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. REFERENCES Alam, M. M., Alam, K. A. and Uddin, M. G. S., (2007), Market Depth and Risk Return Analysis for Dhaka Stock Exchange: An Empirical Test of Market Efficiency. ASA University Review, 1(1), pp. 93-101. Ali, M. H., Islam, S. and Chowdhury, M. M., (2010), Test of CAPM in Emerging Stock Markets: A study on Dhaka Stock Exchange, The Cost and Management, November-December, pp. 34 – 37. Amanulla, S. and Kamaiah, B., (1998). Asset price behaviour in Indian stock market: CAPM still relevant. Journal of Financial Management and Analysis, pp. 32-47. Is the 11(1), Bali, T.G. and Peng, L., (2006). Is there a risk-return trade-off? Evidence from high frequency data. Journal of Applied Econometrics, 21, pp. 1169-1198. Bali, T.G., Demirtas, K. and Levy, H., (2009). Is there an intertermporal relation between downside risk and expected returns? Journal of Financial and Quantitative Analysis, 44, pp. 883-909. Barberis, N. and Thaler, R.(2003), A Survey of Behavioral Finance, in Constantinides, G. M., Harris, M., and Stulz, R. M. (Eds.): Handbook of the Economics of Finance: Financial Markets and Asset Pricing (pp. 1053-1128), 1st edition; Elsevier B.V.; Amsterdam, 2003. Barberis, N., Shleifer, A., and Vishny, R. (1998): A Model of Investor Sentiment; Journal of Financial Economics, 49, pp. 307-343. Bark, T., (1991). Anti-dumping Restrictions against Korean Exports: Major Focus on Consumer Electronics Products. Korean Institute for International Economic Policy (May) No. 91-02. Bartholdy, J. and Peare, P., (2005). Estimation of expected return: CAPM vs. Fama and French. International Review of Financial Analysis, 14(4), pp. 407-427. Bepari, M.K., and Mollik, A., (2008). Bangladesh stock market growing? Key indicators based assessment. Journal of Business Administration Online, 7(2), available at www.atu.edu/business/jbao/. Black, F., (1972). Capital Market Equilibrium with Restricted Borrowing. Journal of Business. 45(3), pp. 444-454. Breeden, D., (1979). An intertemporal asset pricing model with stochastic consumption and investment opportunities. Journal of Financial Economics 7, pp. 265–296. Bruner, F., Eades, K., Harris, R. and Higgins, R., (1998). Best practices in estimating the cost of capital: Survey and synthesis. Financial Practice and Education, 8(1), pp. 13-28. Campbell, J.Y., (1996). Understanding risk and return. Journal of Political Economy, 104, pp. 298–345. Campbell, J., Lettau, M., Malkiel, B. and Xu, Y., (2001). Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. Journal of Finance, 49, pp. 1-43. Chen, L., Novy-Marx, R., Zhang, L., (2010). An Alternative Three-factor Model. Working paper. Cheung, Y.L., Wong, A. and Ho, K.Y., (1993). The pricing of risky assets in two emerging Asian markets—Korea and Taiwan. Applied Financial Economics, 3(4), pp. 315–24. Cheung, Y.L. and Wong, K.T., (1992). An Assessment of Risk and Return: Some Empirical Findings from Hong Kong stock Exchange. Applied Financial Economics, 3, pp. 315-324. Claessens, S., Dasgupta, S. and Glen, J., (1995). Return Behavior in Emerging Stock Markets. World Bank Economic Review, 9(1), pp. 131-151. Darrat, A.F., Gilley, O.W., Li, B. and Wu, Y., (2011). Revisiting the risk/return relations in the Asian Pacific markets: New evidence from alternative models. Journal of Business Research, 64, pp.199-206. Dhankar, R.S. and Kumar, R., (2006). Risk-return relationship and effect of diversification on non-market risk: application of market index model in Indian stock market. Journal of Financial Management and Analysis, 19(2), pp. 22-31. Dimson, E.,(1979). Risk Measurement When Shares are Subject to Infrequent Trading. Journal of Financial Economics, 6, pp.197-226. Douglas, G.M., (1969). Risk in the Equity Markets: An Empirical Appraisal of Market Efficieny. Yale Economic Essay, 9, pp. 3-45. Estrada, J., (2000). The cost of equity in emerging markets: A downside risk approach. Emerging Markets Quarterly, 4, pp. 19-30. Fama, E.F. and MacBeth, J., (1973). Risk return and equilibrium: Empirical tests. Journal Political Economy, 81(3), pp. 607-636. of Fama E.F. and French K., (2004). The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspective, 18, pp. 25–46. Fama, E. F.and French, K. R., (1992). Cross-section of Expected Stock Returns. Journal of Finance, 47, pp. 427-463. Fama, E.F. French, K.R., (1993). Common risk factors in the returns on bonds and stocks. Journal of Financial Economics, 33, pp. 3–56. Fama, E.F. French, K.R., (1996). Multifactor explanations of asset pricing anomalies. Journal of Finance, 51, pp. 55–84. Focardi, F. M. and Fabozzi, F. J., (2004). The Mathematics of Financial Modeling and Investment Management, John-Wiley & Sons, Hoboken: New Jersey Faruque, M., (2012). An Empirical Investigation of the Arbitrage Pricing Theory in a Frontier Stock Market: Evidence from Bangladesh, MPRA Munich Personal RePEc Archive, Online at http://mpra.ub.uni-muenchen.de/38675/MPRA Paper No.38675,May 2012, UTC Ghysels, E., Santa-Clara, P. and Valkanov, R., (2005). There is a risk-return trade-off after all. Journal of Financial Economics, 76, pp. 509-548. Glosten, L.R., Jagannathan, R. and Runkle, D.E., (1993). On the relation between expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48, pp. 1779-1801. Goyal, A. and Santa-Clara, P., (2003). Idiosyncratic risk matters! Journal of Finance, 58, pp. 975-1007. Graham, J.R. and Harvey, C.R., (2001). The theory and practice of corporate finance: evidence from the field. Journal of Financial Economic, 60, pp. 187-243. Guy, J. R. F., (1977). The Behavior of Equity Securities on the German Stock Exchange. Journal of Banking and Finance, 1, pp. 71-93. Hasan, M. Z., Kamil, A. A., Mustafa, A. and Baten, M. A., (2011). A Validity test of Capital Asset Pricing Model for Dhaka Stock Exchange. Journal of Applied Sciences, 11(20), pp. 3490 – 3496. Harvey, C., (1995). Predictable risk and returns in emerging markets. Review of Financial Studies, 8, pp. 773-816. Harvey, C., (1995a). The cross-section of volatility and autocorrelation in emerging markets. Finanzmarket and Portfolio Management, 9, pp. 12-34. Harvey, C., (1995b). The risk exposure of emerging equity markets. World Bank Economic Review, 9(1), pp. 19-50. Harvey, C., (2001). The specification of conditional expectations. Journal of Empirical Finance, 8, pp. 573-638. Hawawini, G. A., and Michel, P. A., (1982). The Pricing of Risky Assets on the Belgian Stock Market. Journal of Banking and Finance, 6, pp. 161-178. Huang, Y. S., (1997). An empirical test of the risk-return relationship on the Taiwan Stock Exchange. Applied Financial Economics, 7, pp. 229-239. Iqbal, J. and Brooks, R.D., (2007). Alternative beta risk estimators and asset pricing tests in emerging markets: the case of Pakistan. Journal of Multinational Financial Management, 17, pp. 75-93. Jagnnathan, R. and McGrattan, E. R., (1995). The CAPM debate. Federal Reserve Bank of Minneapolis. Quarterly Review, 19(4), pp. 2-27. Jegadeesh, N., (1992). Does market risk really explain the size effect? Journal of Financial and Quantitative Analysis, 10, pp. 337-351. Jagannathan, R., Wang, Z., (1996). The conditional CAPM and the cross-section of expected returns. Journal of Finance, 51, pp. 3–54. Kim, D., (2006). On the information uncertainty risk and the January effect. Journal of Business, 79, pp. 2127–2162. Kim, D., Kim, T.-S., Min, B.-K., (2011). Future labor income growth and the cross-section of equity returns. Journal of Banking and Finance, 35, pp. 67–81. Kian-Ping Lim, (2008) "Sectoral efficiency of the Malaysian stock market and the impact of the Asian financial crisis", Studies in Economics and Finance, vol. 25, no 3, pp. 196-208. Lau, S, Lee, C & McInish, T (2002), “Stock returns and beta, firms size, E/P, CF/P, book-tomarket, and sales growth: evidence from Singapore and Malaysia”, Journal of Multinational Financial Management, vol. 12, no. 3, pp. 207–222. Le, T, & Chang, Y (2011) “The Impact of Oil Price Fluctuations on Stock Markets in Developed and Emerging Economies”, Economic Growth centre Working Paper Series, viewed 27 April 2014 <http://ideas.repec.org/s/nan/wpaper.html> Lintner, J., (1965). Security prices, risk and maximum gains from diversification. of Finance, 20, pp. 587-616. Journal Lucas, R., (1978). Asset prices in an exchange economy. Econometrica, 46, pp. 1429–1445. Mandelker, G. and S. Rhee, (1984). The Impact of the Degrees of operating and Financial Leverage on Systematic Risk of Common Stocks. Journal of Financial and Quantitative Analysis, March, pp. 45-57. Markowitz, H., (1952). Portfolio selection. Journal of Finance, 7, pp.77-91. Merton, R.C., (1973). An Intertemporal Capital Asset Pricing Model. Econometrica, 41 (5), pp. 867-887. Mollah, S. A. and Mobarek, A., (2009). Thin Trading, the Estimation of Beta and the Relationship between Share Return and Beta in the Emerging Market of Botswana, International Journal of Business Research, 9(1), pp. 119-125. Morck, R., Yeung, B. and Yu, W., (2000). The information content of stock markets: Why do emerging markets have synchronous stock price movement? Journal of Financial Economics, 58, pp. 215-260. Mollik, T.A. and Bepari, K.M., (2010). Instability of stock beta in Dhaka Stock Exchange.Bangladesh. Managerial Finance, 36 (10), pp. 886-902. Mossin, J., (1966). Equilibrium in a capital asset market. Econometrica, 34, pp.768-783. Nawalkha, S. and Schwarz C., (2004). The progeny of CAPM. Available at SSRN: http://ssrn.com/abstract=966403. Rahman, M.M. and Baten, M.A., (2006). An Empirical Testing of Capital Asset Pricing Model in Bangladesh. Journal of Research (Science), Bahauddin Zakariya University, Multan, Pakistan, 17(4), pp. 225-234. Rubinstein, M., (1976). The valuation of uncertain income streams and the pricing of options. Bell Journal of Economics and Management Science, 7, pp. 407–425. Ross, S.A., (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13, pp. 341–360. Sauer, A., and Murphy, A., (1992). An Empirical Comparison of Alternative Models of Capital Asset Pricing in Germany. Journal of Banking and Finance, Vol. 16, pp. 183-196. Scholes, M., and Williams, J., (1977). Estimating Betas from Nonsynchronous Data. Journal of Financial Economics,. 5, pp. 309-327. Sharpe, W.F., (1964). Capital asset prices: A theory of market equilibrium under conditions risk. Journal of Finance, 19, pp. 425-442. of Sharpe, W.F. and Cooper, G.M., (1972). Risk-return classes of New York Stock Exchange common stocks, 1931-1967. Financial Analysts Journal, 28(2), pp. 46-54. Shefrin, H. (2000), Beyond Greed and Fear: Understanding Behavioral Finance and the Psychology of Investing; Harvard Business School Press; Boston, Massachusetts, 2000. Stoll, H. and Whaley, R., (1990). The Dynamics of Stock Index and Stock Index Futures Returns. Journal of Financial and Quantitative Analysis, 25, pp. 441-468. Strugnell D, Gilbert E and Kruger R., ( 2011). Beta, Size and Value Effects on the JSE. Investment Analysts Journal, 74, pp. 1-17. Tsai, H, Chen, M & Yang, C (2014), “A time-varying perspective on the CAPM and downside betas”, International Review of Economics & Finance, vol. 29, pp. 440–454. United Nations 2014, World Economic Situation and Prospects 2014, United Nations New York, viewed 25 April 2014, Vassalou, M., (2003). News related to future GDP growth as a risk factor in equity returns. Journal of Financial Economics, 68, pp. 47–73. Ward, M. and Muller, C., (2012). Empirical testing of the CAPM on the JSE. Investment Anlaysts Journal, 76: 1 – 12. Wolf, H.C., (1998). Determinants of Emerging Market Correlations in Emerging Market Capital Flows, R. M. Levich, Ed, Kluwer Academic Publishers, Boston.