Economic subjects | Investments, Stock exchange » Husni-Ahmad - Testing Momentum and Contrarian Strategies In The Malaysia Stock Exchange

Source: http://www.doksinet Testing Momentum and Contrarian Strategies In The Malaysia Stock Exchange Tafdil Husni* Zamri Ahmad* * Author Correspondence Tafdil Husni is a lecturer at Faculty of Economics, Andalas University, Padang, Indonesia. E-mail: tafdilhusni@yahoocom International contact: Faculty of Economics Andalas University Padang Kampus Limau Manih Padang 25163 INDONESIA Tel: 62-751-71088 Fax: 62-751-71089 * Dr. Zamri Ahmad is a lecturer at the School of Management, Universiti Sains Malaysia. E-mail: zahmad@usmmy International contact: School of Management Universiti Sains Malaysia 11800 Minden, Pulau Penang MALAYSIA Tel: 60-4-6533888 ext 3953 Fax: 60-4-6577448 2005 Source: http://www.doksinet Testing Momentum and Contrarian Strategies In The Malaysia Stock Exchange Abstract The study investigates whether or not the profitability of momentum and contrarian strategies work in the Malaysian stock exchange (formerly Kuala Lumpur Stock Exchange / KLSE). By using daily

data for the period 1988 through 2002 and following the strategy quite similar to Jegadeesh and Titman (1993), we find that only one strategy, 3-month ranking, 3-month testing period, has statistically significant abnormal returns for momentum strategies. If we construct shorter ranking and test periods, we found that the momentum payoff still work for 2-month ranking and 2 month testing strategy. Whereas, the contrarian strategy appears to work for 1-month ranking, 1-month testing period. Both results are statistically significant at 5% level. The study also reveals that the profitability of momentum and contrarian strategies still exist after the inclusion of transaction cost. These findings are in line with prior studies, which found that the profitability of momentum is achievable in medium term, whereas contrarian strategy works in short term. Introduction An extensive body of finance literature has been written on the issue of whether there is investment strategies based on

stock price data. There are two investment strategies based on historical return that has been recognized. First, there is the contrarian strategy, which arranges stocks on their performance over some previous period and suggests buying past losers and selling past winners. This strategy is based on the premise that market overreacts to information. Second, momentum strategy makes an equivalent ranking but recommend buying past winners and selling past losers. Momentum strategy is based on the premise that market underreacts to information. Both strategies normally maintain prior ranking periods and subsequent investment holding periods of similar length. What keeps contrarian and momentum strategies from being mutually inconsistent is that the former is based on long-term ranking and short term periods, usually of three years or more for long term and of several days to several weeks for the short term, while the latter are based on medium-term ranking periods, usually between three

and twelve months. Source: http://www.doksinet There are now growing empirical evidence of profitability contrarian and momentum strategies. For example, Ahmet and Nurset (1999) examine abnormal profits of long- term contrarian strategies in the stock markets of seven non-US industrialized countries. Rosita et al (1995) investigate abnormal profit of short-term contrarian strategies in Japan stock market. The same result also found by Hameed and Ting (2000) in the Malaysia stock markets. Rouwenhorst (1999) investigate the profitability of momentum strategies in six (out of 20) emerging equity markets. Hameed and Yuanto (1999) find that a momentum strategy yields small but statistically significant profits in six Asian stock markets. Schiereck et al (1999) examine profitability of momentum and contrarian strategies in the Germany equity market. These works on contrarian and momentum effect, stand in stark contrast to wellaccepted doctrine of the efficient market hypothesis. Under the

null hypothesis of weakform market efficiency, the performance of portfolios of stocks should be independent of past returns. However, some researches have shown that assets returns do exhibit some form of positive autocorrelation in the medium; but mean-revert over short and longer horizons (Poterba & Summer, 1988; Lehman, 1990; Daniel, Hirshleifer & Subrahmanyam, 1998). In this paper, we investigate the short-term contrarian profit and momentum profits in the Malaysia Stock Exchange. This research is different from prior studies in the Malaysian Stock Market such as Hameed and Ting (2000) and Ahmad and Hussain (2001). In term of contrarian strategies, first, we use daily price instead of weekly price that used by Hameed and Ting. Second, we examine the profitability of contrarian strategy in short-term rather long term that has been investigated by Ahmad and Hussain. Third, this is the first study that examines the profitability of both contrarian and momentum investment that

simultaneously occur to the same assets in stock returns in the Malaysia Stock Exchange. Fourth, None of study in the Malaysian stock market that investigates the contraian and momentum profits with the inclusion of transaction cost. Lastly, the contrarian and momentum strategies have been investigated in many equity markets but relatively very few researches of strategies have been carried out in the context of emerging markets. Source: http://www.doksinet However given the characteristics of emerging capital markets, namely thin trading, low liquidity, possibly less informed and rational investors, and having low correlation with other emerging markets and developed market, one would expect more return predictability in these markets (Harvey, 1995). He also concludes that emerging markets are less efficient than developed markets and that higher returns and lower risk can be obtained by incorporating emerging market stocks in investors’ portfolios. Based on the above scenario,

it is therefore reasonable to believe that prices of stocks traded in the Malaysian stock market as an emerging market, may not fully reflect their true value. Hence, some degree of predictability should be possible Literature Review Many studies have documented the long-term and short-term contrarian strategies, for the UK, the US and other countries as well. One of the most important early test of study is by DeBondt and Thaler (1985) in US. They based on research in experimental psychology suggesting that most people overreact to unexpected and dramatic events, and tested whether the same thing occurs in the stock market. Their study points out that portfolio of prior extreme “losers” dramatically outperform prior extreme “winners” even if the latter are more risky. In other words, the work of DeBondt and Thaler find a longhorizon return reversal Overreaction phenomenon in the financial market, who observed by DeBond and Thaler; 1985, can be explained by the finding in

psychology reported by Kahneman and Tversky (1973) that people tend to make prediction using behavioral heuristic known as representativeness rather than Bayes’ rule. This finding in psychology infers that investors in stock market overreact and make extreme prediction based on extrapolation of recent trends. Profit generated by contrarian strategies are seen not only in the US market, but also in stock markets across the continent i.e; UK, Spanish, Canada, and Australian; see Clare and Thomas (1995), Forner and Joaquin (2003), Mun et al (1999), and Gaunt (2000) etc. There are also some studies that investigate the overreaction hypothesis in the securities of Pacific-Rim market like Hong Kong and Malaysia. For example, Kwok (1999) supports the overreaction hypothesis, using the monthly returns (capital gains and Source: http://www.doksinet dividends) of all 33 constituent stocks in the HIS in Hong Kong from January 1980 to December 1993, and finds that the losers portfolio, on

average, outperform the winner portfolio by 9.9% 1 year after the formation period Ahmad and Hussain (2001) investigate overreaction in Malaysian (KLSE) returns during 1986-96, and also observe several factors which have been linked with the overreaction effect: firm size, timevarying risk, and seasonalities with regard to Chinese New Year Effect. They find that the result is consistent with the overreaction hypothesis that stocks in the best /worst performing decile experience a reversal of fortune in the following three years. Some evidences of overreaction are also documented in the short term. For example, Howe (1986) using daily stocks returns data from the University of Chicago’s CRSP tape, which were converted to weekly data, finds that the evidence is strongly consistent with the overreaction hypothesis. Specifically, stocks that experience large positive returns (good news) performed poorly in the 50-week period following that event, with returns averaging about 30 % below

the market. Zarowin (1989) examine evidence regarding the existence of stock market overreaction in the short-run. He ranks common stocks according to their performance during a given month, and find that in subsequent month a portfolio the past month’s losers outperforms a portfolio of the past month’s winners by 2.5 per cent, regardless of which group is smaller Using the weekly share price data were obtained from datastream for 47 individual shares registered on the Kuala Lumpur Stock Exchange over the period January 1990 to December 1994, Arifin and Power (1996) find that some evidence of short-run overreaction in share price, particularly in firs two weeks after portfolio formation date. The trading strategy of buying a portfolio of underperforming shares and selling a portfolio of outperforming shares earns a significant profit. Conrad and Gultekin and Kaul (1997) reject the evidence of short-term overreaction. Based on their bid-return analysis for the 1990-1991 NYSE/AMEX

sample reveals that most, but not all, of the profit from price reversal can be explained by the bid-ask bounce. Given some evidence of overreaction for NYSE/AMEX firms, they find that very low levels of transaction cost (typically less than 20%) eliminate all profits to strategies that attempt to benefit from any potential overreaction. Source: http://www.doksinet Bowman and Iverson (1998) examine the behavior of stock prices in New Zealand after a large weekly change in price and found that the stock market significantly overreact, especially in the case of price declines and significant reversal is confined to the following week They observed the result is affected by risk, size, seasonal and bidask bounce. Schnusenberg and Madura (2001) investigate the short-term over-or underraction of six U.S stock market indexes:The Dow, the S&P 500, the Nasdaq, NYSE, the Russel, and the Wilshire 5000 index. They find evidence of a one-day stock market underreaction to highly positive and

negative news release using two methods to predict returns for these indexes on the following day. Over a sixty-day interval, they reveal strong evidence of a stock market underreaction for winner but an overreaction for losers. Subsequent studies of contrarian strategies have sought explanations for return reversal. The followings are some of the explanation put forward in the literature: (i) overreaction (DeBondt and Thaler 1985, 1987); (ii) change in risk (Chan 1988, Ball and Kothari 1989); (iii) seasonality effects (Chopra, Lakanishok & Ritter 1992); (iv) the size effect (Zarowin 1990, Clare & Thomas 1995, Dissainake 1997); market-microstructure biases (Conrad and Kaul 1993); and (v) behavioral aspects (Barberis, Shleifer and Vishny 1998, Daniel and Titman 2001). In contrast, Jegadeesh & Titman (1993) is among the first study to test the momentum strategy. They document significant positive returns when stocks are bought and sold based on short-to medium-run historical

returns. Using a US sample of NYSE / AMEX stocks over the period from 1965 to 1989, portfolios based on stocks’ relative strength were constructed. At the end of each month, all stocks with a return history of at least 12 months were ranked into deciles based on their past J-month return ( J equals 3,6,9, or 12) and assigned to one of ten relative strength portfolios. Portfolio1 consisted of the past lowest performing stocks, or (losers), while portfolio 10 was made up by the past best performing stocks or (winner). These portfolios are equally weighted at formation, and held for K subsequent months (K = 3, 6, 9 or 12 months). Jegadeesh & Titman find that the 6 x 6 momentum strategy generates returns of about 1% per month, while their most profitable, 12 x 3 momentum strategy generates returns of as much as Source: http://www.doksinet 1.49% per month They document that past winners on average continue to outperform past losers, so that there was momentum in stock prices. The

evidence of momentum in stock prices over the medium terms is well accepted and supported for the developed market in the US. For instance, see Chan, Jegadeesh and Lakonishik (1996, 1999), Maskowit and Grinblat (1999), Hong and Stein (1999), O’Neal (2000), Lewellen (2002), Chordia and Shivakumar (2002), Cooper et al (2004) etc. Similar result are found on other stock markets Outside the US as well; see for example, Schiereck, Debondt, and Weber (1999), Rouwenshort (1998, 1999), Liu et al (1999), Chan, Hameed and Tong (2000), Glaser and Weber (2001), Demir et al. (2004), etc. However, these papers do not cover the same period of time and the methodologies used to detect momentum are not uniformed. From the previous studies of momentum, the source of the profit and the interpretation of the evidence are also widely debated. The behaviorist argues that momentum profits provide strong evidence of market inefficiency, and are due to stock prices’ under-reaction to information,

investors’ herding behavior, etc. On the other hand, market efficiency supporters argue that either risk (cross-sectional and/or time-varying expected returns) is the main source of momentum profits or that abnormal return is a product of data mining. While contrarian and momentum profits are often found to be statistically significant, even after controlling for the factors mentioned above, it is important to investigate whether these profits still exist and statistically significant after allowance for plausible values of transaction cost. The evidence of economically significant contrarian and momentum profits after inclusion of transaction cost remains mixed. For example, Lehman (1990) found economically significant short-term profits even after controlling for transaction costs. In contrast, Conrad, Gultekin & Kaul (1997) found that when transaction costs are taken into account, all the short-term contrarian profits are completely eliminated. Cleary and Inglis (1998)

examined the impact of transaction cost on the implementation of momentum strategies and found that it may not be exploitable by the average retail investor facing higher levels of transaction cost. Source: http://www.doksinet Data and Methodology Daily prices are obtained from Pusat Komputer Professional, a company based in Pahang, Malaysia. Adjustment is made to take account into of stock split, rights, and dividend. We choose daily prices instead of weekly and monthly prices as they can capture the dynamic of price behavior. All companies selected for analysis are from the main board and the period covered in January 1988 to December 2002. The numbers of companies will increase every year as we add new companies in the sample as they get listed. To analyze the profitability of price momentum strategies and contrarian, we employ the methodology used by Jegadesh and Titman (1993). We consider ranking periods of r = 3, 6, 9 and 12 months and subsequent holding periods of h = 3, 6, 9

and 12 months, giving a total of 16 r x h momentum strategies and then we continue to examine in short term of 2 and 1 months. Unlike Jegadeesh and Titman’s study, where portfolios involve overlapping periods, this study examines non-overlapping periods. This modification can at least reduce the bias arising form double counting resulting from the use of overlapping periods. In addition, Pan and Hsueh (2001) find that the International momentum effect appears to disappear when the analysis is conducted using non overlapping data. So, they conclude that the result is simply an empirical illusion due to the use of overlapping data. The profits of momentum and contrarian strategies are calculated for the returns on buy-and hold method for both winner and loser portfolios which stocks are ranked based on their returns over the past 1, 2 and 3, 6, 9 and 12 months, labeled here as the ranking period (RP). Stocks are divided into 10 equal-weighted portfolios P1 represents the loser

portfolio with the lowest returns, and P10 represents the winner portfolio with the highest returns. We prefer buy-hold returns instead of cumulative abnormal returns because they accurately reflect the actual return that investors receive from their investment, see Barber and Lyon (1997), Kothari and Warner (1997). Daily returns (R), and buy and hold abnormal returns (BHAR) are calculated using equations 1, 2, 3 and 4, respectively. [ R jt = Ln Pjt / Pjt −1 ] (1) Source: http://www.doksinet where, R jt = Return of security j at period t Pjt = Price of the security j at the end of period t Pjt −1 = Price of the security j at the end of period t-1 This study uses logarithmic returns instead of discrete returns, as they are preferable for theoretical and empirical reasons. Theoretically, they are analytically more tractable when linking together sub period returns to form returns over longer interval (simply add up the sub-period returns). Empirically, they are more

likely to be normally distributed, and they conform to the assumptions of standard statistical techniques (Strong, 1992). In addition, the use of logarithmic returns is common in contrarian and momentum literature. Then, buy and hold abnormal return, BHAR, are calculated as follows: T T t =1 t =1 BHAR j = ∏ (1 + R jt ) − ∏ (1 + Rmt ) where, BHAR j = Buy and hold abnormal returns of security j Rmt T = The return of market, using the KLSE Composite Index returns as proxy = The number of day in the 1, 3, 6, 9, 12 month periods the In the following 1, 3, 6, 9 and 12 months, described here as the test period (TP), the BHAR for all stocks in the winner and loser portfolios are calculated. The mean of these BHAR represent the cumulative buy and hold abnormal return for an equal weighted portfolio and is measured as LBHAR = 1 N WBHAR = 1 N N ∑ BHAR t =1 j N ∑ BHAR t =1 j where WBHAR is the cumulative buy and hold abnormal return of the winner portfolio, LBHAR is the

cumulative buy and hold abnormal return of the loser portfolio, and N is the number of stocks in each portfolio. Source: http://www.doksinet This procedure is replicated for each non-overlapping periods. The cumulative buy and hold abnormal return of the loser and winner portfolios are then averaged across all test periods: L BHAR = W BHAR = 1 M M ∑L 1 M t =1 BHAR M ∑W t =1 BHAR where, L BHAR is the average cumulative buy and hold abnormal return of loser portfolios, − W BHAR is the average cumulative buy and hold abnormal return of winner portfolios; and M is the number test period. The final step of the trading rule is to determine the difference between returns on the average cumulative buy and hold abnormal return of winner portfolios and the loser portfolios that will generate significant abnormal profit. If the contrarian strategy works, assuming zero transaction cost, then the study will find that past loser portfolios outperform past winner

portfolios in the test period: RC = L BHAR − W BHAR If the momentum strategy works, assuming zero transaction cost, then the study will find that past winner portfolios outperform past loser portfolios in the test period: RM = W BHAR − L BHAR where, RC is the return of contrarian strategy and RM is the return of momentum strategy. A t-test will then be used to determine whether the difference is statistically different from zero. Transaction Cost To determine the impact of transaction cost in contrarian and momentum strategies, this study uses the formula: Source: http://www.doksinet RC* = 1 M RM* = 1 M M ∑ [(L m =1 BHAR M ∑ [(W m =1 BHAR − WBHAR ) − TC ] − LBHAR ) − TC ] where, RC* is the after transaction cost of contrarian return, RM is the after transaction of momentum return, and TC is the transaction cost. Then, a t-test will also be used to determine whether the difference is statistically different from zero. Results Table 1 shows

the average returns of winners and losers in the rank periods and also the average returns of winner minus loser (W-L) portfolio for the r x h strategies. In the test period, we find that there are 3 out of 16 trading strategies where momentum returns are positive. All of them come from 3-month ranking However, only one of them appears to be significant at 5 % level, 3-month ranking and 3-month testing. Scanning through the table in the test period, we find that the highest profit is for 3-month ranking periods, 3-month holding periods (3x3). It shows that the performances of past winners remain better in the future periods and the performance of past losers continues under-perform in the next periods. It means that the average difference between P10 (top- winner) and P1 (top-loser) portfolio returns during the 15-year period is 0.0466, which is statistically different from zero at 5%, level, as well as their performance is above the market. Source: http://www.doksinet Table 1

Returns of Momentum Strategy Rank Period (r) R Portfolio 3 Winner (W) Loser (L) Return 0.4050 -0.2671 Test Period (h) Strategy Portfolio 3x3 3x6 3x9 3 x 12 6 Winner Loser 0.6140 -0.3673 6x3 6x6 6x9 6 x 12 9 Winner (W) Loser (L)) 0.8494 -0.4527 9x3 9x6 9x9 9 x 12 12 Winner (W) Loser (L) 1.0354 -0.5231 12 x 3 12 x 6 12 x 9 12 x 12 W–L t-stat W–L t-stat W–L t-stat W–L t-stat W–L t-stat W–L t-stat W–L t-stat W–L t-stat W–L t-stat W–L t-stat W–L t-stat W–L t-stat W–L t-stat W–L t-stat W–L t-stat W–L t-stat Obser vation 58 57 56 55 29 28 28 27 19 19 18 18 14 14 14 13 Return 0.0466 (2.052)* 0.0389 (1.079) 0.0145 (0.503) 0.0025 (0.137) -0.028 (-1.134) -0.011 (-0.478) -0.028 (-1.031) -0.049 (-1.158) -0.037 (-1.008) -0.022 (-0.491) -0.030 (-0.619) -0.041 (-0.595) -0.046 (-1.201) -0.035 (-0.106) -0.040 (-0.965) -0.130 (-1.468) Notes: This table presents for price momentum portfolio strategies in the Malaysian stock market from January 1988 to

October 2002. At the end of month, all stocks are sorted in ascending order based on their ranking (r) period returns into ten relative strength decile portfolios. Stocks in the bottom 10 percent of past returns are assigned as the loser portfolio and the top 10 percent as the winner portfolio. These portfolios are equally weighted at ranking and then held for (h) subsequent months. For each r x h strategy and portfolio, the table reports average h month holding period returns (return) over the sample period and t statistics. The t-statistics for W-L indicate whether the returns from momentum strategy of buying past winner and selling past loser are significantly different from zero. An asterisk * indicates that the t-value is significant at 5% level. Source: http://www.doksinet The underreaction hypothesis suggests that the strategy of buying the winners portfolio and selling the losers portfolio can earn profits. This is what table 1 shows ie the mean difference between winner and

losers portfolio in the test periods. If we look at the rest of strategies (beyond 3-month ranking) that gives the result of momentum are negative. It finds that contrary to the hypothesis, the results generally show otherwise However, as indicated by the t-statistics, these negative abnormal returns are not significant. Therefore, a strategy of buying the winner and selling the losers will not give investor any profit. In fact, this strategy will lose investor money Table 2. Profitability of contrarian and momentum strategies Ranking Testing Portfolio Period Period (h) (r) 1 1 Winner Loser Loser-Winner t-statistic 2 2 Winner Loser Winner-Loser t-statistic Observ. -0.0103 0.0036 0.0139 (2.350)* 0.0038 -0.0172 0.0210 (2.081)* 177 88 Notes: This table presents for contrarian and momentum portfolio strategies in the Malaysian stock market from January 1988 to October 2002. At the end of month, all stocks are sorted in ascending order based on their ranking (r) period returns into ten

relative strength decile portfolios. Stocks in the bottom 10 percent of past returns are assigned as the loser portfolio (L) and the top 10 percent as the winner portfolio (W). These portfolios are equally weighted at ranking and then held for (h) subsequent months. The table reports average h month holding period returns (return) over the sample period and t statistics. The t-statistics for W-L indicate whether the returns from momentum strategy of buying past winner and selling past loser are significantly different from zero. Meanwhile, L-W indicate whether the returns from contrarian strategy of buying past loser and selling past winner. An asterisk * indicates that the t-value is significant at 5% level. As there is only one strategy that has a significantly positive of momentum profit in Table 1, we try to examine shorter period i.e one and two months Table 2 shows that the profitability of momentum still appears to work for 2-month ranking period, 2-month testing period and

earn abnormal return a 2.1 % two-month, which is statistically different from zero at level 5%. It means past winners still outperformed past losers On the other hand, for1-month ranking, 1-month testing period, we find that the prior loser portfolios significantly outperformed the prior winner in the holding period, which yield Source: http://www.doksinet abnormal return a 1.40 % one-month This means that there is the overreaction phenomenon in the stocks. The overreaction suggests contrarian strategy, which is long positions in past worst performing, stocks (losers) and short positions in past best performing stocks (winner) can yield abnormal returns. Table 3 displays the returns of contrarian and momentum categories before and after transaction costs. Contrarian strategy still records a positive profit after the inclusion of transaction cost of 0.15 % For the 1x1 strategy, the profit per month is significant at the 5 % level. The result reveals that even with the inclusion of

transaction cost, the contrarian strategy still remain profitable. For momentum strategies, i.e, the 2x2 and the 3x3, the profits calculated for each of these strategies after the inclusion of 0.15 % transaction cost still produce positive return of 1.9 % and 45 % respectively, but significantly different from zero at the 10 % level. There is indication that the inclusion of transaction cost on momentum strategy make the profits less pronounced, but do not eliminate it. Table 3 Returns before and after transaction cost strategies Investment Ranking Testing period strategy period month(s) month(s) Contrarian 1 1 Momentum of contrarian and momentum Returns before transaction cost # 0.014 (2.35)* Returns after transaction cost 0.012 (2.095)* 2 2 0.021 (2.081)* 0.0197 (1.942)* 3 3 0.046 (2.052)* 0.045 (1.986)* # From table 5.2 * Significant at 5 % level * Significant at 10 % level To mitigate and look into the effect non-synchronous trading, first, this study chooses the sample

from the main board instead of the second board. The justification is that most of securities in the main board are generally more active than the second boards in term of trading activity. Second, upon checking each sample in the extreme portfolios Source: http://www.doksinet (P1, loser portfolio and P10, winner portfolio), the study finds, even though there are very few inactive stocks, that most of stocks in winners and losers are actively traded. In addition, the study also finds that some securities do not trade in a given some interval is in P2 to P9). Based on these reasons, contrarian and momentum profits in this study are not affected severely by the thin trading effect. Our result is in line with previous studies, which found that the momentum strategy is in medium term and contrarian strategy in short-term.(Glaser and Weber, 2001; Hameed and Yuanto, 1999; Condrad et al, 1997 ; Bowmen and Iverson, 1998; etc). Conclusion The evidence of this study shows that momentum

strategies are profitable in the medium term (two and three months) which suggest that past winners will perform better in the next period, while past losers will perform worse in the future period. Therefore, a trading strategy that buys the past winners and sells the past losers would provide significant abnormal returns in the future. Meanwhile, contrarian strategy is profitable in short term period (one month), which assumes just opposite behavior from stock returns, and therefore recommends buying the past losers and selling the past winners. The success of momentum and contrarian investment strategies above is a direct test of the weak form efficient market hypothesis and it could give a serious challenge to efficient market hypothesis. These strategies may imply that the markets are not efficient as future price are predictable. The weak form efficiency reveals that an investor cannot use past security price information to consistently earn a portfolio return in excess of

returns that is in proper proportion with the portfolio risk. The debate on the source of the profit and the interpretation of momentum and contrarian are still ongoing. One of theories that explain momentum and contrarian effect is behavioral or non-risk based. Another theory is advanced by market efficient supporter who argue that risk is the main source of momentum and contrarian profits. The others possible explanations for momentum and contrarian strategies are size effect, marketmicrostructure, etc. In order to investigate what factors drive and the magnitude of momentum and contrarian strategies, further research can look into these two possibilities i.e non-risk based such as size, book-to market and turnover and risk based, etc Source: http://www.doksinet REFERENCES Ahmad, Zamri and Simon Hussain (2001) KLSE Long Run Overreaction and the Chinese New Year Effect. Journal of Business Finance and Accounting, 28 (1&2), p63105 Arifin, Noraini Mohd and David M. Power (1996)

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