Information Act 2007

it is required that all the publicly traded companies should timely release

the annual report to public domain. However it was discovered that there was

29

truancy in abiding the law strictly. Hence the research dependent only on data

from those companies who have publicly made their data available. Further the

risk free rate pertaining to return on Treasury Bill was also made available

only from 2007 onwards by Nepal Rastra Bank. Hence the study pool was

restricted for period of 60 months from (2007 Aug - 2012 Jul). Meanwhile the

methodology employed used capital gains as the sole returns and didn't account

for dividend and capital gain taxes, thus analysis thus may not be able to

represent the real picture of market while including those left out

parameters. Besides the time limitation imposed for study also impose

restricted opportunity for carrying out research freely. Further because of

lack of similar study in Nepalese context there is no precedence to benchmark

the findings.

6. Conclusion

The study has checked the relevance of Fama-French three factor model in

context of Nepalese stock exchange. Further study also compared and contrasted

three factor model and CAPM in their ability to explain the return of cross

sectional portfolio as well as individual stock. It can be concluded from the

results based on coefficient of determination as discussed in section 4.6 7

shows the superiority of Fama French model over CAPM in explaining the

variation in the return of portfolios. In all five portfolios (B/L, B/M, B/H,

S/M, S/L) three factor model has better explanatory power. However the result

also indicated that Excess market Return and Value factor are apparently more

significant than Size factor in model fitting.

The analysis also showed that the intercept term in the model was

insignificant in all portfolios. Hence it can be concluded that none of the

portfolio yields abnormal excess return over the market.

30

Further as discussed in section 4.7, the randomly selected individual stock

of companies however is significantly dependent only on excess Market returns.

Thus it can be concluded that in case of individual stock unlike cross-

sectional portfolio CAPM model is more suitable due to its low computational

complexity.

Besides the study also checked for the Multicollinearity, Heteroscesdasticity

and Autocorrelation for possible violation of ordinary least square

assumption. The data showed significant heteroscesdasticity which was

corrected to determine the three-factor model.

Also the study tested for the seasonality in Nepalese stock return using the

dummy regression model. The result showed that there is significant

seasonality in month of July thus indicating possibility of tax loss effect in

nepalese stock market. Study also showed that festive season of November there

is no seasonality effect.

Further the study has thoroughly enumerated the steps required to carry out

the research and thus provides the methodological guidelines for similar

studies to be undertaken in future.

Thus it can be concluded that the research was able to fulfill all the

objectives stated in the outset of the study.

7. Recommendation

31

The study is a prelude to further similar asset pricing portfolio model

fitting in context of Nepalese Market. As shown by the results portfolio

constructed using intersecting Small size and Middle value (S/M) couldn't be

explained by both CAPM and three factor model. This situation thus opens

possibility for factor analysis to identify possible underlying parameters

that can better explain the variance in return than established model.

Also the result showed that coefficient of determination for all the portfolios was not very high above (80%) using the Fama-French model. This indicates the possibility of other underlying factors such as momentum that may influence cross sectional portfolio return as suggested by Carhart (1997) .

Further since the study only included the portfolio constructed using common

equity stock there is a possibility of future research of fitting three factor

model in mixed portfolio that includes fixed income assets such as bond,

debenture and preferential shares alongside common stock.

8. REFERENCES

Ajili, S. (2005). The Capital Asset Pricing Model and the Three Factor Model

of Fama and French Revisited in the Case of France, Working Paper.

Aksu, M. H. & Onder, T (2003). The Size and Book to-Market Effects and their

Role as Risk Proxies in the Istanbul Stock Exchange, Koc University,

Graduate School of Business, Working Paper 2000-2004.

Bahl, B (2006). Testing the Fama and French Three Factor Model and Its

Variants for the Indian Stock Returns, working paper.

Banz, R. W., (1981). The Relationship between Return and Market Value of

Common Stock, Journal of Financial Economics, 9, pp.3-18.

Cambell, J. Y., Lo, A. W., Mackinlay, A. C (1997). The Econometrics of

Financial Markets, Princeton University Press, Princeton N.J , U.S.A

32

Carhart, Mark M. (1997). On Persistence in Mutual Fund Performance. Journal of

Finance 52 (1): 57–82

Chhatkuli, K. (2010). An Examination of Relationship between Risk and Return

in the Nepalese Stock Market, Journal of Meijo ,3, pp. 149-170

Clarke, R. J. (2005). Research Methodologies, HDR Seminar Series. Faculty of

Commerce, University of Wollongong.

Connor, G & Sehgal, S(2001). Tests of the Fama and French Model in India,

working paper, Decision, 30.2, pp.I-19.

Davis, J (1994). The cross-section of realized stock returns: The pre-

COMPUSTAT evidence, Journal of Finance 49, pp. 1579-1593.

Durbin, J. & Watson , G.S. (1951). Testing for serial Correlation in Least-

Squares Regression, Biometrika, 38, pp 159-171

Fama, E., F. & French, K. R. (1992). The Cross Section of Expected Stock

Returns, The Journal of Finance, 47, pp.427-465.

Fama, E. F. & French, K. R.(1993). Common Risk Factors in the Return on Stocks

and Bonds, Journal of Financial Economics, 33, pp.3-56.

Fama, E. F. & French, Kenneth, R. (1995). Size and Book to Market Factors in

Earnings and Returns, The Journal of Finance, 50, pp.131-156.

Gaunt, C. (2004). Size and Book to Market Effects and The Fama French Three

Factor Asset Pricing Model: Evidence from the Australian stock market,

Accounting and Finance 44, pp. 27-44.

Gujarati, D. N.(2007). Basic Econometrics, Tata McGraw-Hill, pp. 817

Gurung, J. B. (2004). Growth and Performance of Securities Market in Nepal.

The Journal of Nepalese Business Studies, pp. 85-92.

33

Hayes, A. F., & Cai, L. (2007).  Using heteroscedasticity-consistent standard

error estimators in OLS regression: An introduction and software

implementation.  Behavior Research Methods, 39, 709-722

Kiem, D. B. (1983). Size-Related Anomalies and Stock Return Seasonality:

Further Empirical Evidence, Journal of Financial Economics 12.

Lintner, J. (1965). The Valuation of Risk Assets and The Selection of Risky

Investments in Stock Portfolios and Capital Budgets, Review of Economics and

Statistics 47, 13-37.

Mossin, J. (1968). Optimal Multiperiod Portfolio Policies, The Journal of

Business,41, pp. 215-229

Rana, S. B. (2011). Stock Beta, Firm Size and Book-To-Market Effects On Cross-

Section of Common Stock Returns In Nepal. The Lumbini Journal of Business and

Economics 1.

Rosenberg, B., Kenneth R. & Ronald L. (1985). Persuasive Evidence of Market

Inefficiency, Journal of Portfolio Management 11, pp. 9-17.

Sharpe, W. F. (1964). Capital Asset Prices: A theory of Market Equilibrium

under Conditions of Risk, Journal of Finance 19, pp. 425-442.

Taneja, Y. P. (2010). Revisiting Fama French Three Factor Model in Indian

Stock Market, The Journal of Business Perspective, 14, pp. 267 - 274.

Walliman, N (2001). Your Research Project: A step by step guide for first time

researcher. Sage Publication

34

APPENDIX A : Companies selected for different fiscal year with their Market

cap and BE/ME value

Panel A (Companies selected in 2007-2008 with their BE/ME ratio and Market

Cap)

S.No

Company Be/ME Value marketcap

Siz

e

1Nabil Bank Ltd.

0.07931753

6 Low 36259980750 big

2Nepal Investment Bank

Ltd.

0.09566122

4 Low 29495927300 big

3Standard Chartered

Bank Ltd.

0.07498096

6 Low 46497547200 big

4Himalayan Bank Ltd. 0.13370202

Middl

e 24081057000 big

5Nepal SBI Bank Limited

0.11884844

5 Low 13198269201 big

6Everest Bank Ltd

0.09347062

6 Low 11838960000 big

7Bank of Kathmandu

0.06928085

1 Low 14173820550 big

8Nepal Industrial &

Co.Bank

0.10832554

5 Low 10169280000 big

9Machhachapuchhre Bank

Ltd

0.09623715

4 Low 10393888945 big

10Laxmi Bank Limited

0.10643306

4 Low 8147160000 big

11Kumari Bank Ltd

0.13606965

2

Middl

e 9045000000 big

12Siddhartha Bank

Limited

0.11483506

9 Low 9538560000 big

35

13Uniliver Nepal Ltd.

0.06219756

1 Low 3774870000 big

14Chilime Hydro power

Co.

0.17069142

1

Middl

e 11396352000 big

15Nepal Insurance

Co.Ltd.

0.53928571

4 High 359444400

sma

ll

16Himalayan Gen.Insu.

Co.Ltd.

0.57971014

5 High 217350000

sma

ll

17United Insurance Co.

(Nepal)Ltd.

0.65079365

1 High 189000000

sma

ll

18Everest Insurance Co.

Ltd.

0.50144329

9 High 261900000

sma

ll

19Premier Insurance co.

Ltd.

0.72033333

3 High 90000000

sma

ll

20Neco Insurance Co.

1.44155038

8 High 70950000

sma

ll

21Alliance Insurance Co.

Ltd.

0.95402597

4 High 77000000

sma

ll

22Sagarmatha Insurance

Co.Ltd

0.79411764

7 High 171666000

sma

ll

23Nepal Life Insurance

Co. Ltd.

0.07350509

3 Low 4172500000 big

24Life Insurance Co.

Nepal

0.11289525

7 Low 2530000000 big

25Nepal Finance and

Saving Co.Ltd.

0.52393684

2 High 142500000

sma

ll

26NIDC Capital Markets

Ltd.

0.13819089

9

Middl

e 912262500 big

27National Finance Co.

Ltd.

0.19037142

9

Middl

e 1647258900 big28 Nepal Share Markets 0.08003592 Low 7214400000 big

36

Ltd. 8

29Annapurna Finance

Co.Ltd.

0.11004081

6 Low 2963520000 big

30Kathmandu Finance

Limited.

0.66315789

5 High 108157500

sma

ll

31Peoples Finance

Limited.

0.18597997

1

Middl

e 587160000 big

32Citizen Investment

Trust

0.51655251

1 High 262800000

sma

ll

33Nepal Aawas Bikas

Beeta Co. Ltd.

0.22997138

8

Middl

e 493619820 big

34Narayani Finance

Limited

0.13329749

1

Middl

e 744360840 big

35Ace Development Bank

Ltd.

0.13102803

7

Middl

e 2739200000 big

36Gorkha Finance Ltd. 0.6514 High 60000000

sma

ll

37Universal Finance Ltd.

0.59190812

7 High 170418072

sma

ll

38Maha Laxmi Finance

Ltd.

0.12016792

6

Middl

e 952800000 big

39Lalitpur Finance Ltd.

0.30062893

1

Middl

e 603703125 big

40Goodwill Finance Co.

Ltd.

0.19424960

5

Middl

e 316500000

sma

ll

41Paschimanchal Finance

Co. Ltd

0.54767918

1 High 148258000

sma

ll

42Lumbini Finance Ltd.

0.43368421

1 High 171000000

sma

ll

43Siddhartha Finance

Limited 0.11615041 Low 698360000 big

37

44Alpic Everest Finance

Co. Ltd.

0.20695804

2

Middl

e 446160000 big

45United Finance Ltd

0.15914438

5

Middl

e 701250000 big

46International Leasing

& Fin. Co. 0.24047541

Middl

e 878400000 big

47Shree Investment

Finance Co. Ltd

0.28945422

5

Middl

e 381696000 big

48Central Finance Co.

Ltd.

0.20033783

8

Middl

e 577200000 big

49Premier Finance Co.

Ltd

0.31830699

8

Middl

e 146721600

sma

ll

50Nava Durga Finance

Co.Ltd.

0.38924836

6

Middl

e 121025142

sma

ll

51Butwal Finance Ltd

0.12679263

6

Middl

e 719016072 big

52Standard Finance Ltd.

0.15695698

9

Middl

e 675180000 big

53Cosmic Mer.Bank & Fin.

0.63423076

9 High 136592820

sma

ll

54KIST Merchant Bank. &

Fin

0.10974949

9 Low 1996000000 big

55World Merchant Bank

Ltd

0.30303030

3

Middl

e 594000000 big

56Birgunj Finance Ltd

0.08840909

1 Low 958320000 big

57Capital Mer. Bamk &

Fin

0.09606299

2 Low 4089400000 big

58Everest Finance Ltd, 0.6294 High 40000000

sma

ll59 Prudential Bittiya 0.41196363 Middl 137500000 sma

38

Sans 6 e ll

60Royal Mer. Bank.& Fin

0.24112149

5

Middl

e 323204735

sma

ll

61Guheyshwori Mer. Bank.

Fin

0.13952601

2

Middl

e 533543245 big

62Bhajuratna Fin.& Sav.

Co. Ltd.

1.15049180

3 High 42700000

sma

ll

63Development Credit

Bank Ltd.

0.15119298

2

Middl

e 9468748800 big

64Nirdhan Utthan Bank

Ltd.

2.42455223

9 High 105956614

sma

ll

65Chhimek Vikash Bank

Ltd.

0.72803773

6 High 82150000

sma

ll

66Siddhartha Vikash Bank

Ltd

0.07632786

9 Low 1640480625 big

67Sanima Vikash Bank

Ltd. 0.076 Low 4576000000 big

Panel B (Companies selected in 2008-2009 with their BE/ME ratio and Market

Cap)

S.No Company BE/ME Value MarketCap Size

1 Nabil Bank Ltd.

0.04295774

6 Low 47311945530 Big

2 Nepal Investment Bank Ltd.

0.09923631

1 Low 33410116332 Big3 Standard Chartered Bank 0.01826622 Low 56011180640 Big

39

Ltd. 3

4 Himalayan Bank Ltd.

0.06048863

6 Low 21405384000 Big

5 Nepal SBI Bank Limited

0.09331578

9 Low 16596102900 Big

6 Everest Bank Ltd

0.06529124

2 Low 15683031000 Big

7 Bank of Kathmandu

0.09175428

6 Low 14776963250 Big

8 Nepal Industrial & Co.Bank

0.12523090

6 Low 12841804800 Big

9 Machhachapuchhre Bank Ltd

0.25235173

8

Middl

e 6428599380 Big

10 Laxmi Bank Limited

0.15774952

9

Middl

e 11661674382 Big

11 Kumari Bank Ltd

0.23791428

6

Middl

e 7547904000 Big12 Siddhartha Bank Limited 0.1228 Low 8280000000 Big

13 NMB Bank Ltd.

0.22645290

6

Middl

e 5489000000 Big

14 Uniliver Nepal Ltd.

0.02870588

2 Low 3912975000 Big

15 Chilime Hydro power Co.

0.08513117

3 Low 9455616000 Big

16 National LifeInsu. Co.Ltd.

0.21158862

9

Middl

e 789360000 Big

17 Himalayan Gen.Insu. Co.Ltd.

0.84526315

8 High 287280000

Smal

l

18

United Insurance Co.

(Nepal)Ltd.

0.38435374

1 High 176400000

Smal

l

19 Everest Insurance Co. Ltd.

0.86442105

3 High 288562500

Smal

l

40

20 Premier Insurance co. Ltd.

0.72273684

2 High 193800000

Smal

l

21 Sagarmatha Insurance Co.Ltd

0.46492063

5 High 141372000

Smal

l

22

Nepal Life Insurance Co.

Ltd.

0.10560617

8 Low 3885000000 Big

23 Life Insurance Co. Nepal

0.21477941

2

Middl

e 1700000000 Big

24 Lumbini General Insurance

0.76331521

7 High 230000000

Smal

l

25

Nepal Finance and Saving

Co.Ltd. 0.41785 High 120000000

Smal

l

26 NIDC Capital Markets Ltd.

0.13870833

3 Low 1215000000 Big

27 National Finance Co. Ltd.

0.13468571

4 Low 1647258900 Big

28 Nepal Share Markets Ltd.

0.36170212

8

Middl

e 1218240000 Big

29 Annapurna Finance Co.Ltd.

0.25561855

7

Middl

e 2542176000 Big

30 Kathmandu Finance Limited.

0.43457055

2 High 247434000

Smal

l

31 Peoples Finance Limited.

0.38203508

8 High 575995830 Big

32 Citizen Investment Trust

0.34932876

7

Middl

e 584000000 Big

33

Nepal Aawas Bikas Beeta Co.

Ltd.

0.64710204

1 High 338637775

Smal

l

34 Narayani Finance Limited

0.13904121

9 Low 2384352972 Big

35 Gorkha Finance Ltd.

0.39084415

6 High 183414000

Smal

l

41

36 Universal Finance Ltd. 0.30880597

Middl

e 201731640

Smal

l

37 Maha Laxmi Finance Ltd.

0.14851385

4 Low 1143360000 Big

38 Goodwill Finance Co. Ltd. 0.199296

Middl

e 721713125 Big

39

Paschimanchal Finance Co.

Ltd

0.47525597

3 High 148258000

Smal

l

40 Lumbini Finance Ltd.

0.30024955

4

Middl

e 504900000

Smal

l

41 Siddhartha Finance Limited

0.08323157

1 Low 1167499446 Big

42

Alpic Everest Finance Co.

Ltd.

0.46397089

4 High 375180000

Smal

l

43 United Finance Ltd

0.14654791

2 Low 1221000000 Big

44

International Leasing &

Fin. Co.

0.28985245

9

Middl

e 3952800000 Big

45

Shree Investment Finance

Co. Ltd

0.15634760

7

Middl

e 800352000 Big

46 Central Finance Co. Ltd.

0.26172413

8

Middl

e 500638049

Smal

l

47 Premier Finance Co. Ltd

0.31072886

3

Middl

e 162993600

Smal

l

48 Nava Durga Finance Co.Ltd.

0.29362035

2

Middl

e 232989428

Smal

l

49 Butwal Finance Ltd

0.12155038

8 Low 852772560 Big

50 Standard Finance Ltd.

0.36185294

1

Middl

e 493680000

Smal

l

51 World Merchant Bank Ltd

0.28148780

5

Middl

e 295200000

Smal

l

42

52 Birgunj Finance Ltd

0.10461363

6 Low 1125937560 Big

53 Capital Mer. Bamk & Fin

0.18823529

4

Middl

e 2543389040 Big

54 Everest Finance Ltd,

0.64093959

7 High 59600000

Smal

l

55 Prudential Bittiya Sans 0.319

Middl

e 440000000

Smal

l

56 Royal Mer. Bank.& Fin

0.22127272

7

Middl

e 738399200 Big

57 IME Financial Institution

0.12467518

6 Low 2342431278 Big

58

Bhajuratna Fin.& Sav. Co.

Ltd.

1.54053333

3 High 57750000

Smal

l

59

Civil Merchant bittya

sanstha

0.83213367

6 High 194500000

Smal

l

60 ICFC Bittya Sanstha Ltd.

0.24732727

3

Middl

e 1811610350 Big

61 Chhimek Vikash Bank Ltd.

0.36993902

4

Middl

e 101680000

Smal

l

62 Business Dev. Bank Ltd.

0.21783076

9

Middl

e 1365000000 Big

63 Siddhartha Vikash Bank Ltd

0.56557312

3 High 1631850000 Big

64 Sanima Vikash Bank Ltd.

0.16927203

1

Middl

e 3006720000 Big

65 Sahayogi Vikas Bank

0.72653846

2 High 74880000

Smal

l

66 Gurkha Development Bank

0.15697350

1

Middl

e 3441600000 Big

67 Swabalamwan Bikash Bank

0.66265060

2 High 366382667

Smal

l

43

68 Ace Development Bank Ltd.

0.60204081

6 High 2690354016 Big

69 Himchuli Bikash Bank Ltd.

0.09065315

3 Low 1598400000 Big

70 Excel Development Bank

0.68103333

3 High 60000000

Smal

l

71 BiratLaxmi Development Bank

0.30314713

9

Middl

e 367000000

Smal

l

Panel C (Companies selected in 2009-2010 with their BE/ME ratio and Market

Cap)

S.No Company be/me

Valu

e marketcap Size

1 Nabil Bank Ltd.

0.1359060

4 Low

2302340848

0 big

2

Nepal Investment Bank

Ltd.

0.2297872

34 Low

1696983574

5 big

3

Standard Chartered Bank

Ltd.

0.0998871

61 Low

4585627724

4 big

4 Himalayan Bank Ltd.

0.3143627

45 Low 9924314400 big

5 Nepal SBI Bank Limited

0.2627260

46 Low

1229771232

3 big

6 Everest Bank Ltd

0.1611717

79 Low

1041276600

0 big

7 Bank of Kathmandu

0.2455357

14 Low 9930119640 big

8

Machhachapuchhre Bank

Ltd

0.4075531

91

Midd

le 3707290440 big

9 Laxmi Bank Limited

0.2144561

4 Low 6572045280 big

44

10 Kumari Bank Ltd

0.2927350

43 Low 5549719032 big

11 Siddhartha Bank Limited

0.2805405

41 Low 6975817200 big

12 Siddhartha Bank Limited

0.2312837

84 Low 6975817200 big

13 NMB Bank Ltd.

0.3788135

59

Midd

le 4218499705 big

14 DCBL Bank Ltd.

0.4343846

15

Midd

le 4319078400 big

15 KIST Bank Limited

0.5138693

47

Midd

le 3980000000 big

16 Uniliver Nepal Ltd.

0.3386358

16

Midd

le 3819984300 big

17 Chilime Hydro power Co.

0.3448319

71

Midd

le 8032896000 big

18

National LifeInsu.

Co.Ltd.

0.2555761

32 Low 641520000 big

19

Himalayan Gen.Insu.

Co.Ltd.

0.5698717

95 High 235872000 small

20

Everest Insurance Co.

Ltd.

0.5080191

69

Midd

le 316912500 small

21

Premier Insurance co.

Ltd.

0.8882608

7 High 164220000 small

22

Sagarmatha Insurance

Co.Ltd

0.5207395

5

Midd

le 349290942 small

23

Nepal Life Insurance Co.

Ltd.

0.1240705

88 Low 2550000000 big

24 Life Insurance Co. Nepal

0.2002931

03 Low 1450000000 big

25

Lumbini General

Insurance

0.6173333

33 High 206250000 small

45

26

Nepal Finance and Saving

Co.Ltd.

0.6664406

78 High 115050000 small

27

NIDC Capital Markets

Ltd.

0.3883720

93

Midd

le 734713910 big

28 Nepal Share Markets Ltd.

0.9216666

67 High 751680000 big

29

Kathmandu Finance

Limited.

0.9683832

34 High 152103600 small

30 Peoples Finance Limited.

0.5164978

9

Midd

le 700761600 big

31 Citizen Investment Trust

0.4830188

68

Midd

le 530000000 big

32

Nepal Aawas Bikas Beeta

Co. Ltd.

0.5315891

47

Midd

le 432420642 big

33 Gorkha Finance Ltd.

1.2868965

52 High 200512670 small

34 Universal Finance Ltd.

0.7141269

84 High 273136374 small

35 Maha Laxmi Finance Ltd. 0.481875

Midd

le 1209600000 big

36

Goodwill Finance Co.

Ltd.

0.5669333

33

Midd

le 259816725 small

37

Paschimanchal Finance

Co. Ltd

0.8926943

01 High 126955400 small38 Lumbini Finance Ltd. 0.95484 High 281250000 small

39

Siddhartha Finance

Limited

0.2491142

86 Low 608525400 big

40 United Finance Ltd

0.4696258

5

Midd

le 1030837500 big

41

Shree Investment Finance

Co. Ltd

0.3636324

79

Midd

le 707616000 big42 Central Finance Co. Ltd. 0.4175155 Midd 470046262 big

46

28 le

43 Premier Finance Co. Ltd

0.4192542

37

Midd

le 290199760 small

44

Nava Durga Finance

Co.Ltd.

0.6787179

49 High 250062930 small

45 Butwal Finance Ltd

0.2015512

82 Low 644537400 big

46 Standard Finance Ltd.

0.7198876

4 High 1783211298 big

47 World Merchant Bank Ltd

0.9292941

18 High 309264000 small

48 Birgunj Finance Ltd

1.2163978

49 High 781200000 big

49 Prudential Bittiya Sans

0.7797222

22 High 144000000 small

50

IME Financial

Institution

0.2233628

32 Low 1409450130 big

51

Civil Merchant bittya

sanstha

0.6598952

88 High 190860570 small

52 ICFC Bittya Sanstha Ltd.

0.3982720

59

Midd

le 895923664 big

53

Nepal Industrial Dev.

Corp.

1.2130833

33 High 357454080 small

54 Chhimek Vikash Bank Ltd.

0.3564693

88

Midd

le 151900000 small

55 Sanima Vikash Bank Ltd.

0.2281891

35 Low 3816960000 big

56 Sahayogi Vikas Bank

0.5299233

72

Midd

le 156600000 small

57 Gurkha Development Bank

0.2869975

19 Low 2127840000 big58 Swabalamwan Bikash Bank 0.9518987 High 273998860 small

47

34

59

Himchuli Bikash Bank

Ltd.

0.4158095

24

Midd

le 1381174200 big

60 Excel Development Bank

1.2404838

71 High 248000000 small

61

BiratLaxmi Development

Bank

0.4903975

54

Midd

le 179850000 small

62 Subbecha Bikas Bank Ltd.

0.5504528

3

Midd

le 267120000 small

Panel D (Companies selected in 2010-2011 with their BE/ME ratio and Market

Cap)

S.No company BE/ME Value marketcap Size

1 Ace Development Bank Ltd.

0.78014184

4 Medium 1058151279 Big

2 Alliance Insurance Co. Ltd.

1.18406779

7 high 81401238

Smal

l

3 Alpic Everest Finance Co. Ltd.

0.68518072

3 Medium 178682400

Smal

l

4 Annapurna Bikas Bank

0.97415094

3 high 712320000 Big

5 Annapurna Finance Co.Ltd.

0.51691244

2 low 568713600 Big6 Api Finance Limtied 1.13873417 high 189600000 Smal

48

7 l

7

Arun Valley Hydropower Dev Co

Ltd

0.25075342

5 low 1156801800 Big8 Asian Life Insurance Company 0.60017341 Medium 622800000 Big

9 Bageshowori Dev.Bank

0.50411392

4 low 156420000

Smal

l

10 Bank of Asia Nepal Limited

0.55036458

3 Medium 3840000000 Big

11 Bank of Kathmandu

0.30771929

8 low 7749039990 Big

12 Birat Laxmi Devt Bank

0.95884353

7 high 243459636

Smal

l

13 Birgunj Finance Ltd

0.60752873

6 Medium 730800000 Big

14 Business Development Bank Ltd

0.73716312

1 Medium 972984600 Big

15 Butwal Finance Ltd

1.07867256

6 high 253731443

Smal

l

16 Capital Mer. Bamk & Fin

0.74742857

1 Medium 1309097300 Big

17 Central Finance Co. Ltd.

0.42602076

1 low 485154993 Big

18 Chhimek Vikash Bank Ltd.

0.51252873

6 low 299497500

Smal

l

19 Chilime Hydro power Co.

0.49678832

1 low 5997312000 Big

20 Citizen Bank International Ltd

0.49608108

1 low 4440000000 Big

21 Citizen Investment Trust

0.65844444

4 Medium 648000000 Big22 Civil Merchant Bittiya Sanstha 0.701875 Medium 207848160 Smal

49

Ltd l

23

Clean Energy Devlopment Bank

Ltd

0.61916167

7 Medium 1816960000 Big

24 CRYSTAL FINANCE Ltd. 1.10965812 high 81900000

Smal

l

25 DCBL Bank Ltd.

0.76091503

3 Medium 2795772672 Big

26 Everest Bank Ltd

0.30346435

1 low 9085312262 Big

27 Everest Insurance Co. Ltd. 0.51316129 low 313875000

Smal

l

28 Excel Development Bank Ltd.

0.53389285

7 Medium 224000000

Smal

l

29 Fewa Finance Co. Ltd.

0.45644194

8 low 728910000 Big

30 Gandaki Bikash Bank Ltd

0.71578947

4 Medium 342000000 Big

31 Global Bank Ltd

0.49397129

2 low 3135000000 Big

32 Goodwill Finance Co. Ltd.

0.60187845

3 Medium 543000000 Big

33 Gorkha Finance Ltd.

1.43841121

5 high 166608095

Smal

l

34 Gurkha Development Bank

0.46644736

8 low 1203840000 Big

35 Himalayan Bank Ltd.

0.39441739

1 low

1150000000

0 Big

36 Himalayan Gen.Insu. Co.Ltd. 0.84985 high 201600000

Smal

l

37 ICFC Bittiya Sanstha Ltd

0.68078787

9 Medium 543483105 Big38 IME Financial Institution 0.48146341 low 1180374912 Big

50

5

39

Imperial Financial Institution

Ltd

0.97323943

7 high 89815000

Smal

l

40

Infrastructure Development

Bank Ltd

1.05906542

1 high 856000000 Big

41

International Leasing & Fin.

Co.

0.49658536

6 low 1328400000 Big

42 Kaski Finance Limited

0.67795180

7 Medium 348600000 Big

43 Kasthamandap Dev Bank Ltd

1.14097826

1 high 588800000 Big

44 Kathmandu Finance Limited.

0.89225352

1 high 198306692

Smal

l

45 KIST Bank Ltd

0.67393548

4 Medium 3100000000 Big

46

Kuber Merchant Bittiya Sanstha

Ltd

0.84634920

6 Medium 189000000

Smal

l

47 Kumari Bank Ltd

0.51402255

6 low 3627476580 Big

48 Lalitpur Finance Ltd.

0.57447457

6 Medium 554215615 Big49 Laxmi Bank Limited 0.35 low 3920167360 Big

50 Lord Buddha Finance Limited

0.86301369

9 high 243090000

Smal

l

51 Lumbini Bank Ltd.

0.50877828

1 low 2210000000 Big

52 Lumbini Finance Ltd.

1.42602941

2 high 198900000

Smal

l

53 Lumbini General Insurance 0.96671875 high 160000000

Smal

l54 Machhachapuchhre Bank Ltd 0.81947368 Medium 2164171478 Big

51

4

55 Maha Laxmi Finance Ltd.

0.61622222

2 Medium 756000000 Big

56 Malika Devt Bank Ltd

0.75692810

5 Medium 76500000

Smal

l

57 Miteri Development Bank Ltd. 0.905625 high 115568640

Smal

l

58 Nabil Bank Ltd.

0.21166134

2 low

2540024547

2 Big

59

National Life Insurance

Company Ltd 0.34757485 low 440880000 Big

60

Narayani National Finance Co.

Ltd.

0.88193333

3 high 979145400 Big

61 Nava Durga Finance Co.Ltd. 0.8318 Medium 189856050

Smal

l

62 Neco Insurance Co.

1.29464285

7 high 129360000

Smal

l

63

Nepal Aawas Bikas Beeta Co.

Ltd.

0.57046296

3 Medium 362078424 Big

64 Nepal Bangladesh Bank Ltd.

0.88223076

9 high 2418505700 Big

65 Nepal Credit & Com. Bank

0.64802395

2 Medium 2322786300 Big

66 NDEP Development Bank Ltd.

0.97327586

2 high 633516600 Big

67

Nepal Doorsanchar Company

Limited

0.75019093

1 Medium

6285000000

0 Big

68 Nepal Express Finance Ltd

0.66911917

1 Medium 253910800

Smal

l

69

Nepal Finance and Saving

Co.Ltd.

0.96352941

2 high 208397475

Smal

l70 Nepal Industrial & Co.Bank 0.25878846 low 5930496000 Big

52

2

71 Nepal Insurance Co.Ltd.

0.58904624

3 Medium 355336464 Big

72 Nepal Investment Bank Ltd.

0.36959223

3 low

1239640483

5 Big

73 Nepal Life Insurance Co. Ltd.

0.32056537

1 low 1698000000 Big

74 Nepal SBI Bank Limited

0.26125663

7 low

1054889804

0 Big

75 Nepal Share Markets Ltd.

0.59850574

7 Medium 751680000 Big

76 Nerude Laghubitta Bikash Bank

2.63736842

1 high 3895000

Smal

l

77 NIDC Capital Markets Ltd.

0.83369369

4 Medium 493112616 Big

78 Nilgiri Vikas Bank Ltd 0.92380531 high 56500000

Smal

l

79 Nirdhan Utthan Bank Ltd.

0.81974468

1 Medium 306602150

Smal

l

80 NMB Bank Ltd

0.61871794

9 Medium 3900000000 Big

81 Paschimanchal Finance Co. Ltd

0.50949019

6 low 201286800

Smal

l82 Pashupati Dev Bank Limited 1.00875 high 728000000 Big

83 Peoples Finance Limited.

0.51895454

5 low 650496000 Big

84 Pokhara Finance Ltd.

0.68553488

4 Medium 667244760 Big

85 Prabhu Finance Company Ltd

0.79242105

3 Medium 775200000 Big86 Premier Finance Co. Ltd 0.60270270 Medium 267876744 Smal

53

3 l

87 Premier Insurance co. Ltd.

0.96789808

9 high 160140000

Smal

l

88 Prime Commercial Bank Limited

0.56288288

3 Medium 4708620000 Big

89

Prime Life Insurance Company

Ltd

0.73807017

5 Medium 615600000 Big

90 Prudential Finance Company Ltd

0.77449275

4 Medium 138000000

Smal

l

91 Purwanchal Grameen Bikash Bank

0.13667357

5 low 579000000 Big

92 Reliable Finance Ltd

0.89147887

3 high 281160000

Smal

l

93 Resunga Bikas Bank Ltd.

0.89349315

1 high 89352000

Smal

l

94 Royal Mer. Bank.& Fin

0.75476190

5 Medium 503653584 Big95 Sagarmatha Insurance Co.Ltd 0.7034 Medium 387477000 Big

96

Sagarmatha Mer Banking &

Finance Ltd

0.87007246

4 high 362250000 Big

97 Sanima Vikash Bank Ltd.

0.21931589

5 low 3816960000 Big

98 Sewa Bikas Bank Ltd

1.14112149

5 high 123050000

Smal

l

99 Shikhar Bittiya Sanstha Ltd

1.34354545

5 high 110000000

Smal

l

100 Shikhar Insurance Co. Ltd.

0.47503184

7 low 392500000 Big

101

Shree Investment Finance Co.

Ltd

0.34609947

6 low 442814400 Big102 Siddhartha Bank Limited 0.45322222 low 4242051000 Big

54

2

103

Siddhartha Development Bank

Limited 0.87605042 high 767550000 Big

104 Siddhartha Finance Limited

0.77313725

5 Medium 266526000

Smal

l

105 Siddhartha Insurance Limited

1.10161904

8 high 105000000

Smal

l

106 Soaltee Hotel Ltd.

0.18113861

4 low 3014686582 Big

107 Standard Chartered Bank Ltd.

0.13386111

1 low

2894860980

0 Big

108 Standard Finance Ltd.

0.77095588

2 Medium 1362453576 Big

109 Subhechha Bikas Bank Limited

0.89337016

6 high 209815200

Smal

l

110 Swabalamwan Bikash Bank 1.4425 high 188677760

Smal

l

111 Triveni Bikas Bank Limited

0.86310126

6 high 207349246

Smal

l

112 Uniliver Nepal Ltd.

0.18864045

2 low 4401866700 Big

113 Union Finance Co. Ltd.

0.65261111

1 Medium 314825040

Smal

l

114 United Finance Ltd

0.55427135

7 Medium 697743750 Big

115

United Insurance Co.

(Nepal)Ltd. 0.588 Medium 177000000

Smal

l

116 Universal Finance Ltd.

0.75701219

5 Medium 248857536

Smal

l

55

Panel E (Companies selected in 2011-2012 with their BE/ME ratio and Market

Cap)

S.No company BE/ME Value marketcap size

1 Ace Development Bank Ltd.

0.7801418

44

Mediu

m 1058151279 Big

2 Asian Life Insurance Company

0.6001734

1

Mediu

m 622800000 Big

3 Bank of Asia Nepal Limited

0.5503645

83

Mediu

m 3840000000 Big

4 Birgunj Finance Ltd

0.6075287

36

Mediu

m 730800000 Big

5 Business Development Bank Ltd

0.7371631

21

Mediu

m 972984600 Big

6 Capital Mer. Bamk & Fin

0.7474285

71

Mediu

m 1309097300 Big

7 Citizen Investment Trust

0.6584444

44

Mediu

m 648000000 Big

8

Clean Energy Devlopment Bank

Ltd

0.6191616

77

Mediu

m 1816960000 Big

9 DCBL Bank Ltd.

0.7609150

33

Mediu

m 2795772672 Big

10 Gandaki Bikash Bank Ltd

0.7157894

74

Mediu

m 342000000 Big

11 Goodwill Finance Co. Ltd.

0.6018784

53

Mediu

m 543000000 Big

12 ICFC Bittiya Sanstha Ltd

0.6807878

79

Mediu

m 543483105 Big

13 Kaski Finance Limited

0.6779518

07

Mediu

m 348600000 Big

14 KIST Bank Ltd

0.6739354

84

Mediu

m 3100000000 Big

56

15 Lalitpur Finance Ltd.

0.5744745

76

Mediu

m 554215615 Big

16 Machhachapuchhre Bank Ltd

0.8194736

84

Mediu

m 2164171478 Big

17 Maha Laxmi Finance Ltd.

0.6162222

22

Mediu

m 756000000 Big

18

Nepal Aawas Bikas Beeta Co.

Ltd.

0.5704629

63

Mediu

m 362078424 Big

19 Nepal Credit & Com. Bank

0.6480239

52

Mediu

m 2322786300 Big

20

Nepal Doorsanchar Company

Limited

0.7501909

31

Mediu

m

6285000000

0 Big

21 Nepal Insurance Co.Ltd.

0.5890462

43

Mediu

m 355336464 Big

22 Nepal Share Markets Ltd.

0.5985057

47

Mediu

m 751680000 Big

23 NIDC Capital Markets Ltd.

0.8336936

94

Mediu

m 493112616 Big

24 NMB Bank Ltd

0.6187179

49

Mediu

m 3900000000 Big

25 Pokhara Finance Ltd.

0.6855348

84

Mediu

m 667244760 Big

26 Prabhu Finance Company Ltd

0.7924210

53

Mediu

m 775200000 Big

27 Prime Commercial Bank Limited

0.5628828

83

Mediu

m 4708620000 Big

28

Prime Life Insurance Company

Ltd

0.7380701

75

Mediu

m 615600000 Big

29 Royal Mer. Bank.& Fin

0.7547619

05

Mediu

m 503653584 Big30 Sagarmatha Insurance Co.Ltd 0.7034 Mediu 387477000 Big

57

m

31 Standard Finance Ltd.

0.7709558

82

Mediu

m 1362453576 Big

32 United Finance Ltd

0.5542713

57

Mediu

m 697743750 Big

33 Annapurna Finance Co.Ltd.

0.5169124

42 low 568713600 Big

34

Arun Valley Hydropower Dev Co

Ltd

0.2507534

25 low 1156801800 Big

35 Bank of Kathmandu

0.3077192

98 low 7749039990 Big

36 Central Finance Co. Ltd.

0.4260207

61 low 485154993 Big

37 Chilime Hydro power Co.

0.4967883

21 low 5997312000 Big

38

Citizen Bank International

Ltd

0.4960810

81 low 4440000000 Big

39 Everest Bank Ltd

0.3034643

51 low 9085312262 Big

40 Fewa Finance Co. Ltd.

0.4564419

48 low 728910000 Big

41 Global Bank Ltd

0.4939712

92 low 3135000000 Big

42 Gurkha Development Bank

0.4664473

68 low 1203840000 Big

43 Himalayan Bank Ltd.

0.3944173

91 low

1150000000

0 Big

44 IME Financial Institution

0.4814634

15 low 1180374912 Big

45

International Leasing & Fin.

Co.

0.4965853

66 low 1328400000 Big46 Kumari Bank Ltd 0.5140225 low 3627476580 Big

58

5647 Laxmi Bank Limited 0.35 low 3920167360 Big

48 Lumbini Bank Ltd.

0.5087782

81 low 2210000000 Big

49 Nabil Bank Ltd.

0.2116613

42 low

2540024547

2 Big

50

National Life Insurance

Company Ltd

0.3475748

5 low 440880000 Big

51 Nepal Industrial & Co.Bank

0.2587884

62 low 5930496000 Big

52 Nepal Investment Bank Ltd.

0.3695922

33 low

1239640483

5 Big

53 Nepal Life Insurance Co. Ltd.

0.3205653

71 low 1698000000 Big

54 Nepal SBI Bank Limited

0.2612566

37 low

1054889804

0 Big

55 Peoples Finance Limited.

0.5189545

45 low 650496000 Big

56

Purwanchal Grameen Bikash

Bank

0.1366735

75 low 579000000 Big

57 Sanima Vikash Bank Ltd.

0.2193158

95 low 3816960000 Big

58 Shikhar Insurance Co. Ltd.

0.4750318

47 low 392500000 Big

59

Shree Investment Finance Co.

Ltd

0.3460994

76 low 442814400 Big

60 Siddhartha Bank Limited

0.4532222

22 low 4242051000 Big

61 Soaltee Hotel Ltd.

0.1811386

14 low 3014686582 Big62 Standard Chartered Bank Ltd. 0.1338611 low 2894860980 Big

59

11 0

63 Uniliver Nepal Ltd.

0.1886404

52 low 4401866700 Big

64 Annapurna Bikas Bank

0.9741509

43 high 712320000 Big

65

Infrastructure Development

Bank Ltd

1.0590654

21 high 856000000 Big

66 Kasthamandap Dev Bank Ltd

1.1409782

61 high 588800000 Big

67

Narayani National Finance Co.

Ltd.

0.8819333

33 high 979145400 Big

68 Nepal Bangladesh Bank Ltd.

0.8822307

69 high 2418505700 Big

69 NDEP Development Bank Ltd.

0.9732758

62 high 633516600 Big70 Pashupati Dev Bank Limited 1.00875 high 728000000 Big

71

Sagarmatha Mer Banking &

Finance Ltd

0.8700724

64 high 362250000 Big

72

Siddhartha Development Bank

Limited

0.8760504

2 high 767550000 Big

73

Alpic Everest Finance Co.

Ltd.

0.6851807

23

Mediu

m 178682400

Smal

l

74

Civil Merchant Bittiya

Sanstha Ltd 0.701875

Mediu

m 207848160

Smal

l

75 Excel Development Bank Ltd.

0.5338928

57

Mediu

m 224000000

Smal

l

76

Kuber Merchant Bittiya

Sanstha Ltd

0.8463492

06

Mediu

m 189000000

Smal

l

77 Malika Devt Bank Ltd

0.7569281

05

Mediu

m 76500000

Smal

l78 Nava Durga Finance Co.Ltd. 0.8318 Mediu 189856050 Smal

60

m l

79 Nepal Express Finance Ltd

0.6691191

71

Mediu

m 253910800

Smal

l

80 Nirdhan Utthan Bank Ltd.

0.8197446

81

Mediu

m 306602150

Smal

l

81 Premier Finance Co. Ltd

0.6027027

03

Mediu

m 267876744

Smal

l

82

Prudential Finance Company

Ltd

0.7744927

54

Mediu

m 138000000

Smal

l

83 Siddhartha Finance Limited

0.7731372

55

Mediu

m 266526000

Smal

l

84 Union Finance Co. Ltd.

0.6526111

11

Mediu

m 314825040

Smal

l

85

United Insurance Co.

(Nepal)Ltd. 0.588

Mediu

m 177000000

Smal

l

86 Universal Finance Ltd.

0.7570121

95

Mediu

m 248857536

Smal

l

87 Bageshowori Dev.Bank

0.5041139

24 low 156420000

Smal

l

88 Chhimek Vikash Bank Ltd.

0.5125287

36 low 299497500

Smal

l

89 Everest Insurance Co. Ltd.

0.5131612

9 low 313875000

Smal

l

90 Paschimanchal Finance Co. Ltd

0.5094901

96 low 201286800

Smal

l

91 Alliance Insurance Co. Ltd.

1.1840677

97 high 81401238

Smal

l

92 Api Finance Limtied

1.1387341

77 high 189600000

Smal

l

93 Birat Laxmi Devt Bank

0.9588435

37 high 243459636

Smal

l94 Butwal Finance Ltd 1.0786725 high 253731443 Smal

61

66 l

95 CRYSTAL FINANCE Ltd.

1.1096581

2 high 81900000

Smal

l

96 Gorkha Finance Ltd.

1.4384112

15 high 166608095

Smal

l

97 Himalayan Gen.Insu. Co.Ltd. 0.84985 high 201600000

Smal

l

98

Imperial Financial

Institution Ltd

0.9732394

37 high 89815000

Smal

l

99 Kathmandu Finance Limited.

0.8922535

21 high 198306692

Smal

l

100 Lord Buddha Finance Limited

0.8630136

99 high 243090000

Smal

l

101 Lumbini Finance Ltd.

1.4260294

12 high 198900000

Smal

l

102 Lumbini General Insurance

0.9667187

5 high 160000000

Smal

l

103 Miteri Development Bank Ltd. 0.905625 high 115568640

Smal

l

104 Neco Insurance Co.

1.2946428

57 high 129360000

Smal

l

105

Nepal Finance and Saving

Co.Ltd.

0.9635294

12 high 208397475

Smal

l

106 Nerude Laghubitta Bikash Bank

2.6373684

21 high 3895000

Smal

l

107 Nilgiri Vikas Bank Ltd

0.9238053

1 high 56500000

Smal

l

108 Premier Insurance co. Ltd.

0.9678980

89 high 160140000

Smal

l

109 Reliable Finance Ltd

0.8914788

73 high 281160000

Smal

l110 Resunga Bikas Bank Ltd. 0.8934931 high 89352000 Smal

62

51 l

111 Sewa Bikas Bank Ltd

1.1411214

95 high 123050000

Smal

l

112 Shikhar Bittiya Sanstha Ltd

1.3435454

55 high 110000000

Smal

l

113 Siddhartha Insurance Limited

1.1016190

48 high 105000000

Smal

l

114 Subhechha Bikas Bank Limited

0.8933701

66 high 209815200

Smal

l

115 Swabalamwan Bikash Bank 1.4425 high 188677760

Smal

l

116 Triveni Bikas Bank Limited

0.8631012

66 high 207349246

Smal

l

63

APPENDIX B. Annual Report Excrept

APPENDIX C : Stocks selected for constructing different cross sectional

portfolio across different fiscal year

2007-2008 portfolio Selected CompaniesB/L Portfolio a) Unilever Ltd

b) Nabil Bank Ltd

c) Standard Chartered Bank Ltd

B/M Portfolio

a) Ace Development Bank Ltd

b) Chilime Hydropower

c) International Leasing Company

B/H Portfolio

No companies

S/L Portfolio No CompaniesS/M Portfolio a) Navadurga Finance Ltd

b) Premier Finance Ltd

c) Prudential Finance LtdS/h Portofolio a) Sagamartha Insurance

b) Citizen Investment Trust

c) Gorkha Finance

2008-2009 portfolio Selected CompaniesB/L Portfolio a) Unilever Ltd

b) Nabil Bank Ltd

c) Standard Chartered Bank Ltd

64

B/M Portfolio

a) Citizen Investment Trust

b) Goodwill Finance

c) International Leasing

B/H Portfolio

a) Ace Development Bank

b) Siddhartha Development Bank

c) People's Finance

S/L Portfolio No CompaniesS/M Portfolio a) Navadurga Finance Ltd

b) Premier Finance Ltd

c) Prudential Finance LtdS/H Portofolio a) Sagamartha Insurance

b) Excel Development Bank

c) Gorkha Finance

2009-2010 portfolio Selected CompaniesB/L Portfolio a) Kumari Bank Lts

b) Nabil Bank Ltd

c) Standard Chartered Bank Ltd

B/M Portfolio

a) Citizen Investment Trust

b) Unilever Ltd

c) Chilime Hyrdopower

B/H Portfolio

a) Nepal Share Market

b) Standard Finance

c) Birgunj Finance

S/L Portfolio No Companies

65

S/M Portfolio a) Goodwill Finance

b) Premier Finance Ltd

c) Sagarmatha InsuranceS/H Portofolio a) Navadurga Finance

b) Excel Development Bank

c) Gorkha Finance

2010-2011 portfolio Selected CompaniesB/L Portfolio a) Unilever Ltd

b) Nabil Bank Ltd

c) Standard Chartered Bank Ltd

B/M Portfolio

a) Citizen Investment Trust

b) NIDC capital market

c) Nepal Telecom

B/H Portfolio

a) Narayani National Finance

b) Siddhartha Development Bank

c) Nepal Bangladesh BankS/L Portfolio No CompaniesS/M Portfolio a) Navadurga Finance

b) Premier Finance Ltd

c) Sagarmatha InsuranceS/H Portofolio a) Lumbini General Insurance

b) ReliableFinance

c) Gorkha Finance

2011-2012 portfolio Selected CompaniesB/L Portfolio a) Unilever Ltd

b) Nabil Bank Ltd

c) Standard Chartered Bank Ltd

B/M Portfolio

a) Citizen Investment Trust

b) NIDC capital market

66

c) Nepal Telecom

B/H Portfolio

a) Narayani National Finance

b) Siddhartha Development Bank

c) Nepal Bangladesh BankS/L Portfolio a) Everest Insurance

b) Paschimanchal Finance

c) Bageshwori development BankS/M Portfolio a) Navadurga Finance

b) Premier Finance Ltd

c) Sagarmatha InsuranceS/H Portofolio a) Lumbini General Insurance

b) ReliableFinance

c) Gorkha Finance

67

68

APPENDIX D: Fama-French model fitting data

Period

RetB/L

RetB/M

RetB/H

RetS/L

RetS/M

RetS/H Rf

B/L-Rf

B/M-Rf

B/H-Rf

S/M-Rf

S/H-Rf

Rm-Rf

SMB HML

Dashain

Jul

2007-Aug

0.033996

0.97593 0 0

0.464506

0.258933

0.003483

0.030513

0.972447

-0.00348

0.461023

0.2554

5

0.009982

-0.0955

0.112468 0 0

2007-Sept

0.091899

1.690517 0 0 0

0.0238

1

0.003483

0.088416

1.687034

-0.00348

-0.0034

8

0.020326

0.153919

-0.5862

-0.03404 0 0

2007-Oct

0.088255

0.109269 0 0 0

0.067284

0.003483

0.084772

0.105786

-0.00348

-0.0034

8

0.063801

0.050722

-0.0434

1

-0.01049 1 0

2007-Nov

0.063996

0.076889 0 0

0.005051

-0.0125

0.003483

0.060513

0.073406

-0.00348

0.001567

-0.0159

8

0.059219

-0.0494

4

-0.03825 0 0

2007-dec

-0.2636

0.302848 0 0

0.011348

0.026284

0.003483

-0.2670

8

0.299365

-0.00348

0.007864

0.022801

0.117264

-0.0005

4

0.144943 0 0

2008-jan

0.20145

0.696445 0 0

0.009602

0.015605

0.003483

0.197966

0.692962

-0.00348

0.006119

0.012122

-0.0687

9

-0.2909

-0.09292 0 0

2008-Feb

-0.05715

-0.1527 0 0

0.011642

0.0301

6

0.003483

-0.0606

4

-0.1561

8

-0.00348

0.008158

0.026676

-0.1541

5

0.083885

0.043656 0 0

2008-Mar

-0.09516

-0.05696 0 0

0.004902

-0.0018

9

0.003483

-0.0986

5

-0.0604

4

-0.00348

0.001419

-0.0053

7

-0.1258

6

0.051711

0.046637 0 0

2008-Apr

-0.01168

-0.05678 0 0

0.398551

-0.0075

3

0.003483

-0.0151

6

-0.0602

6

-0.00348

0.395067

-0.0110

2

0.041189

0.153158

0.002074 0 0

2008-May

0.025733

-0.0912 0 0 0

0.003607

0.003483

0.0222

5

-0.0946

8

-0.00348

-0.0034

8

0.000124

0.076295

0.023024

-0.01106 0 0

2008-June

0.076964

0.002019 0 0 0 0

0.003483

0.073481

-0.0014

6

-0.00348

-0.0034

8

-0.0034

8

0.150797

-0.0263

3

-0.03848 0 0

2008-Jul

0.11131

-0.15021 0 0

0.106764

0.040941

0.003483

0.107827

-0.1537

-0.00348

0.103281

0.037457

0.031664

0.062203

-0.03518 0 1

2008-Aug

0.159514

0.184608

0.614841

0 0.176418

0.004301

0.004668

0.154846

0.179941

0.610174

0.1717

5

-0.0003

0.121349

-0.2594

0.229814

0 0

69

t 7 12008-Sept

-0.11119

-0.02788

-0.26907 0

0.132288

0.049094

0.004668

-0.1158

5

-0.0325

5

-0.27374

0.1276

2

0.044426

-0.1049

2

0.196507

-0.0544 0 0

2008-Oct

-0.021

0.172926

-0.00146 0

0.006116

0.047752

0.004668

-0.0256

7

0.168258

-0.00613

0.001448

0.043084

-0.0477

4

-0.0322

0.033644 1 0

2008- Nov

-0.13822

-0.01215

-0.05201 0

0.006614

-0.0133

1

0.004668

-0.1428

9

-0.0168

2

-0.05668

0.001946

-0.0179

8

-0.1407

2

0.065231

0.036451 0 0

2008-Dec

-0.02069

-0.0114

-0.19437 0

0.216941

0.073746

0.004668

-0.0253

6

-0.0160

6

-0.19904

0.212273

0.069078

-0.0939

6

0.172383

-0.04997 0 0

2009-Jan

-0.04717

-0.01562

-0.05481 0

-0.0047

7

0.014879

0.004668

-0.0518

4

-0.0202

9

-0.05947

-0.0094

4

0.010211

-0.1067

8

0.042569

0.003621 0 0

2009-Feb

-0.02562

-0.07527

-0.01405 0

0.085264

-0.1452

2

0.004668

-0.0302

9

-0.0799

4

-0.01872

0.080596

-0.1498

9

0.000955

0.018329

-0.06682 0 0

2009-Mar

-0.00447

-0.02542

0.470565 0 0

0.159076

0.004668

-0.0091

4

-0.0300

9

0.465897

-0.0046

7

0.154408

0.000878

-0.0938

7

0.317055 0 0

2009-Apr

0.053669

0.039656

-0.33832 0

-0.0652

1

0.187512

0.004668

0.049001

0.034988

-0.34299

-0.0698

8

0.182844

-0.0135

6

0.122431

-0.10224 0 0

2009-May

0.023301

0.057938

-0.02702 0

0.047613

-0.0483

4

0.004668

0.018634

0.0532

7

-0.03169

0.042945

-0.0530

1

-0.0051

4

-0.0183

1

-0.04933 0 0

2009-Jun

0.101435

-0.01363

-0.1135 0

-0.1694

8

-0.2344

9

0.004668

0.096767

-0.0183

-0.11817

-0.1741

5

-0.2391

6

0.007103

-0.1260

9

-0.22471 0 0

2009-jul

0.08001

0.050141

-0.06599 0 0

-0.0319

7

0.004668

0.075342

0.045473

-0.07065

-0.0046

7

-0.0366

4

0.115498

-0.0320

5

-0.08898 0 1

2009-Aug

0.044525

-0.01649

-0.01961 0

0.050939

0.001255

0.005182

0.039342

-0.0216

7

-0.02479

0.045756

-0.0039

3

-0.0414

3

0.014588

-0.03144 0 0

2009-Sep

-0.22266

-0.02188

-0.05968 0

-0.1838

4

0.014172

0.005182

-0.2278

4

-0.0270

6

-0.06486

-0.1890

2

0.0089

9

-0.1327

1

0.0448

5

0.088577 0 0

2009-Oct

-0.08355

-0.01041

-0.03846 0

-0.0058

5

0.005193

0.005182

-0.0887

3

-0.0155

9

-0.04365

-0.0110

3

1.04E-05

-0.0374

6

0.043921

0.025138 1 0

200 - 0.0 - 0 - - 0.0 - 0.0 - - - - 0.0 0.0 0 0

70

9- Nov

0.06856

13146

0.03391

0.0305

9

0.0191

305182

0.0737

407963

0.03909

0.0357

7

0.0243

1

0.0750

913202

07761

2009-Dec

-0.03557

-0.0164

-0.04384 0

0.028366

0.043001

0.005182

-0.0407

6

-0.0215

9

-0.04902

0.023183

0.037819

-0.0375

1

0.055726

0.01737 0 0

2010-Jan

-0.07593

-0.02012

-0.01185 0

0.039381

-0.1443

9

0.005182

-0.0811

2

-0.0253

-0.01703

0.034199

-0.1495

7

-0.0373

5

0.000964

-0.04015 0 0

2010-Feb

-0.05667

0.017423

-0.04559 0

0.006029

-0.0141

5

0.005182

-0.0618

5

0.012241

-0.05077

0.000846

-0.0193

4

-0.0766

4

0.025571

-0.00154 0 0

2010-Mar

-0.0096

-0.00291

-0.00917 0

0.009338

-0.0281

8

0.005182

-0.0147

8

-0.0080

9

-0.01436

0.004155

-0.0333

7

-0.0306

0.000944

-0.01388 0 0

2010-Apr

-0.08817

-0.03813

-0.03107 0

-0.0272

6

-0.1461

5

0.005182

-0.0933

5

-0.0433

1

-0.03625

-0.0324

4

-0.1513

4

-0.0706

4

-0.0053

5

-0.04453 0 0

2010-May

0.023773

0.034614

0.012168 0

-0.0412

1

-0.0434

9

0.005182

0.018591

0.029431

0.006986

-0.0463

9

-0.0486

7

-0.0241

1

-0.0517

5

-0.02755 0 0

2010-Jun

0.040182

-0.01633

-0.11703 0

-0.0553

8

-0.0015

0.005182

0.034999

-0.0215

1

-0.12221

-0.0605

6

-0.0066

8

0.074515

0.012098

-0.07935 0 0

2010-jul

0.068333

-0.06034

-0.16407 0

-0.0099

1

-0.0025

2

0.005182

0.063151

-0.0655

3

-0.16925

-0.0150

9

-0.0077

-0.0008

1

0.047886

-0.11746 0 1

2010-Aug

-0.07794

0.056706

-0.1085 0 0

-0.1086

7

0.006088

-0.0840

3

0.050618

-0.11459

-0.0060

9

-0.1147

5

-0.0563

9

0.007024

-0.06961 0 0

2010-Sep

-0.08349

-0.03652

-0.02133 0

-0.0056

5

-0.0094

6

0.006088

-0.0895

8

-0.0426

1

-0.02742

-0.0117

4

-0.0155

5

-0.1085

8

0.042077

0.026351 0 0

2010-Oct

0.004593

0.042017

-0.05556 0

-0.0683

8

0.023411

0.006088

-0.0014

9

0.035929

-0.06165

-0.0744

6

0.017323

0.026083

-0.0120

1

-0.01837 1 0

2010- Nov

-0.0566

0.004873

-0.05356 0

-0.0143

-0.0864

0.006088

-0.0626

9

-0.0012

2

-0.05965

-0.0203

9

-0.0924

9

0.001859

0.001529

-0.04168 0 0

2010-Dec

-0.05606

-0.01807

0.016005 0

-0.0171

1

0.049983

0.006088

-0.0621

5

-0.0241

6

0.009917

-0.0232

0.043895

-0.0756

5

0.030334

0.061024 0 0

2011-Jan

0.087271

-0.0393

-0.0301

0 -0.0695

-0.0255

0.006088

0.081183

-0.0454

-0.0362

-0.0756

-0.0315

0.016618

-0.0375

-0.0714

0 0

71

9 3 1 8 2 9 9 5

2011-Feb

-0.01063

-0.00498

-0.03093 0

-0.0515

1

-0.0482

2

0.006088

-0.0167

2

-0.0110

7

-0.03702

-0.0576

-0.0543

-0.0027

4

-0.0177

3

-0.03426 0 0

2011-Mar

-0.05196

0.016487

-0.08254 0 0

-0.0956

6

0.006088

-0.0580

5

0.010399

-0.08863

-0.0060

9

-0.1017

4

-0.0746

2

0.007454

-0.06312 0 0

2011-Apr

-0.03253

-0.01649

-0.06935 0

-0.0066

2

-0.0392

4

0.006088

-0.0386

2

-0.0225

7

-0.07544

-0.0127

1

-0.0453

2

-0.0155

1

0.024169

-0.03803 0 0

2011-May

-0.04715

-0.01291

-0.04639 0

-0.0106

6

-0.0240

5

0.006088

-0.0532

3

-0.019

-0.05248

-0.0167

5

-0.0301

4

-0.0703

4

0.023911

-0.01165 0 0

2011-Jun

-0.17515

-0.15556

-0.0969 0

0.004505

-0.0849

1

0.006088

-0.1812

4

-0.1616

5

-0.10299

-0.0015

8

-0.091

-0.1690

5

0.115736

-0.00333 0 0

2011-jul

0.25748

-0.11304

0.064901 0 0

-0.0231

1

0.006088

0.251392

-0.1191

3

0.058813

-0.0060

9

-0.0292

0.235231

-0.0774

8

-0.10785 0 1

2011-Aug

-0.05714

0.030656

-0.05656

-0.0276

8 0

0.049427

0.001304

-0.0584

5

0.029352

-0.05786

-0.0013

0.048123

-0.0275

4

0.039869

0.031439 0 0

2011-Sep

-0.05509

-0.05946

-0.07281

-0.0624

6

-0.0111

1

-0.1113

3

0.001304

-0.0564

-0.0607

7

-0.07412

-0.0124

2

-0.1126

3

-0.0923

0.019421

-0.06119 0 0

2011-Oct

-0.05804

0.002232

0.021034

-0.0115

9

0.000233

-0.0238

8

0.001304

-0.0593

5

0.000928

0.01973

-0.0010

7

-0.0251

8

0.031108

0.0015

6

0.030826 1 0

2011- Nov

-0.01207

0.02607

0.01782

0.010607 0

0.011797

0.001304

-0.0133

7

0.024766

0.016515

-0.0013

0.010493

-0.0153

-0.0048

8

0.018155 0 0

2011-Dec

-0.04832

-0.01697

-0.0263

-0.0305

3 0

-0.0386

6

0.001304

-0.0496

3

-0.0182

8

-0.02761

-0.0013

-0.0399

7

-0.0339

7

0.008573

0.005283 0 0

2012-Jan

0.084385

0.050393

-0.04136

0.031138

-0.0491

5

-0.0240

9

0.001304

0.083081

0.049089

-0.04267

-0.0504

5

-0.0253

9

0.017414

-0.0555

5

-0.07492 0 0

2012-Feb

-0.04931

-0.02435

-0.13002

-0.0678

9

-0.0985

4

-0.0268

3

0.001304

-0.0506

1

-0.0256

5

-0.13132

-0.0998

4

-0.0281

3

-0.0336

5

0.026106

-0.05377 0 0

2012-Mar

0.000944

0.037176

0.001844

0.013321

-0.0146

1

-0.0241

5

0.001304

-0.0003

6

0.035872

0.00054

-0.0159

2

-0.0254

6

-0.0044

5

-0.0242

7

-0.01459 0 0

201 0.0 - 0.0 0.0 - - 0.0 0.0 - 0.0 - - 0.0 - - 0 0

72

2-Apr

24465

0.03317

17937

03077

0.0858

6

0.0493

601304

23161

0.0344

716633

0.0871

6

0.0506

684188

0.0652

6

0.00228

2012-May

0.080627

0.167725

0.293292

0.180548 0

0.286305

0.001304

0.079323

0.166421

0.291988

-0.0013

0.285001

0.160333

-0.0873

1

0.252774 0 0

2012-Jun

-0.10406

-0.08458

0.034895

-0.0512

50.0

1

-0.1573

4

0.001304

-0.1053

7

-0.0858

9

0.033591

0.008696

-0.1586

4

-0.0707

6

-0.0384

1

0.051629 0 0

2012-jul

0.166877

0.02493

-0.06964

0.040722

-0.0064

7

0.013136

0.001304

0.165573

0.023626

-0.07095

-0.0077

8

0.011832

0.067443

-0.0414

8

-0.10722 0 1

73

APPENDIX E. SQl InnerJoin Query in Excel:

To Perform Inner Join in Excel

i. First Open a new Excel file in 2007

ii. Goto Data -> > From Other Sources > From Microsoft Query

iii. Choose columns (if you don't see any list of columns, make sure to check

Options > System Tables)

iv. edit the Data > Connections > choose your new connection > Properties >

Definition > Command text

For Example: The given query determines inner join between sheet 'small-big'

and 'high-low' on the common column 'company'

SELECT `'small-big$'`.`s#no`, `'small-big$'`.company, `'small-

big$'`.closingprice, `'high-low$'`.company, `'high-

low$'`.bookvaluepershareFROM `<folder>\<file-name>.xls`.`'high-low$'` `'high-

low$'`, `<folder>\<file-name>.xls`.`.`'small-big$'` `'small-big$'` WHERE

`'small-big$'`.company = `'high-low$'`.company

APPENDIX F. Scatter Plots

74

APPENDIX G. Heteroscedasticty checking macro:

75

* BREUSCH-PAGAN & KOENKER TEST MACRO *

* See 'Heteroscedasticity: Testing and correcting in SPSS'

* by Gwilym Pryce, for technical details.

* Code by Marta Garcia-Granero 2002/10/28.

* The MACRO needs 3 arguments:

* the dependent, the number of predictors and the list of predictors

* (if they are consecutive, the keyword TO can be used) .

* (1) MACRO definition (select an run just ONCE).

DEFINE bpktest(!POSITIONAL !TOKENS(1) /!POSITIONAL !TOKENS(1) /!POSITIONAL !CMDEND).

* Regression to get the residuals and residual plots.

REGRESSION

/STATISTICS R ANOVA

/DEPENDENT !1

/METHOD=ENTER !3

/SCATTERPLOT=(*ZRESID,*ZPRED)

/RESIDUALS HIST(ZRESID) NORM(ZRESID)

/SAVE RESID(residual) .

do if $casenum=1.

print /"Examine the scatter plot of the residuals to detect"

/"model misspecification and/or heteroscedasticity"

/""

/"Also, check the histogram and np plot of residuals "

/"to detect non normality of residuals "

/"Skewness and kurtosis more than twice their SE indicate non-normality ".

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end if.

* Checking normality of residuals.

DESCRIPTIVES

VARIABLES=residual

/STATISTICS=KURTOSIS SKEWNESS .

* New dependent variable (g) creation.

COMPUTE sq_res=residual**2.

compute constant=1.

AGGREGATE

/OUTFILE='tempdata.sav'

/BREAK=constant

/rss = SUM(sq_res)

/N=N.

MATCH FILES /FILE=*

/FILE='tempdata.sav'.

EXECUTE.

if missing(rss) rss=lag(rss,1).

if missing(n) n=lag(n,1).

compute g=sq_res/(rss/n).

execute.

* BP&K tests.

* Regression of g on the predictors.

REGRESSION

/STATISTICS R ANOVA

/DEPENDENT g

/METHOD=ENTER !3

/SAVE RESID(resid) .

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*Final report.

do if $casenum=1.

print /" BP&K TESTS"

/" ==========".

end if.

* Routine adapted from Gwilym Pryce.

matrix.

compute p=!2.

get g /variables=g.

get resid /variables=resid.

compute sq_res2=resid&**2.

compute n=nrow(g).

compute rss=msum(sq_res2).

compute ii_1=make(n,n,1).

compute i=ident(n).

compute m0=i-((1/n)*ii_1).

compute tss=transpos(g)*m0*g.

compute regss=tss-rss.

print regss

/format="f8.4"

/title="Regression SS".

print rss

/format="f8.4"

/title="Residual SS".

print tss

/format="f8.4"

/title="Total SS".

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compute r_sq=1-(rss/tss).

print r_sq

/format="f8.4"

/title="R-squared".

print n

/format="f4.0"

/title="Sample size (N)".

print p

/format="f4.0"

/title="Number of predictors (P)".

compute bp_test=0.5*regss.

print bp_test

/format="f8.3"

/title="Breusch-Pagan test for Heteroscedasticity"

+ " (CHI-SQUARE df=P)".

compute sig=1-chicdf(bp_test,p).

print sig

/format="f8.4"

/title="Significance level of Chi-square df=P (H0:"

+ "homoscedasticity)".

compute k_test=n*r_sq.

print k_test

/format="f8.3"

/title="Koenker test for Heteroscedasticity"

+ " (CHI-SQUARE df=P)".

compute sig=1-chicdf(k_test,p).

print sig

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/format="f8.4"

/title="Significance level of Chi-square df=P (H0:"

+ "homoscedasticity)".

end matrix.

!ENDDEFINE.

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