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, Vol. 1, April, 2011

STOCK BETA, FIRM SIZE AND BOOK-TO-MARKET

EFFECTS ON CROSS-SECTION OF COMMON STOCK

RETURNS IN NEPAL Surya Bahadur Rana, M. Phil♠

Abstract

This study examines the cross-sectional variations in common stock returns with respect to stock

beta, firm size and book-to-market equity of 61 sample firms with a total of 455 observations listed in

Nepal stock exchange till mid-April 2010. The study covers firm specific data during the fiscal year

1996/97 through 2008/09. This study basically employs cross-sectional linear regression model along

with empirical CAPM and three-factor model to assess the explanatory power of the firm specific

variables. The results show that firm size does not explain the common stock returns in the context of

stock market in Nepal. On the other hand, study reveals that book-to-market equity and stock beta

effects on common stock returns are consistent across all the analyses and all the specifications of the

model. The results indicate very strong role of stock beta and book-to-market equity to explain

common stock returns in Nepal. The results also indicate that although the stock returns are

significantly explained by stock beta or market risk factor, the underlying assumption of CAMP does

not completely hold in Nepalese stock market as intercept term in empirical CAPM has been observed

to be marginally significant. On the other hand, results support Fama and French (1995) three-factor

model because intercept term in empirical FF three-factor model has been observed to be not

significant, and the evidences establish market risk factor, firm size factor and book-to-market factor

as the most significant determinants of stock returns in Nepal.

1. CONCEPTUAL BACKGROUND AND THEORETICAL FRAMEWORK

Asset pricing theory is concerned with determining how investors choose to

allocate scarce resources among assets. As the underlying theory suggests, the

investors allocate resources into assets based on the ‘object’ and ‘theory’ of choice.

Mean and variance associated with an asset’s returns are the objects of choice. They

indicate the risk-return combination of an investment. On the other hand, theory of

choice guides on selecting the most preferable utility maximizing risk-return

combination of an investment. The basic foundation for asset pricing theory was

laid down by Markowitz (1952) through a seminal work entitled ‘Portfolio

♠ Surya B. Rana is Lecturer at Tribhuvan University, Lumbini Banijya Campus, Butwal. This paper

constitutes a part of his M. Phil. research work.

Stock Beta, Firm Size and Book-to-Market Effects on Cross-Section of Common Stock Returns in Nepal

---- Surya Bahadur Rana

Selection’. Markowitz portfolio theory asserts that a single asset may be very risky

when held in isolation, but not much risky when held in combination with other

assets in a portfolio. This conclusion is based on the idea that the riskiness of a

portfolio is not only determined by variance of assets return, but also by covariance

or correlation of returns between assets held in the portfolio.

The underlying construct of the Markowitz (1952) portfolio theory motivated

Sharpe (1964), Linter (1965), Mossin (1966) and Black (1972) to extend and develop

the assets pricing theory-the capital assets pricing model (CAPM). The underlying

assumption of CAPM is that all investors are price takers and have homogeneous

expectation about asset returns that have a joint normal distribution. In theory,

when all individuals have homogeneous expectations, the market portfolio must

be efficient. Without homogeneous expectations, the market portfolio is not

necessarily efficient and the equilibrium model of capital market does not

necessarily hold. Thus, the efficiency of the market portfolio and the capital asset

pricing model are inseparable, joint hypothesis. It is not possible to test the validity

of one without other.

Given the market efficiency, CAPM postulates that only a component of total risk,

which is related to the market, is relevant for pricing of capital assets. The CAPM

establishes a link between market risk (measured by beta) and return for all assets.

Therefore, the relationship between expected return and market risk is the essence

of the CAPM. It argues that market portfolio is a well diversified portfolio and only

the risk associated with the market portfolio is the systematic risk as measured by

beta. Therefore, if an asset is included in a well diversified portfolio, the asset must

be priced to compensate for systematic risk. The unsystematic risk is uncorrelated

with the market and, therefore, is omitted. Hence, the theoretical foundation of

CAPM reveals that stock beta, a measure of systematic risk, can capture much of

the variations in common stock returns.

The CAPM, however, has not gone unchallenged. The validity of the CAPM is

questioned because it posits a positive linear relation between expected returns

and betas, while other firm specific variables such as firm size and book-to-market

equity should not have any ability to explain average cross-sectional returns. The

key ingredient in the model is the use of beta as a measure of risk. Although, early



studies, for example, Friend and Blume (1970), Black, Jensen, and Scholes (1972),

and Fama and MacBeth (1973), demonstrated beta to have reasonable predictive

power about returns on a portfolio of common stocks, other empirical evidences,

for example, Banz (1981), Stambaugh (1982), Chan, Hamao and Lakonishok (1991),

Fama and French (1992), Davis (1994), and Kothari, Shanken and Sloan (1995),

among others, have raised doubt against validity and applicability of this model.

Many of these studies have concluded that the factors other than beta are

successful in explaining that portion of common stock returns not captured by

beta.

Several anomalies, other than CAPM beta, have become evident when studies have

attempted to explain actual stock returns. For example, size effect of Banz (1981) is

one of them, which has demonstrated that common stocks of firms with small

market capitalization provide higher returns than common stocks of firms with

high capitalization, holding other things constant. Another irregularity is that

common stocks with high book-to-market equity ratio do better than common

stocks with low ratios. For example, Chan, Hamao and Lakonishok (1991) have

documented that book-to-market equity is important in explaining common stock

returns. On the other part of studies, although empirical testing of CAPM by Black,

Jensen and Scholes (1972) has reported a linear empirical market line with positive

risk-return trade-off, the intercept term has been found significantly different from

zero that rejects empirical validity of the CAPM. The study has suggested that the

CAPM is either misspecified and requires the addition of factors other than beta to

explain stock returns or that the problem in measuring beta are systematically

related to variables such as firm size.

Fama and French (1992) published a landmark study on the cross-sectional

relationship between risk and return, as a test of CAPM. The results indicated

noticeable relations between monthly stock returns and both size and beta when

portfolios were arranged by size, but no sure relation when the portfolios were

arranged by beta. Similarly, the study also exhibited no significant relation

between average returns and beta when combined with size. The study

documented that book-to-market equity has the strongest relation with expected

returns. Furthermore, book-to-market equity and market value of equity were

observed to capture the explanatory power of the beta for stock returns. In follow-



up studies, Fama and French (1993; 1995) offered economic justifications for their

findings by showing that book-to-market equity and market value of equity proxy

for stock returns sensitivity to risk factors and postulated that these variables are

also related to earnings. With the inspiration from findings of succeeding studies

by Fama and French (1993; 1995), they proposed a three-factor model that

comprises of market risk factor, size factor and book-to-market equity factor as

opposed to the single factor -the market risk- proposed by the CAPM. The three-

factor model showed that the stocks of small firms and those with a high book-to-

market equity ratio could provide above average returns. This could simply be a

coincidence. But there are also evidences that these factors are related to firm

profitability and therefore may be picking up risk factors that are left out of the

simple CAPM. Since then, there is an ongoing wonder as to which explain the

stock returns better- the CAPM or the three-factor model.

The proposed three-factor model has also generated a number of subsequent

studies with the objective of re-establishing the validity of beta as a measure of risk

and the CAPM as a sound asset pricing model. Basically, the studies have focused

more on the methodological improvements to reduce potential measurement error

in beta. For example, among others, Kim (1995) used a methodology to correct for

measurement bias effects. With this correction, the study revealed that beta has

statistically significant explanatory power, but other variables such as firm size and

book-to-market equity were also significant. Subsequent studies have thus been

able to re-establish the latent value of beta as a significant explanatory variable for

average cross-sectional returns but have not abolished or made clear why other

firm specific variables continue to explain cross-sectional returns. Therefore, the

underlying construct is that if the true beta is known, then the firm specific

variables will not be present in a cross-sectional regression. However, if the CAPM

is valid and if the beta is measured with errors, then it is quite possible to obtain

any one of the two empirical results- either beta is not significant and one or more

of the firm specific variables are; or beta and one or more of the firm-specific

variables are significant.

With respect to the ongoing debate on the role of stock beta, firm size and book-to-

market equity in explaining cross-section of common stock returns, this study is an

attempt to examine these phenomena in the context of Nepalese stock market.



Hence, the basic purpose of this study is to explore the effect size of these firm

specific variables in the context of stock returns in Nepal. This paper is divided

into a total of five sections. The first section dealt with a general background of the

study. The second section provides a brief review of literature along with

statement of the problem. The third section provides a description of data and

methodology used. Results are presented and described in the fourth section and

the final sections presents a concluding remarks of the study along with the

limitations of the study.

2. STATEMENT OF THE PROBLEM

The capital asset pricing model (CAPM) of Sharpe (1964), Linter (1965), Mossin

(1966), and Black (1972) scripts the origin of asset pricing theory. The primary

implication of the CAPM is that the model is mean-variance efficiency. This

implies that there exists a positive linear relation between expected returns and

market betas, and variables other than beta should not have power in explaining

the cross-sectional variations in common stock returns. The main attraction of the

CAPM is that it offers influential and naturally agreeable predictions about how to

measure risk and the relation between expected return and risk. However, the

empirical documentation of the model is poor enough to nullify the way it is used

in application.

The early empirical tests in US stock markets focused on the model’s predictions

about intercept and slope in the relation between expected return and market beta.

Many tests rejected the basic assumption of the CAPM. For example, Friend and

Blume (1970), Black, Jensen, and Scholes (1972), and Stambaugh (1982)

documented positive relation between beta and average stock returns, but it was

too flat. The CAPM also predicts that the intercept term is equal to risk-free rate

and the coefficient on beta is the expected market return in excess of risk-free rate.

On the contrary, the studies such as by Miller and Scholes (1972), Blume and

Friend (1973), Fama and MacBeth (1973), among others, found intercept term

greater than the average risk-free rate, and the coefficient on beta less than the

average excess market returns. However, the issues associated with empirical

validity of CAPM are yet to be tested in the context of stock market in Nepal.



Hence, the present study attempts to test, using more recent data, whether the

central prediction of CAPM holds true in Nepalese stock market.

Contrary to the predictions of the CAPM model, empirical studies have found that

variables relating to firm characteristics have significant explanatory power for

average stock returns, while beta has little power. The most prominent variables

associated with firm characteristics are firm size and book-to-market equity, cash

flow yield and earnings-to-price ratio. Among the several contradictions, earlier

one was Basu’s (1977) evidence that when common stocks were sorted on earnings-

to-price ratios, future returns on high earnings-to-price stocks were observed

higher than that predicted by the CAPM. Similarly, La Porta (1996) demonstrated

low earning growth stocks to have significantly lower standard deviations and

betas than higher earnings growth stocks. On the other hand, in relation to firm

size effect, Banz (1981), Reinganum (1981), and Keim (1983) observed that small

firms have higher returns and larger firms have lower returns than those predicted

by the CAPM. Jagadeesh (1992) also documented no explanatory power of beta in

predicting cross-sectional differences in average returns because when the test

portfolios were constructed the correlations between beta and firm size were found

small.

Finally, Stattman (1980), and Rosenberg, Reid, and Lanstein (1985) demonstrated

high average returns for stocks with high book-to-market equity ratios that were

not captured by their betas. In later period, Chan, Hamao, and Lakonishok (1991)

revealed that book-to-market equity could explain stock returns in Japan. There is

a theme in the contradictions of the CAPM summarized in these studies. Ratios

involving stock prices have information about expected returns missed by market

betas. However, most empirical tests that have found those contradictions to the

CAPM, involve an error-in-variables problem, since true betas are unobservable

and, thus, estimated betas are used as proxy for the unobservable betas. Handa,

Kothari, and Wasley (1989), and Kim (1995) showed that the errors-in-variables

problem could induce an underestimation of price of beta risk and an over

estimation of other cross-sectional regression coefficients associated with firm

characteristics variables such as firm size and book-to-market equity that might be

observed with error. As a mater of fact, a greater correlation between the estimated

betas and firm specific variables causes more downward bias in the price of beta



risk estimate and more exaggeration of the explanatory power of the firm specific

variables. Hence, this study also attempts to identify whether higher correlation

exists between betas and firm specific variables and examine the joint role of beta,

firm size and book-to-market equity in explaining common stock returns in the

context of Nepal.

Fama and French (1992) updated and synthesized the evidence on the empirical

failures of the CAPM. Based on the cross-section regression, the study confirmed

that size, earnings to price, debt-equity and book-to-market ratios could add to the

explanation of expected stock returns provided by market beta. Fama and French

(1996) reached the same conclusion using the time-series regression approach

applied to portfolios of stocks sorted on price ratios. The study also found that

different price ratios did have much the same information about expected returns.

As a result, Fama and French (FF) (1993; 1995; 1996) advocated a three factor model

in which a market portfolio return was attached by a portfolio long in high book-

to-market stocks and short in low book-to-market stocks (HML-high minus low

book-to-market equity) and a portfolio that is long in small firms and short in large

firms (SMB-small minus big size). Since then several studies have used the FF

three-factor model as an empirical asset pricing model.

However, there is controversy over why the firm specific attributes that are used to

form the FF three factors should predict stock returns. Some argue that such

variables may be used to find securities that are systematically mispriced by the

market (for example, Lakonishok, Shleifer, & Vishny, 1994; Daniel & Titman, 1997).

Others argue that these measures are proxies for exposure to underlying economic

risk factors that are rationally priced in the market (for example, Fama and French,

1993; 1995; 1996). A third view is that the observed predictive relations are largely

the result of data snooping and various biases in the data (for example, Kothari,

Shanken, & Sloan, 1995; Chan, Jagadeesh, & Lakonishok, 1995). In similar case,

Berk (1995) emphasized that, because returns are related mechanically to price by a

present value relation, ratios that have price in the denominator are related to

returns by construction. As a matter of fact, if the numerator of such a ratio can

capture cross-sectional variation in the expected cash flows, the ratio is likely to

provide a proxy for the cross-section of expected returns. Ratios like the book-to-

market are therefore likely to be related to the cross-section of stock returns



whether they are related to rationally priced economic risks or to mispricing

effects. Given these prominences of the FF three-factor model, it is interesting to

test its empirical performance as an asset pricing model. Therefore, this study also

attempts to examine whether stock returns are largely associated with three factors

as suggested by FF in the context of small capital market in Nepal. The study

basically deals with following specific issues:

a. Does CAPM explain stock returns in Nepal?

b. Whether CAPM beta alone can predict stock returns, or inclusion of firm size

and book-to-market equity subsume the beta effect on stock returns?

c. Are the stock returns related to three factors suggested by FF three-factor

model?

3. RESEARCH METHODOLOGY

Research design

This study has employed descriptive, correlational and causal comparative

research designs to deal with the fundamental issues associated with factors

affecting common stock returns in the context of stock market in Nepal. The

descriptive research design has been adopted for fact-finding and searching

adequate information about factors affecting common stock returns. This design

has also been employed to describe the nature of cross-sectional common stock

returns of 61 enterprises consisting of 455 observations during fiscal year 1996/97

through 2008/09 by using descriptive statistics with respect to firm specific

variables such as stock beta, firm size and book-to-market equity ratio. This study

is also based on correlational research design. This design has been adopted to

ascertain and understand the directions, magnitudes and forms of observed

relationship between common stock returns and firm specific variables. Moreover,

this study has also employed causal comparative research design to determine the

effect size of stock beta, firm size and book-to-market equity on cross-sectional

common stock returns. The basic purpose of employing causal comparative

research design is to examine whether it is possible to predict common stock

returns on the basis of information about firm specific variables.



Nature and sources of data

This study is based on secondary sources of data. The data for firm specific

variables including stock market data have been obtained from financial

statements of the sample firms recorded in the database of Nepal Stock Exchange

(NEPSE) Limited and Securities Board of Nepal (SEBON) provided in their

respective websites. NEPSE and SEBON have maintained the record of firm

specific data only from the fiscal year 2002/03 to 2008/09 in their respective

database as on mid-April 2010 in websites. Therefore, the firm specific data prior to

2002/03 have been derived from various issues of ‘Financial Statements of Listed

Companies’ published by Nepal Stock Exchange Limited. Similarly, firm specific

data of more recent period (that is for the year 2008/09) are unavailable for most of

the listed firms as these firms have not timely submitted their annual reports.

Overall, the period covered in study with respect to firm specific variables ranges

from fiscal year 1996/97 to 2008/09. The number of observations varies among

enterprises with minimum 2 to maximum 13 observations. Such variations in

number of observations have been noticed mainly due to the unavailability of

continuous years’ data for several firms.

Population and sample

Population of this study includes all listed firms in Nepal Stock Exchange (NEPSE)

Limited to the end of mid-April 2010. Table 1 shows the population and sample of

the study along with their respective number of observations that represents

different sectors as defined by NEPSE. A total of 179 enterprises were listed in

NEPSE as of mid-April 2010 and 61 of them were included in the sample list.

In selecting the most reliable and representative samples, first the population of the

NEPSE was stratified into different sectors as defined by the NEPSE and then

enterprises from each stratum were selected on the basis of availability of market

and firm specific financial information of at least two continuous years from the

fiscal year 1996/97 to 2008/09. There were total 128 enterprises from banking and

finance sector listed in NEPSE to the date. Of these, 84 enterprises did not have

complete firm specific financial information available for the period after 2004/05.

Therefore, these enterprises were excluded from sample and only 44 of them with



more recent years’ observations were included. The number of enterprises from

insurance sector consisted of total 19 insurance companies to the date and 4 of

them with complete financial information to the most recent years were considered

in the sample.

Table 1 Population and sample enterprises from different sectors

S. No. Sector Population Sample Observations

1. Banks and Finance Companies

128 44 342

2. Insurance Companies 19 4 41

3. Manufacturing and Processing

18 3 24

4. Trading 4 2 14

5. Hydropower 4 4 11

6. Hotels 4 3 20

7. Other 2 1 3

Total 179 61 455

Source: www.nepalstock.com

The data problem is more acute for manufacturing and processing sector

enterprises listed in NEPSE. Out of total 18 enterprises from this sector, most have

no regular trading and thus market information about them are not available.

Therefore, only 3 enterprises from manufacturing sector with complete market

information were taken into the sample. Similarly, there were 4 enterprises from

trading sector listed in NEPE till mid-April, 2010. Out of them only two

enterprises, namely Bishal Bazar Company Limited and Salt Trading Corporation

had market information recorded in the NEPSE. Therefore, these enterprises were

also included in the sample. Among the enterprises in hydropower sector, all 4

enterprises had complete market information of more recent years, so all of them

were included in sample. The sample also consists of 3 out of 4 enterprises from

hotels and 1 out of 2 enterprises from other sector defined by NEPSE. The selection

of these enterprises was also based on the availability of complete firm specific and

market information.

The Nepalese stock market is dominated by deep and broad market of banks and

finance companies and the updated financial statements of many of these sectors’



firms are available in the NEPSE and SEBON database. However, financial

information relating to manufacturing and processing, trading, hotels, and other

sectors’ enterprises are relatively of older date and the number of firm years are

relatively fewer. Therefore, sample list is basically dominated by banks and finance

sector’s enterprises both in terms of number of firms and number of observations.

Methods of data analysis

The main purpose of data analysis in this study is to explore the predictive power

of firm specific variables in explaining common stock returns for selected

enterprises in the context of stock market in Nepal. The method of data analysis

used in this study consists of econometric models that include cross-sectional

regression models, empirical version of CAPM model and FF three-factor model.

The study has also used descriptive statistics, correlation analysis, two-way short

of portfolios along with statistical test of significance such as t-test, F-test, Adjusted

R2, test of autocorrelation and multicolinearity.

In order to explain the effect size of firm specific explanatory variables on cross-

section of common stock returns, the empirical regression model (Davis, 1994) of

the form specified in equation (1) has been used.

Rit = α +b1t βit + b2t LMEit + b3t BE/MEit + et . . . (1)

In equation (1) Rit refers to the returns on common stock of firm ‘i’ for period ‘t’, βit

is the stock beta of firm ‘i’ for period ‘t’, LMEit is the natural logarithm of market

value of equity, BE/MEit denotes the ratio of book-to-market equity, and ‘eit’ refers

to the unexplained residual error terms. α is the intercept term, and b1t, b2t, and b3t,

are the respective parameters of the explanatory variables to be estimated. The

cross-sectional variations in stock returns associated with stock beta, firm size,

book-to-market equity ratio, and earnings-to-price ratio have been examined by

using several specifications of equation (1).

The equation (1) specified above assumes the following reasonable a priori

hypothesis:

δδδδRit

δδδδ βit > 0;

δδδδRit

δδδδ LMEit < 0; and

δδδδRit

δδδδ BE/MEit > 0 . . . (2)



The priori sign expectation in equation (2) implies that the stock returns are

positively related with stock beta, book-to-market equity and earnings-to-price

ratios and negatively related with firm size.

The next section of regression analysis tests the comparative performance of the

CAPM and FF three-factor model in the context of Nepalese stock market. For the

purpose of testing empirical validity of the CAPM, the empirical model of Sharpe

(1964), Linter (1965) and Black (1972) version has been used to explain the cross-

section of common stock returns with respect to the market risk premium. The

empirical model is specified in equation (3).

Rit – RFt = ααααi + bi [RMt – RFt] + eit . . . (3)

In equation (3), Rit is the returns on common stock of firm ‘i’ for period ‘t’, RFt is

the risk-free rate of return during period ‘t’, RMt is the rate of return on market

portfolio, and eit denotes the unexplained residual error terms.


hypothesis:

δδδδ (Rit – RFt)

δδδδ (RMt – RFt) > 0 . . . (4)

The priori sign expectation in equation (4) implies that excess stock returns are

positively related with excess market returns.

In an attempt to analyze the predictive power of market risk factor, size factor and

book-to-market equity factor, and to compare with the CAPM, Fama and French

(1995) three-factor model of the following form has been used.

Rit – RFt = ααααi + bi[RMt – RFt] + si(SMBt) + hi(HMLt) + eit . . . (5)

In equation (5), [Rit - RFt] is the excess of stock returns of firm i over risk-free rate

for period t, and [RMt - RFt] is the excess of market return over risk-free rate for

period t. For each year, the stocks were sorted into two size groups- small and big,

and three book-to-market equity group- high, medium, and low. SMBt refers to the

size factor determined as average of small firm size minus average of big firm size

portfolio returns during each year. Similarly, HMLt is the book-to-market equity



factor defined as average of high BE/ME minus average of low BE/ME portfolio

returns during each year. eit is the unexplained residual terms, αi is the intercept

term, and bi, si, and hi are the coefficients of market risk factor, size factor, and

book-to-market equity factor respectively.


hypothesis:


δδδδ (RMt – RFt) > 0;


δδδδ (SMBt) < 0; and


δδδδ (HMLt) > 0 . . . (6)

The priori sign expectation in equation (6) implies that excess stock returns are

positively related with excess market returns and book-to-market premium

denoted by high minus low book-to-market, and negatively related with size

premium denoted by small minus big size stocks.

4. STUDY RESULTS

Descriptive statistics

Descriptive statistics have been used to describe the characteristics of stock returns

and firm specific variables during the study period. The descriptive statistics used

in this study consists of mean, standard deviation, and minimum and maximum

values associated with variables under consideration. Table 2 summarizes the

descriptive statistics of firm specific variables used in this study during the period

1996/97 through 2008/09 associated with 61 sample firms listed in NEPSE.

Market capitalization of equity of the sample firms ranges from minimum Rs 6.75

million to maximum Rs 72,227.675 with an average of Rs 2,062.7639 million and

standard deviation of Rs 6,036.59 million. The wider range of market capitalization

of equity implies that the firm included in the sample varies in terms of their size.

Table 2 also reveals that net worth position of the firms varies significantly. It

ranges from minimum negative Rs 71.4 million to maximum positive Rs 3,521.64

million with a mean value and standard deviation of Rs 359.1576 million and Rs

551.2338 million respectively. The firms also differ in terms of their earnings per

share and market price per share. Earning per share has average value of Rs 35.14,

while market price per share has an average value of Rs 587.4418 and standard



deviation of Rs 867.5687. Relatively larger difference in market price implies that

sample firms consist of low to high growth stocks.

Table 2 Descriptive Statistics of Firm Specific Variables associated with 61 Sample

Firms during the Period 1996/97 through 2008/09 This table shows descriptive statistics- mean, standard deviation, minimum and maximum values- of

firm specific variables associated with 61 sample firms listed in the population of NEPSE till mid-

April 2010 with 455 observations for the period 1996/97 through 2008/09. ME refers to market value

of equity defined as number of outstanding shares multiplied by corresponding market price per

share, BE is the net worth, EPS is the earnings per share, MPS is the market price per share of

common stock, BE/ME is the ratio of book value of equity to market value of equity, LME is the

natural logarithm of market value of equity used as a proxy for firm size, β is common stock beta used

as a proxy of systematic risk, Ri is the annual return on common stock, RM - RF refers to the excess of

market return above risk-free rate, Ri – RF refers to the excess of stock returns above risk-free rate, and

N refers to the number of observations.

Variables N Mean Std. Dev. Minimum Maximum

ME (Rs in Million) 455 2062.7639 6036.5900 6.7500 72227.6750

BE (Rs in Million) 455 359.1576 551.2338 -71.4000 3521.6400

EPS (In Rs) 455 35.1336 37.0411 -79.0500 285.7200

MPS (In Rs) 455 587.4418 867.5687 35.0000 7750.0000

BE/ME 455 0.6820 0.5623 -0.3245 4.7189

LME 455 2.4484 0.8529 0.8293 4.8587

β 455 0.9121 2.2643 -19.3500 12.4900

Ri 455 0.3129 0.7085 -0.7327 4.6198

RM – RF 455 0.2015 0.3313 -0.3941 0.7439

Ri – RF 455 0.2763 0.7095 -0.7798 4.5776

Similarly, book-to-market equity ratio has mean value of 0.6820 and standard

deviation of 0.5623 with minimum to maximum range of negative 0.3245 to

positive 4.7189. Table 2 also indicates that firms differ significantly in terms of their

systematic risk level proxied by stock beta. The stock beta has minimum value of

negative 19.35 to maximum positive 12.49 with a mean of 0.9121. The average stock

return of the sample firms during the period has been recorded at 0.3129 with a

minimum negative return of 0.7327 to maximum positive return of 4.6198. The

range of minimum to maximum excess stock return is wider than that of excess

market return. This implies that average stock returns of the sample firms are more

volatile than the market returns.



Correlation analysis

The firm specific variables used in this study, particularly, book-to-market equity

ratio, firms size, stock beta, and stock returns are all scaled version of market price

per share or market value of equity. Therefore, it is reasonable to expect some kind

of statistically significant relationship among these pairs of variables. This section

therefore is devoted to explaining the direction and magnitude of relationship

among different pairs of these firm specific variables including stock returns. The

correlation analysis has been performed for this purpose. Table 3 presents the

value of bivariate Pearson correlation coefficient between different pairs of firm

specific variables of 61 sample firms with 455 observations during the period

1996/97 through 2008/09.

Table 3 Bivariate Pearson Correlation Coefficients of Firm Specific Variables Observed

for 61 Sample Firms during the Period 1996/97 through 2008/09 This table reveals the bivariate Pearson correlation coefficients between different pairs of firm specific

variables. Ri, β, BE/ME and LME are as defined in the Table 2. The correlation coefficients are based

on the data on Ri, β, BE/ME and LME from 61 sample firms listed in NEPSE till mid-April 2010

with 455 observations for the period 1996/97 through 2008/09. ‘*’ sign indicates that correlation is

significant at 1 percent level.

Ri β BE/ME LME

Ri 1.000

β 0.526* 1.000

BE/ME -0.299* -0.214* 1.000

LME 0.210* 0.201* -0.638* 1.000

As Table 3 reports, common stock returns are positively related to stock beta and

firm size and the relationships are significant at 1 percent level. On the other hand,

stocks returns are significantly negatively related to book-to-market equity ratio.

From among given set of firm specific variables, the stock beta reveals stronger

positive relation with stock returns than other. Similarly, there exists high negative

correlation between firm size and book-to-market equity. Gujarati (1995) states that

high correlations (in excess of 0.8) are a sufficient but not necessary condition for

the existence of multicolinearity because it can exist even though the correlations

are comparatively low (less than 0.5). However, low correlations being observed

among different pairs of explanatory variables in Table 3 gives sufficient evidence



to believe that the problem of multicolinearity may not exist in the analysis. This

has also been confirmed by employing variance inflationary factor (VIF) to test the

problem of multicolinearity.

Properties of portfolios formed on two-way sorts

This section examines the properties of stock returns with respect to firm specific

variables. Five equal percentiles portfolios were formed based on bivariate sorts of

firm size and BE/ME, firm size and stock beta, and BE/ME and stock beta. The

characteristics of average returns associated with each of these bivariate sorts of

portfolios are described below.

Average returns, firm size, and book-to-market equity

Table 4 reports two dimensional variations in average returns that results when

five firm size portfolios are each subdivided into five portfolios based on BE/ME

for individual stocks. Within a firm size portfolio (across a row in Table 4), average

returns decrease strongly with BE/ME. On average, the returns on the lowest and

highest BE/ME portfolios in a size group differ by 36.03 percent (that is, 52.7

percent minus 16.67 percent). Similarly, from top to down in a column of average

return shows that there is a positive relation between average return and firm size.

On average, the spread of returns across the lowest and highest size portfolios in a

BE/ME group is 19.72 percent (that is, 39.58 percent minus 19.86 percent).

The results indicate that controlling for firm size, book-to-market equity captures

strong variation in average returns. Similarly, controlling for book-to-market

equity, firm size also can capture significant variation in average returns although

the average returns from portfolio 4 to 5 have been declined. However, the

direction of movement in average returns with respect to these two variables are

opposite of those documented in Fama and French (1992). As reported in Table 3,

the correlation between the cross-section of firm size and BE/ME for individual

stocks is -0.638. The negative correlation is also apparent in the average values of

firm size and BE/ME for the portfolios sorted in Table 4. Thus, firm with low

market equity are likely to have poor prospects, resulting in a low average return

and high book-to-market equity. Conversely, larger stocks are more likely to have

good prospects with higher average returns and lower book-to-market equity.



Table 4 Average Returns on Portfolios Formed on Firm Size and Book-to-Market Equity

of 61 Sample Firms during the Period 1996/97 through 2008/09 This table shows the average returns of the five portfolios formed on firm size and book-to-market

equity. Stocks sorted by firm size are shown in the order of low to high in the portfolio 1 to 5 from top

to down and stocks sorted by BE/ME are shown in the order of low to high in the portfolio 1 to 5

across left to right. Firm size is measured by natural logarithm of market value of equity and BE/ME

is the ratio of book value of equity to market value of equity.

BE/ME

Firm Size All Low - 1 2 3 4 High - 5

All 0.3136 0.5270 0.4131 0.2431 0.2183 0.1667

Low - 1 0.1986 0.4296 0.2850 0.1222 0.0822 0.0741

2 0.2405 0.4718 0.3087 0.1626 0.1618 0.0977

3 0.2849 0.5241 0.3558 0.2071 0.2017 0.1357

4 0.4484 0.5792 0.5837 0.4153 0.3646 0.2991

High - 5 0.3958 0.6301 0.5322 0.3083 0.2814 0.2268

Average returns, firm size, and stock beta

Table 5 reveals two-way variations in average returns that results when five firm

size portfolios are each subdivided into five portfolios based on stock beta for

individual stocks. There is a positive relation between average returns and stock

beta within a size group. On average, the returns on the lowest and highest stock

beta portfolios in a size group differ by 40.38 percent (that is, 58.97 percent minus

18.59 percent). Similarly, there is also a general pattern of positive relation between

average return and firm size within a beta group. On average, the difference of

returns across the size portfolios in a beta group is 17.20 percent (that is, 37.74

percent minus 20.54 percent).

The results indicate that controlling for firm size, stock beta can capture significant

variation in stock returns as average returns move into the same direction with

beta in a size group. Similarly, controlling for stock beta, firm size also can capture

much of the variation in average returns. Specifically it has been observed that

there is an increase in average returns from portfolio 1 to 4 in a beta group, and

then decrease in size portfolio from the portfolio 4 to 5. The correlation between

firm size and stock beta for individual stocks was observed to be 0.201 and

significant at 1 percent level as indicated in Table 3 in the earlier section. This

positive relation has been also revealed by the average values of firm size and



stock beta in the portfolios sorted in Table 5. This implies that smaller size stocks

are likely to have poor prospects with low average returns and low beta, and larger

stocks are more likely to have good prospects of having higher average returns

with higher beta.

Table 5 Average Returns on Portfolios Formed on Firm Size and Stock Beta of 61 Sample

Firms during the Period 1996/97 through 2008/09 This table shows the average returns of the five portfolios formed on firm size and stock beta. Stocks

sorted by firm size are shown in the order of low to high in the portfolio 1 to 5 from top to down and

stocks sorted by stock beta are shown in the order of low to high in the portfolio 1 to 5 across left to

right.

Stock Beta

Firm Size

All Low - 1 2 3 4 High - 5

All 0.3124 0.1859 0.1973 0.2619 0.3273 0.5897

Low - 1 0.2054 0.0758 0.0775 0.1401 0.2190 0.5144

2 0.2455 0.1168 0.1273 0.1925 0.2551 0.5356

3 0.2872 0.1599 0.1686 0.2197 0.3031 0.5847

4 0.4466 0.3321 0.3501 0.4161 0.4918 0.6431

High - 5 0.3774 0.2447 0.2628 0.3411 0.3677 0.6709

Average returns, book-to-market equity, and stock beta

The characteristics of average returns with respect to two-way sort of portfolios

based on book-to-market equity and then stock beta are reported in Table 6. The

portfolios were first sorted into five equal BE/ME group and then each BE/ME

portfolios were subdivided into five beta group portfolios. The results show that

average returns increase with beta in a BE/ME group (across the row), and

decrease with BE/ME within a beta group (across the column). On average, there

is 40.79 percent difference in average returns of high and low beta portfolio in a

BE/ME group, whereas the spread of low and high BE/ME portfolio in a beta

group is only 35.45 percent.

The results indicate that, controlling for book-to-market equity, beta can capture

strong variations in average returns. Similarly, controlling for stock beta, book-to-

market equity also can capture significant variation in average returns but in

opposite direction to that documented in Fama and French (1992). The observed



correlation between BE/ME and stock beta was -0.214 and was significant at 1

percent level as reported in Table 3. This negative relation between BE/ME and

stock beta has also been established in the BE/ME and beta sorted portfolios in

Table 6. The results suggest that lower book-to-market stocks are likely to have

higher average returns with higher beta, and higher book-to-market stocks are

more likely to have lower average returns with lower beta.

Table 6 Average Returns on Portfolios Formed on Book-to-Market Equity and Stock Beta

of 61 Sample Firms during the Period 1996/97 through 2008/09 This table shows the average returns of the five portfolios formed on book-to-market equity and stock

beta. Stocks sorted by book-to-market equity are shown in the order of low to high in the portfolio 1 to

5 from top to down and stocks sorted by stock beta are shown in the order of low to high in portfolio 1

to 5 across left to right.

Stock Beta

BE/ME

All Low - 1 2 3 4 High - 5

All 0.3122 0.1835 0.1946 0.2624 0.3289 0.5914

Low - 1

0.5217 0.4160 0.4343 0.5196 0.5425 0.6961

2 0.4112 0.2850 0.3072 0.3762 0.4482 0.6395

3 0.2427 0.0950 0.1126 0.1767 0.2340 0.5952

4 0.2180 0.0897 0.0869 0.1428 0.2382 0.5322

High - 5

0.1672 0.0319 0.0320 0.0966 0.1816 0.4939

Cross-sectional regression analysis

In order to test the statistical significance and robustness of the results, this study

also relies on cross-sectional regression model specified in equation (1). It basically

deals with regression results from various specifications of the model to examine

the estimated relationship of common stock returns with firm specific variables for

cross-sectional data of 61 sample firms that include 455 observations during the

period 1996/97 through 2008/09. The regression results are reported in Table 7.

The simple regression result of stock returns on beta in model specification I shows

a positive relationship of stock returns with stock beta. The slope coefficient of

stock beta is significant at 1 percent level which implies that stock returns increase

with stock beta. However, the result also indicates that the intercept term is



significantly different from zero. The empirical validity of the CAPM lies on the

notion that stock returns should have significant positive linear relation with stock

beta and the intercept term should not be statistically significant. The statistical

significance of the intercept term in this study raises a doubt on empirical validity

of the CAPM in the context Nepalese stock market. This result is consistent with

Black, Jensen, and Scholes (1972) where the study reported a linear empirical

market line with positive trade-off between return and market risk denoted by

beta. However the intercept term in the study was also found statistically different

from zero that rejected empirical validity of the CAPM.

Table 7 Estimated Relationship from Cross-Sectional Regression of Stock Returns on Beta, Firm Size, Book-to-Market Equity Ratio, and Earnings-to-Price Ratio for 61 Sample Firms with 455 Observations during the Period 1996/97 through 2008/09 Model 1: Rit = α +b1t βit + b2t LMEiit + b3t BE/MEit + eit

This table shows regression results of stock returns on four firm specific variables based on pooled

cross-sectional data of 61 firms listed in NEPSE with 455 observations from the year 1996/97 to

2008/09. The regression results consist of various specifications of the model 1 in the form of simple

and multiple regressions. The reported values are intercepts and slope coefficients of respective

explanatory variables with t-statistics in the parentheses. Dependent variable is the stock return

denoted as Rit, and independent variables are stock beta (βit), firm size (LMEit), and book-to-market

equity ratio (BE/MEit). The reported results also include the values of F-statistics (F) and adjusted

coefficient of determination (Adj. R2). The single asterisk (*) sign indicates that result is significant at

1 percent level.

Dependent Variable: Stock Returns

Model Intercept β LME BE/ME F Adj. R2

I 0.163

(5.339*)

0.165

(13.170*) 173.440* 0.275

II -0.114

(-1.150)

0.174

(4.567*) 20.862* 0.042

III 0.570

(11.419*)

-0.377

(-6.670*) 44.493* 0.087

IV -0.052

(-0.608)

0.158

(12.452*)

0.090

(2.683*) 91.504* 0.285

V 0.343

(7.247*)

0.152

(12.137*)

-0.246

(-4.893*) 103.083* 0.310

VI 0.487

(3.073*)

0.027

(0.549)

-0.351

(-4.781*) 22.363* 0.086

VII 0.394

(2.855*)

0.152

(12.116*)

-0.017

(-0.399)

-0.262

(-4.076*) 68.647* 0.309



Similarly, the regression result of stock returns on firm size in model specification

II shows a positive relationship between stock returns and firm size and the

regression coefficient of firm size is statistically significant at 1 percent level. In

another simple regression result of specification III, common stock returns are

observed to be negatively related with book-to-market equity and coefficient is

again significant at 1 percent level. However, the result indicates that only 8.7

percent variations in common stock returns are captured by book-to-market

equity. In all simple regressions, except specification I, despite of statistical

significance of F-value, the firm specific variables such as firm size, and book-to-

market equity ratio individually explains small variations in common stock returns

as indicated by adjusted R2 in the respective model specifications.

In specification VII, three variables, namely stock beta, firm size, and book-to-

market equity, have been used as explanatory variables. The results show that

stock returns have significant positive relation with stock beta and significant

negative relation with book-to-market equity. However, a surprising result has

been obtained in relation to firm size that its observed direction of relation has

been reversed although the size coefficient is not statistically significant. This study

hypothesized that common stock returns are positively related to stock beta and

book-to-market equity and negatively related to firm size. Thus, the observed

relationship of common stock returns with stock beta is according to priori sign

expectation although the priori sign expectations do not hold with other firm

specific variables.

In cross-sectional regression, data are often collected on the basis of a probability

sample of cross-sectional firms so that there is no prior reason to believe that the

error term pertaining to one firm is correlated with the error tem of another firm. If

by chance such a correlation is observed in cross-sectional firms, it is called spatial

autocorrelation, that is, correlation in space rather than over time. However, it is

important in cross-sectional analysis that the ordering of the data must have some

logic, or economic interest, to make sense of any determination of whether spatial

autocorrelation is present or not. In this study, cross-sectional data have the

ordering over time so that there is a need to detect the problem of autocorrelation,

and it has been confirmed by using Durbin-Watson (DW) d-statistic. Similarly, in a

multiple regression analysis, the problem of multicolinearity is more prominent.



Therefore, the diagnostic check of the model has been conducted using variance

inflationary factor (VIF) of explanatory variables to detect the multicolinearity

problem, if any, associated with multiple regressions of specification IV through

VII. As argued by Durbin and Watson (1951), if computed DW is less than lower

bound critical value (dL), there is enough evidence to believe that the problem of

positive autocorrelation exists. If it lies between dU to 4-dU, there is no evidence of

autocorrelation. However if computed DW falls in between of lower and upper

bound critical value, the result is inconclusive as to whether the problem of

autocorrelation exists or not. Analyses show that computed DW for all the model

specifications falls in between dU to 4-dU so that there is no evidence of

autocorrelation. With regard to multicolinearity, the analyses also show that

variance inflationary factors (VIF) of explanatory variables across all the model

specifications are significantly lower than 10. Therefore, there is also no evidence of

multicolinearity in the regression model.

The CAPM and the three-factor model

The empirical tests of CAPM (for example Friend and Blume, 1970; Black, Jensen &

Scholes, 1972; Fama & Macbeth, 1973, among others) have asserted that stocks can

earn higher returns if they have a high beta. There is a general agreement that if the

CAPM is a better model of the reward-risk trade-off for securities, beta should be a

better measure of risk. However, the studies (for example, Rosenberg, Reid, &

Lanstein, 1985; Fama & French, 1992; 1993; 1995; Lakonishok, Shleifer, & Vishny,

1994; Kothari, Shanken, & Sloan, 1995; Daniel, Titman, & Wei, 2001, among others)

have also shown that the single risk factor is not quite enough for describing cross-

sectional variations in common stock returns. The current consensus is that firm

size and book-to-market equity factors are pervasive risk factors besides the overall

market risk factor. Therefore, this section attempts to explore the economic

performance of the CAPM versus the three-factor model in explaining cross-

section of common stock returns.

In order to test the performance of CAPM versus three-factor model, this study

relies on regression analysis of the empirical CAPM and three-factor model. The

regression results have been reported in Table 8. The CAPM model reports the

simple regression results, where excess stock returns have been regressed on



excess market returns, and FF model reports the multiple regression results where

excess stock returns have been regressed on market risk factor, size factor, and

book-to-market equity factor. At first it is necessary to note that empirical validity

of the CAPM will be established if slope coefficient of market risk factor is

significantly positive and intercept term is not statistically different from zero. The

results report the intercept value of 0.059 with t-statistic of 1.762. The intercept

term is marginally significant at 10 percent level. It, thus, implies that intercept is

statistically different from zero and rejects the second condition of empirical

CAPM. On the other hand, the same results report regression slope coefficient of

1.077 with t-statistic of 12.376. The coefficient is statistically significant at 1 percent

level. The results indicate that stock returns are significantly positively related to

market risk factor. But the statistical significance of the market risk factor

coefficient satisfies only first condition of empirical CAPM. Therefore, the

regression results for the CAPM shown in Table 8 are not consistent with the

expectations.

Table 8 Estimated Relationship from Regression of Excess Stock Returns on Market Risk Factors, Firm Size Factors, and Book-to-Market Equity Factors for 61

Sample Firms with 455 Observations during the Period 1996/97 through 2008/09 CAPM Model: Rit – RFt = αi + bi [RMt – RFt] + eit

FF Three-Factor Model: Rit – RFt = αi + bi[RMt – RFt] + si(SMBt) + hi(HMLt) + eit

This table presents comparative regression results of the CAPM and Fama-French three-factor model.

The dependent variable is the excess of stock returns over risk-free rate and denoted by ‘Rit – RFt’. The

independent variables are market risk factors [RMt – RFt], size factors (SMBt) measured as small

minus big size portfolio, and book-to-market factors (HMLt) measured as high minus low book-to-

market equity portfolio. The sample includes 61 firms listed in NEPSE till mid-April 2010 with 455

observations from the year 1996/97 to 2008/09. ‘b’ denotes to the coefficient of market risk factor, ‘s’

refers to the coefficient of size factor, and ‘h’ is the coefficient of book-to-market equity factor. The

reported values are intercepts and slope coefficients, and figures in the parentheses are t-statistics.

Also reported are the F-statistics, and adjusted coefficient of determination (Adj. R2). ‘*’, ‘**’, and

‘***’ indicate that results are significant at 1, 5, and 10 percent levels respectively.

Dependent Variable: Excess Stock Returns Model

Intercept b s h F Adj. R2

CAPM 0.059

(1.762***)

1.077

(12.376*) 153.159* 0.251

FF -0.198

(-0.583)

1.085

(12.119*)

0.672

(2.587**)

-0.636

(-6.480*) 73.087* 0.323



Table 8 also shows the regression results of FF three-factor model. The coefficient

of market risk factor is 1.085 with a t-value of 12.119. The effect size of the market

risk factor has been slightly improved with the coefficient significant at 1 percent

level. Besides the coefficient of market risk factor, other coefficients are also

statistically significant. The firm size factor coefficient is 0.672 with a t-value of

2.587 which is significant at 5 percent level. Similarly, coefficient of book-to-market

equity factor is -0.636 with t-value of -6.480, which is significant at 1 percent level.

The results exhibit an increase in explanatory power of three-factor model as

compared to CAPM results because adjusted R2 has been increased to 0.323 with

significant F-value and intercept term not statistically different from zero. The

results also reveal that FF three-factor model is able to explain the book-to-market

and firm size effects. It has documented a negative HML coefficient which means

that high book-to-market stocks have lower expected returns than low book-to-

market stocks. However, the direction of book-to-market effect contradicts with

Drew and Veeraraghavan (2003), and Gaunt (2004), among other, who observed

positive HML coefficient. Hence, the results in this study suggest that low book-to

market firms are more risky than their high book-to-market counterparts thereby

attracting a risk premium. The results also show a positive SMB coefficient

implying that larger stocks have higher expected returns than smaller stocks. But,

again, the direction of firm size effect contradicts with Halliwell, Heaney, and

Sawicki (1999), Drew and Veeraraghavan (2003) and Gaunt (2004), among others,

who reported a negative SMB coefficient. On the whole, the applicability of the FF

three-factor model in explaining Nepalese stock returns has to be up-weighted due

to significant s and h coefficient along with the significant coefficient for market

risk factor (b).

5. CONCLUSION

The study revealed an important picture in relation to the significance of market

risk factor, and firm specific variables in predicting stock returns. Rationality-based

asset-pricing models assert that the cross-section of stock returns can be explained

by betas or factor loadings on a set of common factors (Chou, Chou & Wang, 2004).

Early evidence in the 1970s largely supported the Sharpe-Linter-Black (SLB) capital

asset pricing model and the efficient market hypothesis. The seminal work of Fama



and French (1992), however, identified market value of equity and the ratio of

book-to-market equity as the two major determinants of the cross-sectional

expected returns, and sentenced the death of beta. The major conclusion of this

study is that the firm size does not explain the common stock returns in the context

of stock market in Nepal. The results show the inconsistent relationship of firm

size with common stock returns, and hence its effects are not conclusive. On the

other hand, book-to-market equity and stock beta effects on common stock returns

are consistent across all the analyses and all the specifications of the model. The

results indicate very strong role of stock beta and book-to-market equity to explain

common stock returns in Nepal. Stock beta has consistently significant explanatory

power in all the models indicating that stocks with higher beta have higher returns.

Similarly, book-to-market equity also has consistent significant negative relation

with stock returns in all cases. The results associated with positive and significant

relationship between stock returns and beta do not support the findings of some

earlier studies such as by Banz (1981), Stambaugh (1982), Fama and French (1992)

and others. So far the results in this study are concerned, although the stock

returns are significantly explained by stock beta or market risk factor, the

underlying assumption of CAMP does not completely hold in Nepalese stock

market as intercept term in empirical CAPM has been observed to be marginally

significant. On the other hand, results support Fama and French (1995) three-factor

model because intercept term in empirical FF three-factor model has been observed

to be not significant, and the evidences establish market risk factor, firm size factor

and book-to-market factor as the most significant determinants of stock returns in

Nepal. However, the direction of relationship of size and book-to-market equity

factors with excess stock return contradicts from priori hypothesis.

The conclusions derived from this study, however, deserve some considerations

from the methodological aspects and thus can not be generalized. First, this study

used annual closing price of shares of common stock to provide an estimate of

stock returns and annual closing NEPSE index to estimate market return. Annual

closing prices and stock indexes are suffered from high deviations and thus inflate

the annual returns. Therefore, future studies should be directed towards

computing returns from daily or weekly or monthly observations of closing prices.

Second, this study has assumed linear relationship between stock returns and

explanatory variables. In emerging markets, it is expected that there exists non-



linearity. Moreover, emerging markets are characterized by less frequent

transactions termed as thin trading. In order to incorporate these issues, the future

studies are suggested to apply non-linear models to test the predictive power of

explanatory variables. Third, this study used few firm specific variables to assess

the cross-sectional variations in stock returns. Inclusion of some other variables, for

example cash flow to price (Chan, Hamao, & Lakonishok, 1991) leverage (Fama &

French, 1992), annual sales growth (Davis, 1994), sales-to-price and debt-to-equity

ratio (Barbee, Mukherji, & Raines, 1996), may provide an important insight into the

cross-sectional relationship of common stock returns in Nepal. Therefore, future

studies are recommended to include these variables as well. Lastly, this study

used over 75 percent observations from banking and financial sectors. The results

are thus not representative of all sectors of the economy. Hence, future studies are

suggested to include significant number of observations from the sectors other

than banks and financial institutions as well.

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The Lumbini Journal of Business and Economics, Vol. 1, April, 2011

Stock Beta, Firm Size and Book-To-market Effects on Cross-section of Common Stock Returns in Nepal

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