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@yahoo.com
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@usm.my
International contact:
School of Management
Universiti Sains Malaysia
11800 Minden, Pulau Penang
MALAYSIA
Tel: 60-4-6533888 ext 3953
Fax: 60-4-6577448
2005
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.
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.
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
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.
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 U.S. 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
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.
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)
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.
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
t 1
1
M
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:
RC* 
1
M
RM* 
1
M
M
 L
m 1
BHAR
 WBHAR   TC 
M
 W
m 1
BHAR
 LBHAR   TC 
where, R C* 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.
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.
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 i.e.
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
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 4.5 % 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
(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.
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Arifin, Noraini Mohd and David M. Power (1996) Some Evidence on Short-run Market
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