fundamental analysis of nepalese stock exchange return using fama french three factor model

7/29/2019 Fundamental Analysis of Nepalese Stock Exchange Return Using Fama French Three Factor Model

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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 nave 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

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 CapitalAsset 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.


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Thus the major objectives of this study are:

i. To Test the Fama-French Model in return of Nepalese Stock Exchangeii. To Verify whether Nepalese stock return corresponds to established portfolio theoriesiii. To attempt to explain the variation and risk factor associated with the returniv. To address the seasonality issue in time series stock returnv. To carry out fundamental analysis of stock return using three factor model

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 byevaluating covariance of asset return with overall market portfolio. The Sharpe-Lintner-Mossin CAPM equation

can be mathematically represented as:

[ ] .......................i)

= Cov( ............................. ii)

where,

Expected Return on any Asset

= Risk Free Rate

= Expected Return on Market Portfolio

= 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 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:


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i) Market proxy might be mean variance efficient when in actuality the true market portfolio may beotherwise.

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).

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

[ ] .......................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

: sensitivity to size

: 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


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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 () 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

for three factor model being

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

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


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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 2010-2012

No. of Enlisted Company 142 159 176 207 216

No. 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.

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


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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 .

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

Table 2.


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Table 2

Year No. of Companies

Selected

Median Market Cap BE/ME

30th percentile 70th percentileAug 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 andHML parameter for each year. It should be noted that no company assorted 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:


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Table 3 (Average Monthly Return of various portfolio between Aug 2007 - Jul 2012)

Portfolios 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 .372

SMB -.0095727053 .11035779470 -2.701 12.714

HML -.0073232543 .08712751846 1.441 4.200

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 HML

Rm-Rf Pearson Correlation 1 -.488 .027

Sig. (2-tailed) .000 .838

N 60 60 60

SMB Pearson Correlation -.488** 1 -.093


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Sig. (2-tailed) .000 .478

N 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.

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 .864

B/M .056 .008 1.489 .058 .142 .954

B/H -.019 .004 -1.060 .069 .293 .945

S/L .001 -.003 .185 -.225 .854 .823

S/M .014 -.028 1.065 -.596 .291 .554

S/H -.009 .033 -.798 .820 .428 .416

Rm-Rf -.012 .016 -1.037 .415 .304 .680

SMB-.010 .001

-.645 .024 .522 .981

HML -.009 .021 -.769 .519 .445 .606


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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 .000

B/M .066 -.116 1.775 -.899 .081 .372

B/H -.016 -.031 -.901 -.489 .371 .626

S/L .000 .008 -.059 .622 .953 .536

S/M .012 .006 .852 .137 .398 .892

S/H-.007 .006

-.603 .157 .549 .876

Rm-Rf -.020 .109 -1.843 2.978 .071 .004

SMB -.010 .002 -.646 .029 .521 .977

HML .000 -.092 .028 -2.336 .978 .023

Similarly 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 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

( ) ............... vii)

where : excess return on jth portfolio on time 't'

: excess market return on jth portfolio on time 't'

: return on size factor portfolio on time 't'

: return on value factor portfolio

: market, size and value factor exposure

: intercept that indicates abnormal return on portfolio

: mean zero asset specific return of portfolio 'j '


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The hypothesized model thus illustrated above requires that term be zero and be independently and identically

distributed as a normal distribution with zero mean and constant variance. Therefore 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.

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 HML

VIF 1.314 1.325 1.009

4.6Regression 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-value

B/L (Constant) -.010 -1.166 .249

Rm-Rf .563 4.825 .000

SMB -.245 -2.759 .008

HML -.348 -3.538 .001

R Square: .546

Adj. R square .521

Durbin-Watson 2.468

B/H

(Constant) -.015 -1.644 .106Rm-Rf .303 2.410 .019

SMB -.364 -3.809 .000

HML 1.089 10.294 .000

R Square: .735

Adj. R square .721


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Durbin-Watson 1.906

B/M

(Constant) 0.028842 1.240551 0.219944

Rm-Rf -0.3886 -1.24255 0.21921

SMB -2.07231 -8.70273 5.47E-12

HML 0.08312 0.315716 0.753392

R Square: 0.614

Adj. R square 0.593

Durbin-Watson 1.361

S/M

(Constant) .014 1.059 .294

Rm-Rf .186 1.062 .293

SMB .178 1.334 .188

HML .294 1.990 .051

R Square: .090

Adj. R square .042

Durbin-Watson 1.490

S/H

(Constant) -.003 -.272 .786

SMB .129 1.320 .192

Rm-Rf .276 2.144 .036

HML .547 5.044 .000

R Square: .346

Adj. R square .311

Durbin-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

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.


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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 wecan 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 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 test

Val p-value val p-value

B/L 30.239 .0000 9.423 .0242

B/M 40.754 .0000 10.495 0.0148

B/H 61.149 .0000 22.223 0.0001

S/M 14.513 0.0023 2.941 0.4

S/H 1.325 0.7232 0.703 0.8725


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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 P>|t| 0.5458

Constant -.0101 .0109 -.9276 .3576

Rm-Rf .5635 .1996 2.8235 .0066

SMB -.2453 .2867 -.8556 .3959

HML -.3478 .1834 -1.8968 .0630

B/H Portfolio R-Square


Constant -.0154 .0113 -1.3628 .1784

Rm-Rf .3029 .1587 1.9093 .0613

SMB -.3644 .3856 -.9451 . 3487

HML 1.0890 .2285 4.7663 .0000

B/M Portfolio R-Square


Constant .0288 .0258 1.1198 .2676

Rm-Rf -.3886 .3260 -1.1921 .2382

SMB -2.0723 .9165 -2.2611 .0277

HML .0831 .4941 .1682 .8670

S/H Portfolio R-Square


Constant -.0026 .0104 -.2516 .8023

Rm-Rf .2761 .1445 1.9110 .0611

SMB .1295 .1874 .6907 .4926

HML .5469 .1670 3.2747 .0018

S/M portfolio R-Square


Constant .0138 .0155 .8930 .3757


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Rm-Rf .1861 .1910 .9741 .3342

SMB .1781 .2179 .8174 .4171

HML .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.

4.7 CAPM analysis.

Regressing the excess return on portfolio against excess market return using the following relation

( )

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 0.009

p-value constant .704 0.301 0.078 0.447 0.504

(Rm-Rf) .712 .568 .949 0.208 0.080


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p-value Rm-Rf 0.000 .005 0.025 0.125 0.615

R square 0.396 0.127 0.083 0.04 0.004

Adjusted 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.

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

0.000

Ace Development Bank


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Constant .0065 .0200 .7461

RmRf 1.2045 .2995 .0002

SMB .1939 .3839 .6158HML .7737 .3487 .0310

-.0023 .0207 .9137

1.1733 .30 .0004

R-sq 0.5245

P-value .0.0001

0.3531

0.0004

Chilime Hydropower


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 2007August 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 byinefficiency 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 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


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23

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.

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.


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7. Recommendation

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 Paper2000-2004.

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

Carhart, Mark M. (1997). On Persistence in Mutual Fund Performance.Journal of Finance52 (1): 5782

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.


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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 Journalof 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.

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.


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

Management11, 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


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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 Size

1 Nabil Bank Ltd. 0.079317536 Low 36259980750 big

2Nepal Investment Bank Ltd. 0.095661224 Low 29495927300 big

3Standard Chartered Bank Ltd. 0.074980966 Low 46497547200 big

4Himalayan Bank Ltd. 0.13370202 Middle 24081057000 big

5Nepal SBI Bank Limited 0.118848445 Low 13198269201 big

6

Everest Bank Ltd 0.093470626 Low 11838960000 big7

Bank of Kathmandu 0.069280851 Low 14173820550 big

8Nepal Industrial & Co.Bank 0.108325545 Low 10169280000 big

9Machhachapuchhre Bank Ltd 0.096237154 Low 10393888945 big

10Laxmi Bank Limited 0.106433064 Low 8147160000 big

11Kumari Bank Ltd 0.136069652 Middle 9045000000 big

12Siddhartha Bank Limited 0.114835069 Low 9538560000 big

13Uniliver Nepal Ltd. 0.062197561 Low 3774870000 big

14Chilime Hydro power Co. 0.170691421 Middle 11396352000 big

15Nepal Insurance Co.Ltd. 0.539285714 High 359444400

smal

l

16Himalayan Gen.Insu. Co.Ltd. 0.579710145 High 217350000

smal

l

17United Insurance Co.(Nepal)Ltd. 0.650793651 High 189000000

smal

l

18Everest Insurance Co. Ltd. 0.501443299 High 261900000

smal

l

19Premier Insurance co. Ltd. 0.720333333 High 90000000

smal

l


23/50

28

20Neco Insurance Co. 1.441550388 High 70950000

smal

l

21Alliance Insurance Co. Ltd. 0.954025974 High 77000000

smal

l

22Sagarmatha Insurance Co.Ltd 0.794117647 High 171666000

small

23Nepal Life Insurance Co. Ltd. 0.073505093 Low 4172500000 big

24Life Insurance Co. Nepal 0.112895257 Low 2530000000 big

25Nepal Finance and Saving

Co.Ltd. 0.523936842 High 142500000

smal

l

26NIDC Capital Markets Ltd. 0.138190899 Middle 912262500 big

27 National Finance Co. Ltd. 0.190371429 Middle 1647258900 big

28Nepal Share Markets Ltd. 0.080035928 Low 7214400000 big

29Annapurna Finance Co.Ltd. 0.110040816 Low 2963520000 big

30Kathmandu Finance Limited. 0.663157895 High 108157500

smal

l

31Peoples Finance Limited. 0.185979971 Middle 587160000 big

32Citizen Investment Trust 0.516552511 High 262800000

smal

l

33Nepal Aawas Bikas Beeta Co.

Ltd. 0.229971388 Middle 493619820 big

34Narayani Finance Limited 0.133297491 Middle 744360840 big

35Ace Development Bank Ltd. 0.131028037 Middle 2739200000 big

36Gorkha Finance Ltd. 0.6514 High 60000000

smal

l

37Universal Finance Ltd. 0.591908127 High 170418072

smal

l

38Maha Laxmi Finance Ltd. 0.120167926 Middle 952800000 big

39Lalitpur Finance Ltd. 0.300628931 Middle 603703125 big


24/50

29

40Goodwill Finance Co. Ltd. 0.194249605 Middle 316500000

smal

l

41Paschimanchal Finance Co. Ltd 0.547679181 High 148258000

smal

l

42Lumbini Finance Ltd. 0.433684211 High 171000000

small

43Siddhartha Finance Limited 0.11615041 Low 698360000 big

44Alpic Everest Finance Co. Ltd. 0.206958042 Middle 446160000 big

45United Finance Ltd 0.159144385 Middle 701250000 big

46International Leasing & Fin. Co. 0.24047541 Middle 878400000 big

47

Shree Investment Finance Co. Ltd 0.289454225 Middle 381696000 big

48Central Finance Co. Ltd. 0.200337838 Middle 577200000 big

49Premier Finance Co. Ltd 0.318306998 Middle 146721600

smal

l

50Nava Durga Finance Co.Ltd. 0.389248366 Middle 121025142

smal

l

51Butwal Finance Ltd 0.126792636 Middle 719016072 big

52Standard Finance Ltd. 0.156956989 Middle 675180000 big

53Cosmic Mer.Bank & Fin. 0.634230769 High 136592820

smal

l

54KIST Merchant Bank. & Fin 0.109749499 Low 1996000000 big

55World Merchant Bank Ltd 0.303030303 Middle 594000000 big

56Birgunj Finance Ltd 0.088409091 Low 958320000 big

57

Capital Mer. Bamk & Fin 0.096062992 Low 4089400000 big

58Everest Finance Ltd, 0.6294 High 40000000

smal

l

59Prudential Bittiya Sans 0.411963636 Middle 137500000

smal

l

60Royal Mer. Bank.& Fin 0.241121495 Middle 323204735 smal


25/50

30

l

61Guheyshwori Mer. Bank. Fin 0.139526012 Middle 533543245 big

62Bhajuratna Fin.& Sav. Co. Ltd. 1.150491803 High 42700000

smal

l

63Development Credit Bank Ltd. 0.151192982 Middle 9468748800 big

64Nirdhan Utthan Bank Ltd. 2.424552239 High 105956614

smal

l

65Chhimek Vikash Bank Ltd. 0.728037736 High 82150000

smal

l

66Siddhartha Vikash Bank Ltd 0.076327869 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. 0.042957746 Low 47311945530 Big

2 Nepal Investment Bank Ltd. 0.099236311 Low 33410116332 Big

3 Standard Chartered Bank Ltd. 0.018266223 Low 56011180640 Big

4 Himalayan Bank Ltd. 0.060488636 Low 21405384000 Big

5 Nepal SBI Bank Limited 0.093315789 Low 16596102900 Big

6 Everest Bank Ltd 0.065291242 Low 15683031000 Big

7 Bank of Kathmandu 0.091754286 Low 14776963250 Big

8 Nepal Industrial & Co.Bank 0.125230906 Low 12841804800 Big

9 Machhachapuchhre Bank Ltd 0.252351738 Middle 6428599380 Big

10 Laxmi Bank Limited 0.157749529 Middle 11661674382 Big

11 Kumari Bank Ltd 0.237914286 Middle 7547904000 Big

12 Siddhartha Bank Limited 0.1228 Low 8280000000 Big

13 NMB Bank Ltd. 0.226452906 Middle 5489000000 Big

14 Uniliver Nepal Ltd. 0.028705882 Low 3912975000 Big


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31

15 Chilime Hydro power Co. 0.085131173 Low 9455616000 Big

16 National LifeInsu. Co.Ltd. 0.211588629 Middle 789360000 Big

17 Himalayan Gen.Insu. Co.Ltd. 0.845263158 High 287280000 Small

18 United Insurance Co.(Nepal)Ltd. 0.384353741 High 176400000 Small

19 Everest Insurance Co. Ltd. 0.864421053 High 288562500 Small20 Premier Insurance co. Ltd. 0.722736842 High 193800000 Small

21 Sagarmatha Insurance Co.Ltd 0.464920635 High 141372000 Small

22 Nepal Life Insurance Co. Ltd. 0.105606178 Low 3885000000 Big

23 Life Insurance Co. Nepal 0.214779412 Middle 1700000000 Big

24 Lumbini General Insurance 0.763315217 High 230000000 Small

25 Nepal Finance and Saving Co.Ltd. 0.41785 High 120000000 Small

26 NIDC Capital Markets Ltd. 0.138708333 Low 1215000000 Big

27 National Finance Co. Ltd. 0.134685714 Low 1647258900 Big

28 Nepal Share Markets Ltd. 0.361702128 Middle 1218240000 Big

29 Annapurna Finance Co.Ltd. 0.255618557 Middle 2542176000 Big

30 Kathmandu Finance Limited. 0.434570552 High 247434000 Small

31 Peoples Finance Limited. 0.382035088 High 575995830 Big

32 Citizen Investment Trust 0.349328767 Middle 584000000 Big

33 Nepal Aawas Bikas Beeta Co. Ltd. 0.647102041 High 338637775 Small

34 Narayani Finance Limited 0.139041219 Low 2384352972 Big

35 Gorkha Finance Ltd. 0.390844156 High 183414000 Small

36 Universal Finance Ltd. 0.30880597 Middle 201731640 Small

37 Maha Laxmi Finance Ltd. 0.148513854 Low 1143360000 Big

38 Goodwill Finance Co. Ltd. 0.199296 Middle 721713125 Big

39 Paschimanchal Finance Co. Ltd 0.475255973 High 148258000 Small

40 Lumbini Finance Ltd. 0.300249554 Middle 504900000 Small

41 Siddhartha Finance Limited 0.083231571 Low 1167499446 Big

42 Alpic Everest Finance Co. Ltd. 0.463970894 High 375180000 Small

43 United Finance Ltd 0.146547912 Low 1221000000 Big

44 International Leasing & Fin. Co. 0.289852459 Middle 3952800000 Big

45 Shree Investment Finance Co. Ltd 0.156347607 Middle 800352000 Big

46 Central Finance Co. Ltd. 0.261724138 Middle 500638049 Small

47 Premier Finance Co. Ltd 0.310728863 Middle 162993600 Small

48 Nava Durga Finance Co.Ltd. 0.293620352 Middle 232989428 Small

49 Butwal Finance Ltd 0.121550388 Low 852772560 Big

50 Standard Finance Ltd. 0.361852941 Middle 493680000 Small


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32

51 World Merchant Bank Ltd 0.281487805 Middle 295200000 Small

52 Birgunj Finance Ltd 0.104613636 Low 1125937560 Big

53 Capital Mer. Bamk & Fin 0.188235294 Middle 2543389040 Big

54 Everest Finance Ltd, 0.640939597 High 59600000 Small

55 Prudential Bittiya Sans 0.319 Middle 440000000 Small56 Royal Mer. Bank.& Fin 0.221272727 Middle 738399200 Big

57 IME Financial Institution 0.124675186 Low 2342431278 Big

58 Bhajuratna Fin.& Sav. Co. Ltd. 1.540533333 High 57750000 Small

59 Civil Merchant bittya sanstha 0.832133676 High 194500000 Small

60 ICFC Bittya Sanstha Ltd. 0.247327273 Middle 1811610350 Big

61 Chhimek Vikash Bank Ltd. 0.369939024 Middle 101680000 Small

62 Business Dev. Bank Ltd. 0.217830769 Middle 1365000000 Big

63 Siddhartha Vikash Bank Ltd 0.565573123 High 1631850000 Big

64 Sanima Vikash Bank Ltd. 0.169272031 Middle 3006720000 Big

65 Sahayogi Vikas Bank 0.726538462 High 74880000 Small

66 Gurkha Development Bank 0.156973501 Middle 3441600000 Big

67 Swabalamwan Bikash Bank 0.662650602 High 366382667 Small

68 Ace Development Bank Ltd. 0.602040816 High 2690354016 Big

69 Himchuli Bikash Bank Ltd. 0.090653153 Low 1598400000 Big

70 Excel Development Bank 0.681033333 High 60000000 Small

71 BiratLaxmi Development Bank 0.303147139 Middle 367000000 Small

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

S.No Company be/me Value marketcap Size

1 Nabil Bank Ltd. 0.13590604 Low 23023408480 big

2 Nepal Investment Bank Ltd. 0.229787234 Low 16969835745 big

3 Standard Chartered Bank Ltd. 0.099887161 Low 45856277244 big

4 Himalayan Bank Ltd. 0.314362745 Low 9924314400 big

5 Nepal SBI Bank Limited 0.262726046 Low 12297712323 big

6 Everest Bank Ltd 0.161171779 Low 10412766000 big

7 Bank of Kathmandu 0.245535714 Low 9930119640 big

8 Machhachapuchhre Bank Ltd 0.407553191 Middle 3707290440 big

9 Laxmi Bank Limited 0.21445614 Low 6572045280 big

10 Kumari Bank Ltd 0.292735043 Low 5549719032 big

11 Siddhartha Bank Limited 0.280540541 Low 6975817200 big


28/50

33

12 Siddhartha Bank Limited 0.231283784 Low 6975817200 big

13 NMB Bank Ltd. 0.378813559 Middle 4218499705 big

14 DCBL Bank Ltd. 0.434384615 Middle 4319078400 big

15 KIST Bank Limited 0.513869347 Middle 3980000000 big

16 Uniliver Nepal Ltd. 0.338635816 Middle 3819984300 big17 Chilime Hydro power Co. 0.344831971 Middle 8032896000 big

18 National LifeInsu. Co.Ltd. 0.255576132 Low 641520000 big

19 Himalayan Gen.Insu. Co.Ltd. 0.569871795 High 235872000 small

20 Everest Insurance Co. Ltd. 0.508019169 Middle 316912500 small

21 Premier Insurance co. Ltd. 0.88826087 High 164220000 small

22 Sagarmatha Insurance Co.Ltd 0.52073955 Middle 349290942 small

23 Nepal Life Insurance Co. Ltd. 0.124070588 Low 2550000000 big

24 Life Insurance Co. Nepal 0.200293103 Low 1450000000 big

25 Lumbini General Insurance 0.617333333 High 206250000 small

26 Nepal Finance and Saving Co.Ltd. 0.666440678 High 115050000 small

27 NIDC Capital Markets Ltd. 0.388372093 Middle 734713910 big

28 Nepal Share Markets Ltd. 0.921666667 High 751680000 big

29 Kathmandu Finance Limited. 0.968383234 High 152103600 small

30 Peoples Finance Limited. 0.51649789 Middle 700761600 big

31 Citizen Investment Trust 0.483018868 Middle 530000000 big

32 Nepal Aawas Bikas Beeta Co. Ltd. 0.531589147 Middle 432420642 big

33 Gorkha Finance Ltd. 1.286896552 High 200512670 small

34 Universal Finance Ltd. 0.714126984 High 273136374 small

35 Maha Laxmi Finance Ltd. 0.481875 Middle 1209600000 big

36 Goodwill Finance Co. Ltd. 0.566933333 Middle 259816725 small

37 Paschimanchal Finance Co. Ltd 0.892694301 High 126955400 small

38 Lumbini Finance Ltd. 0.95484 High 281250000 small

39 Siddhartha Finance Limited 0.249114286 Low 608525400 big

40 United Finance Ltd 0.46962585 Middle 1030837500 big

41 Shree Investment Finance Co. Ltd 0.363632479 Middle 707616000 big

42 Central Finance Co. Ltd. 0.417515528 Middle 470046262 big

43 Premier Finance Co. Ltd 0.419254237 Middle 290199760 small

44 Nava Durga Finance Co.Ltd. 0.678717949 High 250062930 small

45 Butwal Finance Ltd 0.201551282 Low 644537400 big

46 Standard Finance Ltd. 0.71988764 High 1783211298 big

47 World Merchant Bank Ltd 0.929294118 High 309264000 small


29/50

34

48 Birgunj Finance Ltd 1.216397849 High 781200000 big

49 Prudential Bittiya Sans 0.779722222 High 144000000 small

50 IME Financial Institution 0.223362832 Low 1409450130 big

51 Civil Merchant bittya sanstha 0.659895288 High 190860570 small

52 ICFC Bittya Sanstha Ltd. 0.398272059 Middle 895923664 big53 Nepal Industrial Dev. Corp. 1.213083333 High 357454080 small

54 Chhimek Vikash Bank Ltd. 0.356469388 Middle 151900000 small

55 Sanima Vikash Bank Ltd. 0.228189135 Low 3816960000 big

56 Sahayogi Vikas Bank 0.529923372 Middle 156600000 small

57 Gurkha Development Bank 0.286997519 Low 2127840000 big

58 Swabalamwan Bikash Bank 0.951898734 High 273998860 small

59 Himchuli Bikash Bank Ltd. 0.415809524 Middle 1381174200 big

60 Excel Development Bank 1.240483871 High 248000000 small

61 BiratLaxmi Development Bank 0.490397554 Middle 179850000 small

62 Subbecha Bikas Bank Ltd. 0.55045283 Middle 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. 0.780141844 Medium 1058151279 Big

2 Alliance Insurance Co. Ltd. 1.184067797 high 81401238 Small

3 Alpic Everest Finance Co. Ltd. 0.685180723 Medium 178682400 Small

4 Annapurna Bikas Bank 0.974150943 high 712320000 Big

5 Annapurna Finance Co.Ltd. 0.516912442 low 568713600 Big

6 Api Finance Limtied 1.138734177 high 189600000 Small

7 Arun Valley Hydropower Dev Co Ltd 0.250753425 low 1156801800 Big

8 Asian Life Insurance Company 0.60017341 Medium 622800000 Big

9 Bageshowori Dev.Bank 0.504113924 low 156420000 Small

10 Bank of Asia Nepal Limited 0.550364583 Medium 3840000000 Big

11 Bank of Kathmandu 0.307719298 low 7749039990 Big


30/50

35

12 Birat Laxmi Devt Bank 0.958843537 high 243459636 Small

13 Birgunj Finance Ltd 0.607528736 Medium 730800000 Big

14 Business Development Bank Ltd 0.737163121 Medium 972984600 Big

15 Butwal Finance Ltd 1.078672566 high 253731443 Small

16 Capital Mer. Bamk & Fin 0.747428571 Medium 1309097300 Big17 Central Finance Co. Ltd. 0.426020761 low 485154993 Big

18 Chhimek Vikash Bank Ltd. 0.512528736 low 299497500 Small

19 Chilime Hydro power Co. 0.496788321 low 5997312000 Big

20 Citizen Bank International Ltd 0.496081081 low 4440000000 Big

21 Citizen Investment Trust 0.658444444 Medium 648000000 Big

22 Civil Merchant Bittiya Sanstha Ltd 0.701875 Medium 207848160 Small

23 Clean Energy Devlopment Bank Ltd 0.619161677 Medium 1816960000 Big

24 CRYSTAL FINANCE Ltd. 1.10965812 high 81900000 Small

25 DCBL Bank Ltd. 0.760915033 Medium 2795772672 Big

26 Everest Bank Ltd 0.303464351 low 9085312262 Big

27 Everest Insurance Co. Ltd. 0.51316129 low 313875000 Small

28 Excel Development Bank Ltd. 0.533892857 Medium 224000000 Small

29 Fewa Finance Co. Ltd. 0.456441948 low 728910000 Big

30 Gandaki Bikash Bank Ltd 0.715789474 Medium 342000000 Big

31 Global Bank Ltd 0.493971292 low 3135000000 Big

32 Goodwill Finance Co. Ltd. 0.601878453 Medium 543000000 Big

33 Gorkha Finance Ltd. 1.438411215 high 166608095 Small

34 Gurkha Development Bank 0.466447368 low 1203840000 Big

35 Himalayan Bank Ltd. 0.394417391 low 11500000000 Big

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

37 ICFC Bittiya Sanstha Ltd 0.680787879 Medium 543483105 Big

38 IME Financial Institution 0.481463415 low 1180374912 Big

39 Imperial Financial Institution Ltd 0.973239437 high 89815000 Small

40 Infrastructure Development Bank Ltd 1.059065421 high 856000000 Big

41 International Leasing & Fin. Co. 0.496585366 low 1328400000 Big

42 Kaski Finance Limited 0.677951807 Medium 348600000 Big

43 Kasthamandap Dev Bank Ltd 1.140978261 high 588800000 Big

44 Kathmandu Finance Limited. 0.892253521 high 198306692 Small

45 KIST Bank Ltd 0.673935484 Medium 3100000000 Big

46 Kuber Merchant Bittiya Sanstha Ltd 0.846349206 Medium 189000000 Small


31/50

36

47 Kumari Bank Ltd 0.514022556 low 3627476580 Big

48 Lalitpur Finance Ltd. 0.574474576 Medium 554215615 Big

49 Laxmi Bank Limited 0.35 low 3920167360 Big

50 Lord Buddha Finance Limited 0.863013699 high 243090000 Small

51 Lumbini Bank Ltd. 0.508778281 low 2210000000 Big52 Lumbini Finance Ltd. 1.426029412 high 198900000 Small

53 Lumbini General Insurance 0.96671875 high 160000000 Small

54 Machhachapuchhre Bank Ltd 0.819473684 Medium 2164171478 Big

55 Maha Laxmi Finance Ltd. 0.616222222 Medium 756000000 Big

56 Malika Devt Bank Ltd 0.756928105 Medium 76500000 Small

57 Miteri Development Bank Ltd. 0.905625 high 115568640 Small

58 Nabil Bank Ltd. 0.211661342 low 25400245472 Big

59 National Life Insurance Company Ltd 0.34757485 low 440880000 Big

60 Narayani National Finance Co. Ltd. 0.881933333 high 979145400 Big

61 Nava Durga Finance Co.Ltd. 0.8318 Medium 189856050 Small

62 Neco Insurance Co. 1.294642857 high 129360000 Small

63 Nepal Aawas Bikas Beeta Co. Ltd. 0.570462963 Medium 362078424 Big

64 Nepal Bangladesh Bank Ltd. 0.882230769 high 2418505700 Big

65 Nepal Credit & Com. Bank 0.648023952 Medium 2322786300 Big

66 NDEP Development Bank Ltd. 0.973275862 high 633516600 Big

67 Nepal Doorsanchar Company Limited 0.750190931 Medium 62850000000 Big

68 Nepal Express Finance Ltd 0.669119171 Medium 253910800 Small

69 Nepal Finance and Saving Co.Ltd. 0.963529412 high 208397475 Small

70 Nepal Industrial & Co.Bank 0.258788462 low 5930496000 Big

71 Nepal Insurance Co.Ltd. 0.589046243 Medium 355336464 Big

72 Nepal Investment Bank Ltd. 0.369592233 low 12396404835 Big

73 Nepal Life Insurance Co. Ltd. 0.320565371 low 1698000000 Big

74 Nepal SBI Bank Limited 0.261256637 low 10548898040 Big

75 Nepal Share Markets Ltd. 0.598505747 Medium 751680000 Big

76 Nerude Laghubitta Bikash Bank 2.637368421 high 3895000 Small

77 NIDC Capital Markets Ltd. 0.833693694 Medium 493112616 Big

78 Nilgiri Vikas Bank Ltd 0.92380531 high 56500000 Small

79 Nirdhan Utthan Bank Ltd. 0.819744681 Medium 306602150 Small

80 NMB Bank Ltd 0.618717949 Medium 3900000000 Big

81 Paschimanchal Finance Co. Ltd 0.509490196 low 201286800 Small


32/50

37

82 Pashupati Dev Bank Limited 1.00875 high 728000000 Big

83 Peoples Finance Limited. 0.518954545 low 650496000 Big

84 Pokhara Finance Ltd. 0.685534884 Medium 667244760 Big

85 Prabhu Finance Company Ltd 0.792421053 Medium 775200000 Big

86 Premier Finance Co. Ltd 0.602702703 Medium 267876744 Small87 Premier Insurance co. Ltd. 0.967898089 high 160140000 Small

88 Prime Commercial Bank Limited 0.562882883 Medium 4708620000 Big

89 Prime Life Insurance Company Ltd 0.738070175 Medium 615600000 Big

90 Prudential Finance Company Ltd 0.774492754 Medium 138000000 Small

91 Purwanchal Grameen Bikash Bank 0.136673575 low 579000000 Big

92 Reliable Finance Ltd 0.891478873 high 281160000 Small

93 Resunga Bikas Bank Ltd. 0.893493151 high 89352000 Small

94 Royal Mer. Bank.& Fin 0.754761905 Medium 503653584 Big

95 Sagarmatha Insurance Co.Ltd 0.7034 Medium 387477000 Big

96 Sagarmatha Mer Banking & Finance Ltd 0.870072464 high 362250000 Big

97 Sanima Vikash Bank Ltd. 0.219315895 low 3816960000 Big

98 Sewa Bikas Bank Ltd 1.141121495 high 123050000 Small

99 Shikhar Bittiya Sanstha Ltd 1.343545455 high 110000000 Small

100 Shikhar Insurance Co. Ltd. 0.475031847 low 392500000 Big

101 Shree Investment Finance Co. Ltd 0.346099476 low 442814400 Big

102 Siddhartha Bank Limited 0.453222222 low 4242051000 Big

103 Siddhartha Development Bank Limited 0.87605042 high 767550000 Big

104 Siddhartha Finance Limited 0.773137255 Medium 266526000 Small

105 Siddhartha Insurance Limited 1.101619048 high 105000000 Small

106 Soaltee Hotel Ltd. 0.181138614 low 3014686582 Big

107 Standard Chartered Bank Ltd. 0.133861111 low 28948609800 Big

108 Standard Finance Ltd. 0.770955882 Medium 1362453576 Big

109 Subhechha Bikas Bank Limited 0.893370166 high 209815200 Small

110 Swabalamwan Bikash Bank 1.4425 high 188677760 Small

111 Triveni Bikas Bank Limited 0.863101266 high 207349246 Small

112 Uniliver Nepal Ltd. 0.188640452 low 4401866700 Big

113 Union Finance Co. Ltd. 0.652611111 Medium 314825040 Small

114 United Finance Ltd 0.554271357 Medium 697743750 Big

115 United Insurance Co.(Nepal)Ltd. 0.588 Medium 177000000 Small

116 Universal Finance Ltd. 0.757012195 Medium 248857536 Small


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38

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. 0.780141844 Medium 1058151279 Big

2 Asian Life Insurance Company 0.60017341 Medium 622800000 Big

3 Bank of Asia Nepal Limited 0.550364583 Medium 3840000000 Big

4 Birgunj Finance Ltd 0.607528736 Medium 730800000 Big

5 Business Development Bank Ltd 0.737163121 Medium 972984600 Big

6 Capital Mer. Bamk & Fin 0.747428571 Medium 1309097300 Big

7 Citizen Investment Trust 0.658444444 Medium 648000000 Big

8 Clean Energy Devlopment Bank Ltd 0.619161677 Medium 1816960000 Big

9 DCBL Bank Ltd. 0.760915033 Medium 2795772672 Big

10 Gandaki Bikash Bank Ltd 0.715789474 Medium 342000000 Big

11 Goodwill Finance Co. Ltd. 0.601878453 Medium 543000000 Big

12 ICFC Bittiya Sanstha Ltd 0.680787879 Medium 543483105 Big

13 Kaski Finance Limited 0.677951807 Medium 348600000 Big

14 KIST Bank Ltd 0.673935484 Medium 3100000000 Big

15 Lalitpur Finance Ltd. 0.574474576 Medium 554215615 Big

16 Machhachapuchhre Bank Ltd 0.819473684 Medium 2164171478 Big

17 Maha Laxmi Finance Ltd. 0.616222222 Medium 756000000 Big

18 Nepal Aawas Bikas Beeta Co. Ltd. 0.570462963 Medium 362078424 Big

19 Nepal Credit & Com. Bank 0.648023952 Medium 2322786300 Big

20 Nepal Doorsanchar Company Limited 0.750190931 Medium 62850000000 Big

21 Nepal Insurance Co.Ltd. 0.589046243 Medium 355336464 Big

22 Nepal Share Markets Ltd. 0.598505747 Medium 751680000 Big

23 NIDC Capital Markets Ltd. 0.833693694 Medium 493112616 Big

24 NMB Bank Ltd 0.618717949 Medium 3900000000 Big

25 Pokhara Finance Ltd. 0.685534884 Medium 667244760 Big

26 Prabhu Finance Company Ltd 0.792421053 Medium 775200000 Big

27 Prime Commercial Bank Limited 0.562882883 Medium 4708620000 Big

28 Prime Life Insurance Company Ltd 0.738070175 Medium 615600000 Big

29 Royal Mer. Bank.& Fin 0.754761905 Medium 503653584 Big

30 Sagarmatha Insurance Co.Ltd 0.7034 Medium 387477000 Big

31 Standard Finance Ltd. 0.770955882 Medium 1362453576 Big

32 United Finance Ltd 0.554271357 Medium 697743750 Big

33 Annapurna Finance Co.Ltd. 0.516912442 low 568713600 Big


34/50

39

34 Arun Valley Hydropower Dev Co Ltd 0.250753425 low 1156801800 Big

35 Bank of Kathmandu 0.307719298 low 7749039990 Big

36 Central Finance Co. Ltd. 0.426020761 low 485154993 Big

37 Chilime Hydro power Co. 0.496788321 low 5997312000 Big

38 Citizen Bank International Ltd 0.496081081 low 4440000000 Big39 Everest Bank Ltd 0.303464351 low 9085312262 Big

40 Fewa Finance Co. Ltd. 0.456441948 low 728910000 Big

41 Global Bank Ltd 0.493971292 low 3135000000 Big

42 Gurkha Development Bank 0.466447368 low 1203840000 Big

43 Himalayan Bank Ltd. 0.394417391 low 11500000000 Big

44 IME Financial Institution 0.481463415 low 1180374912 Big

45 International Leasing & Fin. Co. 0.496585366 low 1328400000 Big

46 Kumari Bank Ltd 0.514022556 low 3627476580 Big

47 Laxmi Bank Limited 0.35 low 3920167360 Big

48 Lumbini Bank Ltd. 0.508778281 low 2210000000 Big

49 Nabil Bank Ltd. 0.211661342 low 25400245472 Big

50 National Life Insurance Company Ltd 0.34757485 low 440880000 Big

51 Nepal Industrial & Co.Bank 0.258788462 low 5930496000 Big

52 Nepal Investment Bank Ltd. 0.369592233 low 12396404835 Big

53 Nepal Life Insurance Co. Ltd. 0.320565371 low 1698000000 Big

54 Nepal SBI Bank Limited 0.261256637 low 10548898040 Big

55 Peoples Finance Limited. 0.518954545 low 650496000 Big

56 Purwanchal Grameen Bikash Bank 0.136673575 low 579000000 Big

57 Sanima Vikash Bank Ltd. 0.219315895 low 3816960000 Big

58 Shikhar Insurance Co. Ltd. 0.475031847 low 392500000 Big

59 Shree Investment Finance Co. Ltd 0.346099476 low 442814400 Big

60 Siddhartha Bank Limited 0.453222222 low 4242051000 Big

61 Soaltee Hotel Ltd. 0.181138614 low 3014686582 Big

62 Standard Chartered Bank Ltd. 0.133861111 low 28948609800 Big

63 Uniliver Nepal Ltd. 0.188640452 low 4401866700 Big

64 Annapurna Bikas Bank 0.974150943 high 712320000 Big

65 Infrastructure Development Bank Ltd 1.059065421 high 856000000 Big

66 Kasthamandap Dev Bank Ltd 1.140978261 high 588800000 Big

67 Narayani National Finance Co. Ltd. 0.881933333 high 979145400 Big

68 Nepal Bangladesh Bank Ltd. 0.882230769 high 2418505700 Big


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40

69 NDEP Development Bank Ltd. 0.973275862 high 633516600 Big

70 Pashupati Dev Bank Limited 1.00875 high 728000000 Big

71 Sagarmatha Mer Banking & Finance Ltd 0.870072464 high 362250000 Big

72 Siddhartha Development Bank Limited 0.87605042 high 767550000 Big

73 Alpic Everest Finance Co. Ltd. 0.685180723 Medium 178682400 Small74 Civil Merchant Bittiya Sanstha Ltd 0.701875 Medium 207848160 Small

75 Excel Development Bank Ltd. 0.533892857 Medium 224000000 Small

76 Kuber Merchant Bittiya Sanstha Ltd 0.846349206 Medium 189000000 Small

77 Malika Devt Bank Ltd 0.756928105 Medium 76500000 Small

78 Nava Durga Finance Co.Ltd. 0.8318 Medium 189856050 Small

79 Nepal Express Finance Ltd 0.669119171 Medium 253910800 Small

80 Nirdhan Utthan Bank Ltd. 0.819744681 Medium 306602150 Small

81 Premier Finance Co. Ltd 0.602702703 Medium 267876744 Small

82 Prudential Finance Company Ltd 0.774492754 Medium 138000000 Small

83 Siddhartha Finance Limited 0.773137255 Medium 266526000 Small

84 Union Finance Co. Ltd. 0.652611111 Medium 314825040 Small

85 United Insurance Co.(Nepal)Ltd. 0.588 Medium 177000000 Small

86 Universal Finance Ltd. 0.757012195 Medium 248857536 Small

87 Bageshowori Dev.Bank 0.504113924 low 156420000 Small

88 Chhimek Vikash Bank Ltd. 0.512528736 low 299497500 Small

89 Everest Insurance Co. Ltd. 0.51316129 low 313875000 Small

90 Paschimanchal Finance Co. Ltd 0.509490196 low 201286800 Small

91 Alliance Insurance Co. Ltd. 1.184067797 high 81401238 Small

92 Api Finance Limtied 1.138734177 high 189600000 Small

93 Birat Laxmi Devt Bank 0.958843537 high 243459636 Small

94 Butwal Finance Ltd 1.078672566 high 253731443 Small

95 CRYSTAL FINANCE Ltd. 1.10965812 high 81900000 Small

96 Gorkha Finance Ltd. 1.438411215 high 166608095 Small

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

98 Imperial Financial Institution Ltd 0.973239437 high 89815000 Small

99 Kathmandu Finance Limited. 0.892253521 high 198306692 Small

100 Lord Buddha Finance Limited 0.863013699 high 243090000 Small

101 Lumbini Finance Ltd. 1.426029412 high 198900000 Small

102 Lumbini General Insurance 0.96671875 high 160000000 Small

103 Miteri Development Bank Ltd. 0.905625 high 115568640 Small


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41

104 Neco Insurance Co. 1.294642857 high 129360000 Small

105 Nepal Finance and Saving Co.Ltd. 0.963529412 high 208397475 Small

106 Nerude Laghubitta Bikash Bank 2.637368421 high 3895000 Small

107 Nilgiri Vikas Bank Ltd 0.92380531 high 56500000 Small

108 Premier Insurance co. Ltd. 0.967898089 high 160140000 Small109 Reliable Finance Ltd 0.891478873 high 281160000 Small

110 Resunga Bikas Bank Ltd. 0.893493151 high 89352000 Small

111 Sewa Bikas Bank Ltd 1.141121495 high 123050000 Small

112 Shikhar Bittiya Sanstha Ltd 1.343545455 high 110000000 Small

113 Siddhartha Insurance Limited 1.101619048 high 105000000 Small

114 Subhechha Bikas Bank Limited 0.893370166 high 209815200 Small

115 Swabalamwan Bikash Bank 1.4425 high 188677760 Small

116 Triveni Bikas Bank Limited 0.863101266 high 207349246 Small


37/50

42

APPENDIX B. Annual Report Excrept

APPENDIX C : Stocks selected for constructing different cross sectional portfolio across different fiscal year

2007-2008 portfolio Selected Companies

B/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 Companies

S/M Portfolio a) Navadurga Finance Ltd

b) Premier Finance Ltd

c) Prudential Finance Ltd

S/h Portofolio a) Sagamartha Insurance

b) Citizen Investment Trust

c) Gorkha Finance



b) Nabil Bank Ltd


B/M Portfolioa) Citizen Investment Trustb) Goodwill Finance

c) International Leasing

B/H Portfolio

a) Ace Development Bank

b) Siddhartha Development Bank

c) People's Finance


38/50

43


S/M Portfolio a) Navadurga Finance Ltd

b) Premier Finance Ltdc) Prudential Finance Ltd

S/H Portofolio a) Sagamartha Insurance

b) Excel Development Bank

c) Gorkha Finance


B/L Portfolio a) Kumari Bank Lts

b) Nabil 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/M Portfolio a) Goodwill Finance


c) Sagarmatha Insurance

S/H Portofolio a) Navadurga Finance

b) Excel Development Bank

c) Gorkha Finance



b) Nabil Bank Ltd




39/50

44

B/M Portfolio b) NIDC capital market

c) Nepal Telecom

B/H Portfolio

a) Narayani National Finance


c) Nepal Bangladesh BankS/L Portfolio No Companies

S/M Portfolio a) Navadurga Finance



S/H Portofolio a) Lumbini General Insurance

b) ReliableFinance

c) Gorkha Finance



b) Nabil Bank Ltd


B/M Portfolio


b) NIDC capital market

c) Nepal Telecom

B/H Portfolio

a) Narayani National Finance


c) Nepal Bangladesh Bank

S/L Portfolio a) Everest Insurance

b) Paschimanchal Finance

c) Bageshwori development Bank

S/M Portfolio a) Navadurga Finance



S/H Portofolio a) Lumbini General Insurance

b) ReliableFinance

c) Gorkha Finance


40/50

45


41/50

46

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 SM

BHML

Dashain

Jul

2007-

Aug

0.03

3996

0.97

593 0 0

0.46

4506

0.25

8933

0.00

3483

0.03

0513

0.97

2447

-0.00

348

0.46

1023

0.25

545

0.00

9982

-0.09

55

0.11

2468 0 0

2007-Sept

0.091899

1.690517 0 0 0

0.02381

0.003483

0.088416

1.687034

-0.00348

-0.00348

0.020326

0.153919

-0.58

62

-0.03404 0 0

2007-Oct

0.088255

0.109269 0 0 0

0.067284

0.003483

0.084772

0.105786

-0.00348

-0.00348

0.063801

0.050722

-0.04341

-0.01049 1 0

2007-Nov

0.063996

0.076889 0 0

0.005051

-0.01

250.003483

0.060513

0.073406

-0.00348

0.001567

-0.01598

0.059219

-0.04944

-0.03825 0 0

2007-dec

-0.26

360.302848 0 0

0.011348

0.026284

0.003483

-0.26708

0.299365

-0.00348

0.007864

0.022801

0.117264

-0.00054

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

-0.06879

-0.29

09

-0.09292 0 0

2008-Feb

-0.05715

-0.1527 0 0

0.011642

0.03016

0.003483

-0.06064

-0.15618

-0.00348

0.008158

0.026676

-0.15415

0.083885

0.043656 0 0

2008-Mar

-0.09516

-0.05696 0 0

0.004902

-0.00189

0.003483

-0.09865

-0.06044

-0.00348

0.001419

-0.00537

-0.12586

0.051711

0.046637 0 0

2008-Apr

-0.01168

-0.05678 0 0

0.398551

-0.00753

0.003483

-0.01516

-0.06026

-0.00348

0.395067

-0.01102

0.041189

0.153158

0.002074 0 0

2008-May

0.025733

-0.09

12 0 0 00.003607

0.003483

0.02225

-0.09468

-0.00348

-0.00348

0.000124

0.076295

0.023024

-0.01106 0 0

2008-June

0.076964

0.002019 0 0 0 0

0.003483

0.073481

-0.00146

-0.00348

-0.00348

-0.00348

0.150797

-0.02633

-0.03848 0 0

2008-Jul

0.11131

-0.15021 0 0

0.106764

0.040941

0.003483

0.107827

-0.15

37

-0.00348

0.103281

0.037457

0.031664

0.062203

-0.03518 0 1

2008-Augt

0.159514

0.184608

0.614841 0

0.176418

0.004301

0.004668

0.154846

0.179941

0.610174

0.17175

-0.00037

0.121349

-0.25941

0.229814 0 0

2008-Sept

-0.11119

-0.02788

-0.26907 0

0.132288

0.049094

0.004668

-0.11585

-0.03255

-0.27374

0.12762

0.044426

-0.10492

0.196507

-0.05

44 0 0

2008-Oct

-0.02

10.172926

-0.00146 0

0.006116

0.047752

0.004668

-0.02567

0.168258

-0.00613

0.001448

0.043084

-0.04774

-0.03

220.033644 1 0

2008-Nov

-0.13822

-0.01215

-0.05201 0

0.006614

-0.01331

0.004668

-0.14289

-0.01682

-0.05668

0.001946

-0.01798

-0.14072

0.065231

0.036451 0 0

2008-Dec

-0.02069

-0.01

14

-0.19437 0

0.216941

0.073746

0.004668

-0.02536

-0.01606

-0.19904

0.212273

0.069078

-0.09396

0.172383

-0.04997 0 0

2009-Jan

-0.04717

-0.01562

-0.05481 0

-0.00477

0.014879

0.004668

-0.05184

-0.02029

-0.05947

-0.00944

0.010211

-0.10678

0.042569

0.003621 0 0

2009-Feb

-0.02562

-0.07527

-0.01405 0

0.085264

-0.14522

0.004668

-0.03029

-0.07994

-0.01872

0.080596

-0.14989

0.000955

0.018329

-0.06682 0 0

2009-Mar

-0.00447

-0.02542

0.470565 0 0

0.159076

0.004668

-0.00914

-0.03009

0.465897

-0.00467

0.154408

0.000878

-0.09387

0.317055 0 0

2009-Apr

0.053669

0.039656

-0.33 0

-0.06

0.187512

0.004668

0.049001

0.034988

-0.34

-0.06

0.182844

-0.01

0.122431

-0.10 0 0


42/50

47

832 521 299 988 356 224

2009-May

0.023301

0.057938

-0.02702 0

0.047613

-0.04834

0.004668

0.018634

0.05327

-0.03169

0.042945

-0.05301

-0.00514

-0.01831

-0.04933 0 0

2009-Jun

0.101435

-0.01363

-0.11

35 0

-0.16948

-0.23449

0.004668

0.096767

-0.01

83

-0.11817

-0.17415

-0.23916

0.007103

-0.12609

-0.22471 0 0

2009-jul

0.08001

0.050141

-0.06599 0 0

-0.03197

0.004668

0.075342

0.045473

-0.07065

-0.00467

-0.03664

0.115498

-0.03205

-0.08898 0 1

2009-Aug

0.044525

-0.01649

-0.01961 0

0.050939

0.001255

0.005182

0.039342

-0.02167

-0.02479

0.045756

-0.00393

-0.04143

0.014588

-0.03144 0 0

2009-Sep

-0.22266

-0.02188

-0.05968 0

-0.18384

0.014172

0.005182

-0.22784

-0.02706

-0.06486

-0.18902

0.00899

-0.13271

0.04485

0.088577 0 0

2009-Oct

-0.08355

-0.01041

-0.03846 0

-0.00585

0.005193

0.005182

-0.08873

-0.01559

-0.04365

-0.01103

1.04E-05

-0.03746

0.043921

0.025138 1 0

2009-Nov

-0.06856

0.013146

-0.03391 0

-0.03059

-0.01913

0.005182

-0.07374

0.007963

-0.03909

-0.03577

-0.02431

-0.07509

0.013202

0.007761 0 0

2009-Dec

-0.03557

-0.01

64

-0.04384 0

0.028366

0.043001

0.005182

-0.04076

-0.02159

-0.04902

0.023183

0.037819

-0.03751

0.055726

0.01737 0 0

2010-Jan

-0.07593

-0.02012

-0.01185 0

0.039381

-0.14439

0.005182

-0.08112

-0.02

53

-0.01703

0.034199

-0.14957

-0.03735

0.000964

-0.04015 0 0

2010-Feb

-0.05667

0.017423

-0.04559 0

0.006029

-0.01415

0.005182

-0.06185

0.012241

-0.05077

0.000846

-0.01934

-0.07664

0.025571

-0.00154 0 0

2010-Mar

-0.00

96

-0.00291

-0.00917 0

0.009338

-0.02818

0.005182

-0.01478

-0.00809

-0.01436

0.004155

-0.03337

-0.03

060.000944

-0.01388 0 0

2010-Apr

-0.08817

-0.03813

-0.03107 0

-0.02726

-0.14615

0.005182

-0.09335

-0.04331

-0.03625

-0.03244

-0.15134

-0.07064

-0.00535

-0.04453 0 0

2010-May

0.023773

0.034614

0.012168 0

-0.04121

fundamental analysis of nepalese stock exchange return using fama french three factor model

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