The CAPM and Risk-Return Anomalies in Asian Emerging Markets:
Evidence from Bangladesh and Malaysia
Abu Taher Mollik
Accounting, Banking and Finance
School of ISA
Faculty of Business and Government
University of Canberra
Building 11, Room: 11C17
Email:abumollik@yahoo.com.au; abu.mollik@canberra.edu.au
Phone: (+61) 0422 239 066
Presented at the 11th Asian Business Research Conference, 2014. Copy right reserved.
The CAPM and Risk-Return Anomalies in Asian Emerging Markets:
Evidence from Bangladesh and Malaysia
Abstract
The Capital Asset Pricing Model (CAPM) establishes a positive relationship between the
risk and return of an asset, the risk being measured by beta, an asset’s returns sensitivity
to the market returns. Inconsistencies/ deviations from the prediction of the CAPM are
considered as market anomalies in finance literature. This paper investigates the market
anomalies based on the risk-return relationship in Dhaka Stock Exchange (DSE) and
Malaysian stock exchange Bursa Malaysia Berhad (BMB). The study finds that there
exist significant share price anomalies in both the markets. Specifically, the results show
that there are high (low) beta companies with low (high) returns which are clear
deviations from the predictions of the CAPM in both the markets. The findings thus
suggest that pricing of some individual companies may deviate from its long-term
equilibrium price (i.e., mispricing) predicted by the standard single factor asset pricing
model, the CAPM. These findings have important implications for stock brokers, fund
managers, speculators, practitioners and general equity investors for investment decisions
in that the investors, analysts and practitioners may be able to use the mispricing
information to create profitable investment strategies in both the markets.
Broad Track: FINANCE
JEL Classification: C 12, C 23, D 53, G11& 12, N 15
Key words: Risk and return, CAPM model, Market anomalies, Market risk, Risk
premium, Dhaka Stock Exchange, Bursa Malaysia.
INTRODUCTION
The standard (mean-variance) capital asset pricing model (CAPM) independently
developed by Sharp (1964), Lintner (1965) and Mossin (1968) establishes a positive
linear relationship between beta risk and expected asset returns. The expected return on
an asset need to be more as the risk/ uncertainty increases because investors will hold a
risky asset if they are compensated with commensurably higher returns. The concept of
risk-return trade-off plays a crucial role in most financial decision-making processes of a
firm - its asset valuation, investment, financing and distribution decisions. In the
(CAPM) framework, systematic risk or beta is the only relevant risk of an asset or
parameter that influences the equilibrium return on a risky stock (Mandelker and Rhee,
1984) and can be measured by the covariance of the asset return with the market return or
by the covariance with other common factors related to investors’ marginal utility in
Merton’s (1973) intertemporal capital asset pricing model (ICAPM). If market portfolio
(index) is the asset, the risk can be measured by the conditional variance of market return.
The traditional finance theory has enabled the development of a huge amount of financial
models, that tend to be based on strict, and in many cases unrealistic, assumptions.
Sscholars in the 1980’s begin to discover a host of empirical results inconsistent with the
view that security prices are determined in accordance with the equilibrium models and
the Efficient Market Hypothesis (Shefrin, 2000). Some examples of these empirical
findings are the medium term momentum effect, the long term reversal effect, the size
effect, and the January effect. Proponents of the traditional finance theory regard the
findings as anomalous and thus call them anomalies. The persistence of such observed
anomalies in the financial market, as well as the appearance of new anomalies, however,
make scholars begin to wonder whether the traditional theory is in fact fully capable of
explaining the market and thereby the pricing of financial assets (Shefrin, 2000).
In response to the shortcomings of the traditional finance theory in explaining the
financial market, a field now known as behavioural finance has emerged which rests on
two pillars, being investor psychology and limits to arbitrage (Barberis and Thaler, 2003).
The empirical findings are mixed for the prediction of the standard CAPM model. Early
researches, Fama and MacBeth (1973) for example, find support for the risk-return
relationship in a cross-section of companies. However, empirical evidence in the 1990s
(e.g. Fama and French, 1992, 1996; 2004; Jegadeesh, 1992, Jegadeesh and Titman, 1993)
indicates that betas are not statistically related to returns, but other factors i.e., size, value
and momentum has risk premium. Indeed, Fama and French (1992) reveal a negative
relationship between risk and return in terms of single factor CAPM and suggest that a
multi-index model as the more realistic approach for measuring the risk in the market.
Dempsey (2013) declared the CAPM dead, once again following Fama and French
(1992) based on a re-examination of the research of Fama and French (1992) and Black
et al., (1972).
Bartholdy and Peare (2004) find that the ability of beta to explain differences in returns in
subsequent periods ranges from a low of 0.01% to a high of 11.73% across years and is at
best 3%, on average and that the Fama and French three factor model does not do much
better; although the size factor is found to be significant with the R-square at around only
5%, suggesting that that neither model is useful for estimating cost of equity, at least for
the simple estimation techniques. The finance practitioners, nevertheless, seem to prefer
CAPM for estimating cost of equity (see, for example, Bruner et al., 1998 and Graham
and Harvey, 2001). In the context of market portfolios, Darrat et al. (2011), Bali et al.
(2009), Bali and Peng (2006) and Ghysels et al. (2005) lend statistically significant
support for a positive relationship, while Goyal and Santa-Clara (2003), Chan et al.
(2002), Harvey (2001) and Glosten et al. (1993) fail to detect any positive relationship. In
fact, Harvey (2001) and Glosten et al. (1993) reported a reverse relationship. The low
explanatory power of the simple equation technique, for both the CAPM and Fama and
French three factor models, however, suggest that there are market anomalies either
because the standard finance models are not strong enough to capture the cross-sectional
variations in returns on assets or the markets considered are not efficient enough for the
models to be effectively applied. Whatever the reasons, the existence of market
anomalies implies that investors are able to make various profitable trading strategies
depending on the degree of anomalies in the particular market. The primary objective of
the study is to investigate the CAPM based risk-return anomalies in the equities traded in
DSE and in Malaysian Stock Market, the Bursa Malaysia.
Most prior research focuses on mature markets such as those in the U.S. and Europe (see,
for instance, Nawalkha & Schwarz, 2004; Jagannathan & McGrattan, 1995). Although
the tests of CAPM are extremely sensitive to use of market proxy since the market
portfolio needs to be ex-ante mean variance efficient (Focardi and Fabozzi, 2004), the
model has been used in developed and emerging markets alike. Surprisingly, a few of the
studies found evidence to support the validity of CAPM despite imperfect nature of those
emerging markets (e.g., Guy, 1977; Hawawini and Mitchel, 1982; Sauer and Murphy,
1992; and Amanulla and Kamaiah, 1998). A non-trivial number of studies, however,
failed to establish a linear risk-return relationship in emerging markets. For example, for
a group of 19 emerging markets, including Pakistan, using eight year data from 1986 to
1993, Claessenset al. (1995) conclude that while similar factors govern the cross-section
of emerging market return, the signs of most of the coefficients are contrary to those
found in developed markets. It is interesting to note that in their study, Pakistan was the
only country with a significant negative beta risk premium. Estrada (2000) concludes that
betas and stock returns in emerging markets do not seem to be related. Wang and Tang
(1991), Bark (1991) and Huang (1997) report a negative risk/return relationship for three
Asian markets - Singapore, South Korea and Taiwan, respectively. Cheunget al. (1993)
also reports a weak risk/return relation for South Korea and Taiwan. Cheung and Wong
(1992) find a weak relationship between risk and return in the Hong Kong market. Molla
and Mobarek (2009) using the daily returns and Dimson corrected beta, suggested that
the overall market movements did not influence the share returns in the Botswana Stock
Exchange for the period of 2000-2005. Ward and Muller (2012) found that portfolios
constructed on the basis of ranked beta exhibited a monotonic inverse relationship to
what the CAPM prescribes, for most of the time series. They suggested that the use of the
single beta CAPM is therefore inappropriate. Tsai et al. (2014) focus on the capital asset
pricing model (CAPM) beta and downside betas. The empirical results of market index
returns in the international samples of 23 developed countries exhibit significant
differences between the CAPM and downside betas, indicating that these models capture
distinct risks. Considering autocorrelation variance, the DCC downside betas (HW-beta
and HR-beta) more effectively explain the expected stock market returns than does the
CAPM beta. They use a DCC model to estimate the time-varying CAPM beta and
downside betas. The CAPM beta and downside betas capture different risks. Downside
betas explain the expected stock returns better than CAPM beta.
This paper focuses on Dhaka Stock Exchange (DSE) in Bangladesh and Bursa Malaysia
(BM). It is important to explore the risk-return anomalies of companies listed on DSE
and BM, as existence of significant may provide investors with opportunities for making
profitable investment strategies in the markets, avoiding crucial downside risk. Also, the
markets may be important for international diversification in that emerging markets have
great potential for equity risk diversification and they also offer higher average returns
than the developed markets (Harvey, 1995). Thus, the findings of the study will be of
great value to national and international portfolio investors in DSE.
Earlier researches on DSE have reported mixed evidence regarding risk-return
relationship, using various different models and data sets. Rahman and Baten (2006)
tested the validity of CAPM applying the Fama-French (1992) Three-Factor model to a
data set of 123 DSE listed non-financial companies. They found the beta and size (sales)
to be statistically significantly related (beta was inversely related) to returns in their
cross-sectional models. They used five-yearly average cross-section and pooled timeseries and cross-section models log of daily frequency of returns, including lag and lead,
to estimate the individual equity beta. Alam et al. (2007) showed an inverse risk and
return relationship in the DSE plugging in the simple average of market returns over 1994
to 2005 periods and Bangladesh T-bill rate as risk-free rate of return in the famous single
index market model equation. Their analysis is too simplified to establish the findings
and the average return calculation could be biased downward by including the period of
market crash in 1996, therefore, re-enforcing the inverse relationship. Ali et al. (2010)
test the validity of CAPM in the Dhaka Stock Exchange (DSE) using Fama and Macbeth
(1973) approach to 160 companies for the period from July 1998 to June 2008. They find
a positive, but non-linear and statistically insignificant relation between 24-months
rolling monthly risk (beta) and return. They suggested that beta cannot be used as the
main and only source of risk. Hasan et al. (2011) investigated the risk-return relationship
in DSE under CAPM framework using monthly stock returns for 80 non-financial
companies for the period of January 2005 to December 2009. They observed that the
intercept term is significantly different from zero and a positive, but insignificant
relationship between beta and share return. They further observed the existence of
linearity of the security market line and the unique risk and the interaction were
insignificant during their study period. Faruque (2012) investigated the performance of
Arbitrage Pricing Theory (APT) and found exchange rate, as being priced in DSE out of
seven macroeconomic variable tested. He used monthly data sets of 23 most actively
traded stocks and macroeconomic variables covering the period from December 1995 to
November 2010. Mollik (2013) reveals a statistically significant positive relationship
between risk and return, confirming the theoretical predictions and empirical findings on
this issue in developed markets, and supports the findings of Ali et al., (2010), and
Hassan et al., (2011) that revealed a positive, but inconsistent relationship between beta
and return using the data sets of different sample periods and frequencies, these studies
examines the issue in a more comprehensive manner for evidence of risk and return
relationship in DSE. Hassan et al. (2013), however, did not find any significant
relationship between beta and excess returns both in mean-variance CAPM and higher
moments CAPM conditions (third and fourth moments), while concluded that risk
premium for co-skewness is positive for the period 2005-2009 and that in the emerging
markets like Bangladesh stock market, the higher moment CAPM performs
comparatively well. (Mollik and Bepari, 2010) found that betas of individual stocks in
DSE are unstable and the instability increases with the holding periods.
Malaysia is also not considered a developed country, as according to the United Nations
(2014) Malaysia still has a developing economy. Tsai et al. (2014) focus on the capital
asset pricing model (CAPM) beta and downside betas. The empirical results of market
index returns in the international samples of 23 developed countries exhibit significant
differences between the CAPM and downside betas, indicating that these models capture
distinct risks. Considering autocorrelation variance, the DCC downside betas (HW-beta
and HR-beta) more effectively explain the expected stock market returns than does the
CAPM beta. They use a DCC model to estimate the time-varying CAPM beta and
downside betas. The CAPM beta and downside betas capture different risks. Downside
betas explain the expected stock returns better than CAPM beta. Lau, Lee and McInish
(2002) argue in their analysis of Malaysian stock data from 1988-1996, is that there is a
conditional relationship between beta and stock returns. They found that during periods
of positive excess market returns, there was a significant positive relationship and a
negative relationship between beta and returns during months with negative market
excess returns. There results, thus, support the role of beta as a risk measure in markets
such as Malaysia. Le and Chang (2011) found to have a much greater negative and
volatile impact on the Malaysian market than it did in more developed economies such as
Japan during the period 1986 to 2011.
Observations on how local conditions can cause significant impact on the market have
been made by Lim (2008) in her study of the efficiency of eight economic sectors in the
Malaysian stock market and the impact following the 1997 Asian Financial Crisis. She
found that for period of 1 January 1994 to 31 October 2006, the tin and mining sector was
found to be the most efficient sector while the properties sector had the most persistent
market deviations. In fact tin and mining was the most stable sector being least affected
during economic crises. Though it is important to note that when the Malaysian
Government imposed capital controls during turbulent economic periods, investor
confidence increased and the sectors became more stable. These observations were made
for a period that covers almost half of the data set assessed in this essay and so is a likely
influence on the overall underperformance that we have observed in the Malaysian
market.
Considering the fact that betas of individual stocks are unstable and the instability
increases with the holding periods (Mollik and Bepari, 2010) and that two recent studies
(Ali et al., 2010 and Hassan et al., 2011) revealed a positive, but inconsistent relationship
between beta and return, this study explores the existence of risk-return anomalies based
on CAPM consistent risk-class portfolios and relevant returns.
The rest of the paper is organized as follows: Section 2, briefly discusses the theory of
risk and return relationship and portfolio risk classes to identify the market anomalies and
the objectives of the study. Sections 3, discusses the data and methodology. Section 4
reports the results and discussion. Section 5, concludes the paper indicating limitations
and further research prospects.
2.1. Theory of risk and return relationship, portfolio risk classes and the market
anomalies
Theoretically, according to the capital asset pricing model (CAPM), the beta of a market
as a whole is one (b=1) for diversified investors and the beta of an individual stock can be
equal to, more than or less than that of a market. Also, beta of an individual stock can be
zero (b=0), as an asset can be risk-free. Although an individual stock beta can be less than
one (b<1), that is less than the market beta, negative stock beta (b<0) is not logically
defined. This is because, in the CAPM framework, assets with negative betas do not add
value in excess of risk-free assets. In this framework, the minimum rate of return on a
riskless investment is the risk-free rate of return. A risk-free asset (b=0) is sufficient to
earn the risk-free rate of return. Beta (risk) lower than zero, that is negative beta does not
provide any additional benefit to investors in terms of return. Therefore, no rational
investors are expected to accept returns lower than risk-free rate of return even if the
stock has a negative beta, as it is not required for the investors to have investments safer
than to be risk-free (b=0). So, beta is truncated to its lower bound to zero. Thus, if beta
less than one (b<1), that is less than market beta is considered as low-beta, then the lowbeta revolves between zero to less than one (0 to <1). The spectrum of variation of high
beta, beta greater than one (b>1), however, is open to its upper bound. Thus, ceteris
paribus, the returns of a low-beta will have far less variability than its high-beta
counterpart in a given market. Combining the fact of the assumption that most investors
are risk averse in the market i.e., wanting more return (risk premium) for the increased
amount of risk and that the risk seeking investors are happy with relatively less return for
the same given risk (beta). Nevertheless, based on the above analysis, assets in a market
can be divided into following risk-return classes:
1. High-risk high-return and High-risk low-return (inconsistent with the CAPM);
2. Low-risk low-return and Low-risk high-return (inconsistent with the CAPM).
In (1) above, the high-risk low-return class is inconsistent with the CAPM predictions
and indicates crucial downside risk for the investors. Although the low-risk high-return
class, in (2) above indicates inconsistencies with CAPM, it does not includes risk of loss
as such for the investors.
2.2. Objective of the Study
The primary objective of the study is to investigate the CAPM based risk-return
anomalies in the equities traded in DSE and in Malaysian Stock Market, the Bursa
Malaysia. Using the CAPM beta as the measure of risk, the hypotheses being tested are:
H1: There is a statistically significant positive beta (risk) for each individual
securities/stocks listed in DSE and BM, i.e. stock betas are positive and significantly
different from zero.
H2: There is a statistically significant linear relationship between beta and return of
individual stocks in DSE and MB, i.e. higher return is associated with a
commensurable higher risk. The stock beta is significantly different from zero with
positive risk premium.
H3: There is a statistically significant linear relationship between portfolio beta and
portfolio return in DSE and BM, i.e. higher portfolio return is associated with
commensurable higher portfolio risk.
3. Data and Methodology
The data set consists of monthly realized returns of 110 stocks included in the DSE
General Index for the period January 2000 to December 2007 and 404 companies on the
Malaysian stock exchange with share price information from 2001 to 2013. The natural
logarithmic differences in prices are used to measure the stock returns. The natural
logarithmic differences in prices are used to measure the stock returns. Symbolically, the
returns have been expressed in percentage form as follows:
Rit =Ln (
pt
) *100
Pt 1
Where Rit is the return on stock i in time period t, Pt is monthly closing price of stock i
and Pt – 1 is the monthly opening price of stock i. The market return was calculated as
follows:
Xt = Ln(
It
)*100
I t 1
Where Xt is the return on the market index It is the monthly closing number of points of
the market index and It – 1 is the opening number of points of the market index.
The famous market model (CAPM) beta was used as the measure of risk and was
estimated using the following regression model:
E( Rit ) =  i +  i Xt + eit
Beta (β) was estimated by regression of monthly security returns on the returns of the
market index.
Alpha (  ) represents a constant intercept, indicating minimum level of return that is
expected from security i, if the market remains flat.
Where  i is the constant intercept of security i,  i is the slope (systematic risk) of
security i and eit is error term representing the residuals of security i. eit represents non
market (diversifiable) risk of security i.
Scholes and Williams (1977) and Dimson (1979) reported that the CAPM beta is biased
upwards for frequently traded securities and downwards in case of thin trading. Literature
suggests two or three types of alternative estimators to reduce the thin trading bias.
Scholes and Williams (1977) suggested beta estimator accounts for non-trading that last
at most for one period. Dimson (1979) proposed a multiple regression with leading and
lagging market returns as additional regressors, where Dimson’s beta is the sum of all
these multiple coefficients. These two approaches are widely used (see for example,
Mollah and Mobarek, 2009 and Ward and Muller 2012 for recent application in emerging
market beta analysis). Another alternative is to measure returns at lower frequencies e.g.,
weekly holding-period returns rather than daily ones. The advantage of using the longer
holding-period returns is that while the noise generated by thin trading is not affected, the
true returns become larger, implying a better signal-to-noise ratio (see e.g., Stoll and
Whaley, 1990). We hope that our estimates of betas using lower frequency (longer
holding-period) monthly returns are reasonably free from thin trading bias that existed in
DSE. Also, thin trading may be unrelated to the risk-return relationship (Iqbal and
Brooks, 2007).
4. Results and Discussion
4.1. Risk-Return Relationship for Individual Securities
Out of 110 securities, 8 securities have beta value exceeding market beta value of 1, and
9 securities have negative beta values. The remaining securities have beta values between
0 and 1.
Table 1A shows the risk-return matrix for 110 securities .When classified in terms of
mean risk (0.519) and mean return (0.679 %), 34 securities belong to the high risk and
high return class, 19 securities belong to the high risk and low return category, 17
securities lie in low risk and high return class while the residual 40 securities constitute
the low risk and low return group. When classified in terms of market risk and market
return, 4 securities fall under high risk and high return class, 4 securities belong to the
high risk low return class, 11 securities lie in low risk high return class, while 91
securities are classed as representing the low risk and low return subset.
Table 1A: Risk-Return Matrix in DSE
Based on Mean Security Return
Mean = 0.68
Based on Mean
Security Risk (Beta)
Mean= 0.52
High
Low
Total
High
34
17
Low
19
40
Total
53
57
51
59
110
Based on Market index return =1.93
Based on Market Risk
(Beta) =1
High
Low
Total
High
4
11
15
Low
4
91
95
Total
8
102
110
Table 1B: Risk-Return Matrix in Bursa Malaysia
Based on Mean Security Return
Mean = 1.398
Based on Mean
Security Risk (Beta)
Mean= 1.069
High
Low
Total
High
29
46
Low
153
176
Total
182
222
75
329
404
Based on Market index return = 0.65
Based on Market Risk
(Beta) =1
High
Low
Total
High
49
64
113
Low
157
134
291
Total
206
198
404
Table 1B, for Bursa Malaysia, shows the risk-return matrix for 404 securities .When
classified in terms of mean risk (1.069) and mean return (1.398%), 29 securities belong to
the high risk and high return class, 153 securities belong to the high risk and low return
category, 46 securities lie in low risk and high return class while the residual 176
securities constitute the low risk and low return group. When classified in terms of
market risk and market return, 49 securities fall under high risk and high return class, 157
securities belong to the high risk low return class, 64 securities lie in low risk high return
class, while 134 securities are classed as representing the low risk and low return subset.
Table 2A: Risk-Return Anomalies in DSE
Based on Mean Security Return
Mean = 0.68
High
Based on Mean
Security Risk (Beta)
Mean= 0.52
High
Low
Downside Risk
Total
Based on Market Risk
(Beta) =1
High
Low
Downside Risk (Market)
Total
Low
19 (35.85%)
17 (33.33%)
19 (17.27%)
59
Based on Market index return =1.93
High
Low
4 (50.00%)
11 (73.33%)
4 (3.60%)
15
95
51
Total
53
57
110
Total
8
102
110
Table 2B: Risk-Return Anomalies in Bursa Malaysia
Based on Mean Security Return
Mean = 1.398
High
Based on Mean
Security Risk (Beta)
Mean= 1.069
High
Low
Downside Risk
Total
Based on Market Risk
(Beta) =1
High
Low
Downside Risk (Market)
Total
Low
153 (84.07%)
46 (20.72%)
153 (37.87%)
329
Based on Market index return = 0.65
High
Low
157 (76.21%)
64 (32.32%)
157 (38.86%)
113
291
75
Total
182
222
404
Total
206
198
404
Table 2 A and 2B depict the risk-return anomalies in Malaysian Stock Exchange, Bursa
Malaysia. Two cases anomalies are identified: high-risk low-return and low-risk high-
return. While the low-risk high- return is preferred by the investors, high-risk low-return
creates a crucial investment risk for the investors and are expected to be avoided. This
can be considered a potential downside risk. The results of the analysis reveals that the
potential downside risk is higher (38.86%) for Malaysian market than that in Bangladeshi
market (3.60%), suggesting that investors in DSE has less risk of investment in
Bangladesh than the investors in Bursa Malaysia.
The results from the analysis of the Bursa Malaysia above link with Lau, Lee and
McInish’s (2002) argument that there is a conditional relationship between beta and
returns on stock investment in the sense that the CAPM does not dictate how the market
will perform overall, but provides an additional tool for an investor to aid their decisionmaking and based on their personal level of risk aversion or risk taking.
5. Conclusions
This paper attempts to investigate the CAPM based risk-return anomalies in the equities
traded in DSE and in Malaysian Stock Market, the Bursa Malaysia. The study reveals that
there are risk-return anomalies in both the markets with higher downside risk (with high
risk- low return) in Bursa Malaysia. Inconsistencies revealed in the context of relative
risk for high-risk portfolios, suggest the existence of some mispricing in high risk assets.
Using the Single Index CAPM Model security returns in both the markets, DSE and
Bursa Malaysia exhibit statistically significant positive movements with market
movements. This study used an additional technique of risk class based portfolio
formation unlike previous studies in the area. While the portfolio risk and returns are
significantly positively related in general, some inconsistencies were revealed in the
context of relative risk for high risk portfolios, suggesting the existence of some
anomalies or mispricing in high risk assets. Portfolio risks and returns are also highly
positively related in that portfolio beta values are statistically significant in the portfolio
risk-return models. The results also reveal that when companies are grouped into
portfolios based on betas (high and low betas) and beta-returns (high-beta-high-return
and low-beta-low-return and so on), the portfolios with the highest beta assets are not
always associated with the highest return, suggesting the existence of market anomalies.
These findings validate the efficient market hypothesis in DSE in terms of risk-return
trade-off in general, subject to some market anomalies. These findings have important
implications for investment decisions at the DSE and Bursa Malaysia in that the
investors, analysts and practitioners may be able to use the mispricing information to
create profitable investment strategies. Investors should hold, in general, efficiently
diversified portfolios to maximize their return for a particular level of risk because they
will be rewarded only for systematic risk.
The findings of the study are limited to the use of standard mean-variance CAPM model.
Further studies, using other multi-variate asset pricing models e.g., AP, Fama-French
Three-factor to Five-factor models can reconfirm the findings in DSE, Bursa Malaysia
and other emerging markets. Since we have used low frequency monthly data, future
studies using higher frequency (i.e., daily or weekly) data for a longer period can be
attempted to revalidate the findings in emerging markets, including DSE and Bursa
Malaysia.
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