The Effectiveness of Short-Term Contrarian Trading...

Prof. Joseph K W Fung, FIN

The Effectiveness of Short-Term Contrarian Trading Strategy

in Hong Kong Stock Market

BY

JIA Yanqing

11051647

Finance Concentration

An Honours Degree Project Submitted to the

School of Business in Partial Fulfilment

of the Graduation Requirement for the Degree of

Bachelor of Business Administration (Honours)

Hong Kong Baptist University

Hong Kong

April 2014

Table of Contents

Abstract ...................................................................................................................................... 1

1. Introduction ............................................................................................................................ 1

2. Related Literature and Research Background ........................................................................ 2

2.1 Prior literature .................................................................................................................. 2

2.2 Research background ....................................................................................................... 3

3. Problem Specification and Assumptions ............................................................................... 4

3.1 Statement of the problems ................................................................................................ 4

3.2 Assumptions and expected results.................................................................................... 4

4. Data Sample and Research Methodology .............................................................................. 5

4.1 Data collection.................................................................................................................. 5

4.2 Simulation methodology .................................................................................................. 5

4.3 Methodology for data analysis ......................................................................................... 7

5. Data analysis .......................................................................................................................... 7

5.1 Histograms and time-series graphs .................................................................................. 7

5.2 Statistical significance .................................................................................................... 11

5.3 Other analyses ................................................................................................................ 11

6. Conclusion ........................................................................................................................... 16

Appendix 1 ............................................................................................................................... 18

Appendix 2 ............................................................................................................................... 22

References ................................................................................................................................ 34

Abstract

This paper aims to investigate the momentum/contrarian profit in the Hong Kong stock

market in very short periods, namely in one week to one month. To materialize the

continuation of previous price trend or a reversal in stock price, this paper sorts out the five

stocks that had the highest returns and the five that had the lowest returns in the previous one-

month period and labels them as “winners” and “losers”. Then investment portfolios

containing only winners and only losers, as well as combination portfolios are constructed,

under the design of three sets of weights within each portfolio.

After analyses on the return distributions of these portfolios and the Hang Seng Index, this

paper finds out that past losers are very likely to experience a price increase in near future.

Overreaction to information does exist in the stock market in Hong Kong, and a contrarian

strategy of longing losers and shorting winners can turn out to be very lucrative. This profit

cannot be attributed to a higher market risk contained by the portfolios.

1. Introduction

Real-world trading practices, academia and journal articles reveal that the weak form of

market efficiency hypothesis is sometimes violated. As a result, behavioral finance is the

product of research in that area. Momentum strategy, as suggested by the name, implies that

stocks whose price rises have a momentum to keep on going up and the price of stocks

making loss will continue dropping. In contrast, the upholders of contrarian strategy adhere

that betting against the existing price movement will make a profit.

I am motivated by this topic, momentum/contrarian trading strategy, and aim to conduct such

a research to get an integrative view about the momentum/contrarian profit in the Hong Kong

stock market in very short terms.

Above all, the main and core motivation of this paper is to examine the momentum effect or

the tendency of price reversion in Hong Kong stock market. The analysis and results can be

referential for traders to develop trading strategies, for regulators to analyze the efficiency of

Hong Kong stock market and for academia to examine the source of momentum/contrarian

profit etc.

1

2. Related Literature and Research Background

2.1 Prior literature

Momentum is a common phenomenon in the financial market. It suggests that a past winner

will continue to enjoy an increase in its price, and is more likely to generate an above average

profit, and a past loser will see a further drop in price. The opposite phenomenon is

sometimes also present and used as “contrarian strategy”. These are seen as a market

anomaly and generally recognized by market practitioners and academics, especially by

investors. That is also the reason why technical analysis is playing a role in investors’

decision making.

In general, the academics and market participants agree and perceive that there are two types

of investors in the market, that is, value investors (fundamentalists) and

momentum/contrarian investors (chartists). Chartists conduct technical analysis about the

price of an asset or a security. When they identify the momentum in the stock and are

convinced that the trend will continue or reserve, they will open a corresponding position

(long or short) and hold the portfolio until they again after analysis think that the momentum

is no longer going to persist or that the trend is fully corrected. Chartists may use various

analysis tools available to identify the entry and close signals. Commonly used momentum

indicators are relative strength index (RSI), price rate of change (ROC) and moving average

convergence divergence (MACD) and stochastic oscillator.

Momentum and contrarian profits, as a market anomaly, are a complex topic and challenging

even the weakest form of market efficiency theory. Yet, no dominant and convincing theories

are widely recognized to fully explain why they exist (Doukas and McKnight, 2005). The

market practitioners and academics mostly recognize the evidence of their existence but

claim no legitimate reasoning for them. The traditional explanation is from the perspective of

behavioral finance that investors tend to underreact or overreact to new information. The

psychology of overreaction towards good news and underreaction to bad news is also one

argument raised by Hong, Lim and Stein (2000) to explain momentum. However, a handful

recent research papers, for instance, Crombez (2001), have elaborated that irrationality of

investors is not the only reason for momentum. It is even the other way round that rational

investors account for the momentum in the market.

Furthermore, researchers intend to discover the driving forces behind momentum. For this

Carhart (1997) introduced momentum (MOM) as a risk factor into the Fama-French three-

2

factor model and developed Carhart four-factor model to describe stock returns. In a recent

paper by Bandarchuk and Hilscher (2013), they reviewed the research done by many scholars

in the past decades that stock specific characteristics like size, R2, turnover, age, market-to-

book etc play a role in the momentum profit on the stock. Yet interestingly, they retested the

factors and found the stock specific characters not significantly important. In other words,

they argue that stock characteristics cannot be a useful indicator for momentum. They assure

that extreme momentum profit was empirically indicated by the past extreme returns. Its

relationship should be further examined.

There are abundant research papers on the testing of the existence of momentum in different

countries (Czaja et al, 2012) and on how to select stocks to capture momentum profit (for

example, Baltas and Kosowski, 2011), and momentum is worldwide seen but the magnitude

varies from country to country (Hens, 2010). Many practical, not academically focused

articles have also shown the actual use of momentum trading in the real market practice, for

example articles on FinanceAndEconomics.org, Investopedia, Research Affiliates and

MoneyShow.

2.2 Research background

As mentioned before, according to previous research work, the behavioral finance concept

pair of momentum and contrarian profit is present in many countries. Interestingly, it is

revealed by some research papers that a momentum profit will occur in very short term, and

be followed by a contrarian profit later. The answer to the question when the contrarian profit

starts to appear depends on the type of financial security and the countries. For example, the

paper of Chan et al. (2000) shows that for the stock market the reversing trend in price comes

after approximately 3 months. Another paper of Shen et al. (2007) points out that commodity

futures have relatively longer period, 6 to 9 months, before the momentum profit fades away.

A point to note is that the definition of “long term” and “short term” is different across

countries. Hong Kong is known around the globe for its presumption of “short” for horizon in

the financial markets. In Hong Kong, the average investment period is much shorter than

most of developed markets in the world, for instance, the US and Europe. In the US,

investment periods of less than 1 year can all be called short-term.

In this paper, I will look into the profitability and effectiveness of trading strategies and

mainly focus on the performance in very short investment periods, namely from one week to

1 month. One of the reasons is that most of the previous research papers mainly focus on

3

momentum/contrarian profit in relative long terms, i.e. from 1 month up to 5 years. The

second reason is the extraordinarily short investment horizon in Hong Kong. Thirdly, this

focus is also specially set to meet up with the increasing demand of a shorter lock-up period

for hedge funds and closely-held mutual funds. In the real world, fund managers care about

the liquidity problem of cash flow when they evaluate a trading strategy.

3. Problem Specification and Assumptions

3.1 Statement of the problems

The research problem is whether there is momentum or contrarian profit in very short term in

Hong Kong stock market. The further analysis then tries to test how robust the profit is when

looked alone, when compared with the stock index and when the profit is adjusted for market

risk. When the systematic risk in the stocks is hedged away, it may be able to be testified

whether the momentum/contrarian profit comes from firm-specific risk.

3.2 Assumptions and expected results

Due to the prevailing short investment horizon in Hong Kong, in this paper I assume a

contrarian profit will dominate the holding periods shorter than one month. Therefore, it is

assumed that long-buying losers and short-selling winners will generate positive returns.

Another assumption is that the constituent stocks of Hang Seng Index have the same

momentum/contrarian behavior as the others, and the research on them can be representative

for the whole stock market in Hong Kong. This assumption is legitimate from the point of

view that the HS index constituents cover about 60% of the market value of stocks listed in

Hong Kong at all times.

The third assumption is that beta is a good market risk measurement and the market risk

contained in a stock can be almost perfectly hedged away by taking a corresponding position

in beta amount of Hang Seng Index (HS Index). For simplicity, the Hang Seng Index Gross

Total Return Series is used here, instead of Hang Seng index futures. Another advantage of

using HS Index Total Return is that it captures not only the capital gain on price, but also the

before-tax dividend yields.

4

4. Data Sample and Research Methodology

4.1 Data collection

Base on the second assumption, the historical constituent stocks of the Hang Seng Index are

used for the data simulation.

The history of constituent stocks is downloaded from the official website of Hang Seng Index

and their daily adjusted close prices are collected from databases of Yahoo Finance and

Bloomberg. The data are tracked back to 1 January 2004, up to 31 December 2013, making

up a ten-year sample. I use the adjusted close price of stocks here, because total return is the

concern of investors. They take capital gain as well as dividend return into their evaluation of

an investment. The adjusted close price is calculated, with stock splits and dividends taken

into consideration, as if the dividend is reinvested in the same stock as soon as received.

The daily level of HS Index (Gross) Total Return Series is also retrieved from Bloomberg

from 11 October 2004 to the year end of 2013. Since the data before October 2004 are not

available at the Bloomberg Terminal, the Hang Seng Index is used for January to October

2004 as an approximation.

4.2 Simulation methodology

For simulation and data analysis, Excel Visual Basic for Applications and Eviews are used.

The steps of trading simulation are as follows:

1) Sort out the winners and losers of the month

Based on the adjusted close prices at hand, the simple monthly holding return of the

constituent stocks is calculated for the 119 months in the examined period, from January

2004 to November 2013. One thing to note here is that the HS Index constituent list changes

from time to time. The list is reviewed quarterly by the Hang Seng Bank. In the following

simulation, as long as the stocks are included in the Hang Seng Index by the end of the month,

they are included to be ranked by their total return for that month. The five best performers

(winners) and five worst performers (losers) are selected and used for the next step.

2) Construct winner portfolio W and loser portfolio L

Then the winners and losers are used to construct investment portfolios. The specific trading

strategies are described below.

• Entry point: to open the position at the next month beginning

5

The ten selected stocks are purchased or short-sold at the opening price on the first trading

day of the next month (the month after the ranking). For simplicity, it is assumed here that

this opening price equals to the adjusted close price of the month end before.

• Exit point: to close the position one week, two weeks, three weeks and one month since the

last month end

• How to invest: The five winner stocks are bought to create the winner-portfolio W, and the

five losers are bought for the loser-portfolio L. The weights of the five stocks within the

portfolio W and L are designed as below, according to their rankings of total return of that

month.

Table I

Ranking on the winner/loser list

1: “Equal weights” 2: “Unequal weights” 3: “Top and bottom”

1st 20% 5/15=33.33% 100%

2nd 20% 4/15=26.67% 0

3rd 20% 3/15=20% 0

4th 20% 2/15=13.33% 0

5th 20% 1/15=6.67% 0

W1 and L1 are the portfolios in which the 5 winners and 5 losers have equal weights of 20%.

This means that equal amount of money is invested in each stock.

W2 (L2) is the portfolio in which the stocks with higher (lower) past return are granted with

more weights.

W3 and L3 portfolios contain respectively only the stock that ranked on the top and the

bottom of the list of past return.

As shown above, among the winner-portfolios Ws and loser-portfolios Ls, I assign different

weights and aim to check if there is difference between the return distributions of the Ws and

of the Ls. This may show from another angle whether the momentum or a tendency of price

reversion is greater in stocks with extreme past price movements, to confirm if a

momentum/contrarian profit exists.

3) Combine W and L: Long-loser short-winner portfolio (L-W)

6

If the results under step 2 show that the winners and losers have the consistent evidence for

either momentum or contrarian profit, then the next step is to combine and intensify the effect,

so as to maximize the momentum/contrarian profit one can make out from the market. Under

the first assumption made before, I will long-buy the losers and short-sell winners, to

investigate whether a reversal of previous price trend exists or dominances in the stock

market in Hong Kong.

4.3 Methodology for data analysis

With the winner-portfolios and loser-portfolios with different weights at hand, the analysis on

their performance will be conducted as follows.

1) Histograms and time-series graphs

It will be checked whether the return distribution is random walk or shows certain pattern

with respect to time. Based on the mean and standard deviation of the return distributions, the

profitability and riskiness of each portfolio will be compared.

If the trading strategy of longing losers and shorting winners generates a positive return, then

it implies a contrarian strategy works in short term. If the average return appears to be

negative, it implies that momentum exists in very short period.

2) Statistical significance

After the simple comparison before, the Student’s t-test will be used to test the robustness of

the mean of return. The key concerns are whether Ls (Ws) standing alone can be a profitable

(loss generating) strategy and whether the L-W portfolios work at 95% significance level.

3) Other analyses: for example, risk-adjusted returns

The profitability of Ws, Ls and the combined portfolios W-L will be compared with market

index directly. Also, the portfolio will be adjusted as if hedged to be market-risk neutral, and

then be tested again if there are any statistically significant profits.

5. Data analysis

5.1 Histograms and time-series graphs

From eyeballing returns plotted on time-series graphs, it can be seen that the return

distributions are rather random walk (graphs are not attached in this report but available upon

request). The histograms of all the distributions are in the appendix 2.

7

Table II extracts in the important information from the histograms and shows clearly the

mean, standard deviation, the information ratio and the t-value for each portfolio with

different holding periods and weights.

From table II, it can be seen that the standard deviations of the portfolios L, W and L-W that

have the same length of holding period are similar to each other, and are not significantly

higher than that of the HS Index.

In order to visualize the mean return of each portfolio, the graphs in the appendix 1 plot out

the value of the portfolios throughout the 10 years, provided that $1000 is invested at the

beginning of 2004.

Here an information ratio is also utilized for judging the performance of the portfolios, which

is the mean divided by standard deviation.

Information ratio = µ/ σ

This ratio can reveal how much return is compensated for each percent of total risk taken. I

use this ratio here to evaluate the different portfolios.

After comparison, the following results stand out:

[1] With a 1-week holding period, the loser-portfolios L1, L2, L3 are the best choice

compared with Ws, L-W and the market. The L-W portfolios are the second best investment

and more profitable than the market, except in the group of portfolios with equal weights.

At the 2-week and 3-week level, L-W portfolios perform best, followed by the loser-

portfolios. Both the L-W and the Ls outperform the market index notably.

However, when the holding period is prolonged to 1 month, the Ls do not outperform the

market much and the long-loser short-winner strategy does not work well.

This indicates that there is a contrarian profit in Hong Kong stock market which lasts for less

than one month.

[2] When the return is across holding periods, the 1-week holding period generates the largest

return for portfolio Ls and L-W, as high as 0.7% to 1.4% per week. This suggests that most

contrarian profit is realized in the first week, i.e. the price correction is very rapid for stocks

that have dropped a lot in price. On the other hand, the performance of winner-portfolios Ws

is neither outstanding nor consistent with time, but the price reversion for winners maximizes

in 2 weeks. With a holding period of 2 weeks, the returns of all Ws are negative.

8

Table II Holding period return

1-week Equal weights Unequal weights Top and Bottom

HS Index L1 W1 L1-W1 L2 W2 L2-W2 L3 W3 L3-W3 mean µ 0.003801584 0.007684494 0.003724028 0.003960467 0.009627807 0.00248309 0.007144717 0.014960839 0.003543398 0.011417441

std dev σ

0.031923784 0.036804669 0.044584523 0.041405803 0.036153429 0.044544558 0.041276668 0.051644452 0.065008121 0.069095935

µ/ σ 0.119083134 0.208791285 0.083527363 0.095650035 0.266304108 0.055743964 0.173093343 0.289689183 0.054507008 0.165240413

t-value 1.299043629 2.277644019*

0.911175952 1.0434187 2.905034845**

0.608094856 1.888225449*

3.160135905**

0.594601261 1.802560092*


HS Index L1 W1 L1-W1 L2 W2 L2-W2 L3 W3 L3-W3 mean µ 0.003139529 0.004883312 -0.00234111 0.007224423 0.007399029 -0.00358829 0.010987321 0.016560321 -0.0003835 0.01694382

std dev σ

0.041626576 0.052306439 0.054903244 0.050844014 0.053043491 0.060138556 0.056839103 0.075086687 0.07836109 0.07903616

µ/ σ 0.07542127 0.093359677 -0.04264066 0.142089941 0.139489853 -0.05966709 0.193305674 0.220549364 -0.00489399 0.214380603

t-value 0.822748923 1.018433839 -0.4651547 1.550018256 1.521654647 -0.65089106 2.108715943*

2.405909519**

-0.05338716 2.338616278*


HS Index L1 W1 L1-W1 L2 W2 L2-W2 L3 W3 L3-W3 mean µ 0.001549842 0.005652317 -0.00159258 0.007244896 0.008710741 -0.00170818 0.010418918 0.017079045 0.004613943 0.012465102

std dev σ

0.057209259 0.066670634 0.072421961 0.059598206 0.0675249 0.07502069 0.062007913 0.091317759 0.103067005 0.098350356

µ/ σ 0.027090762 0.084779711 -0.02199027 0.121562311 0.129000435 -0.02276941 0.168025626 0.187028734 0.04476644 0.126741808

t-value 0.295525321 0.924837458 -0.23988555 1.326088252 1.40722861 -0.24838495 1.832943187*

2.040242612*

0.488344204 1.382589901

9

1-month Equal weights Unequal weights Top and Bottom

HS Index L1 W1 L1-W1 L2 W2 L2-W2 L3 W3 L3-W3 mean µ 0.009304107 0.013097572 0.0070769 0.006020672 0.014368914 0.008621366 0.005747548 0.015643403 0.018558062 -0.002914659

std dev σ

0.062953051 0.086216648 0.079502969 0.080016193 0.086298192 0.080363532 0.08348516 0.112053417 0.110719076 0.132251374

µ/ σ 0.147794373 0.151914655 0.089014288 0.075243172 0.166503076 0.107279586 0.068845145 0.139606655 0.167613951 -0.022038781

t-value 1.612246272 1.657193235 0.971031247 0.8208061 1.816334119*

1.170282116 0.751011866 1.522928811 1.828452338*

-0.240414721

*,** significant respectively at 95% and 99% level

10

[3] The higher the weight assigned for the stocks with most extreme past performance, the

more profitable the portfolios Ls and L-W are. This from another angle proves that a

contrarian profit exists and that it is possibly due to the overreaction of investors towards the

new information that is made public.

5.2 Statistical significance

When the t-values of the return distributions are also taken into account, it can be seen that

the returns of Ws not only are smaller in magnitude, but also have little statistical evidence

that they are different from zero.

On the contrary, the high positive returns of Ls and L-W are significant at 95% level or above

for 1-week holding period, and for 2-week and 3-week scenarios the significance persists

especially for the portfolios with “unequal weights” and “top and bottom” weights.

Nevertheless, the statistical significance fades away for the 1-month holding period.

5.3 Other analyses

[1] Correlation coefficient of Ws and Ls

Table III correlation coefficient between W and L 1: equal weights 2: unequal weights 3: top and bottom 1-week 0.50268 0.495293 0.316255 2-week 0.551289 0.502287 0.470522 3-week 0.635609 0.626039 0.493471 1-month 0.545747 0.508548 0.293552

Table III shows the correlation coefficient between Ws and Ls and suggests that the

coefficient becomes lower when more weights are given to extreme past winners and losers.

Overall, the correlation coefficient for case 1 and case 2 is around 0.5.

This medium positive correlation between Ws and Ls may explain the results of table II that

the long-L short-W portfolio performs well due to the diversification effect, although the sign

of the return of Ws is not consistent across different length of holding periods.

[2] Test: whether the portfolios outperform HS Index

Since the profitability of Ws is not consistent or significant from the previous analysis, the

focus here is only on Ls and the L-W strategy. In this part, I subtract HS Index return from

the portfolio returns and analyze the distribution of the difference between them. The

statistics are presented in table IV.

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Based on this table, it can be seen that with 1-week holding period loser-portfolios Ls

outperform the market, by 0.39% to 1.11% per week. At 2-week and 3-week level, only L2

and L3 outperform, due to the higher tendency of price reversion in the stocks with extreme

past returns.

The long-loser short-winner strategy cannot outperform the market according to table IV, but

its return is positive as proven in the last analysis by table II.

12

Table IV difference between portfolio return and index return

1-week Equal weights Unequal weights Top and Bottom L1 L1-W1 L2 L2-W2 L3 L3-W3

mean

0.00388291 0.000158882

0.005826223 0.003343132

0.011159255 0.007615857 standard deviation

0.024150343 0.053698812

0.024990163 0.053986673

0.045808073 0.076622181

t-value

1.746525838* 0.032140448

2.532555541** 0.6726792

2.646270123** 1.079705362

2-week

Equal weights Unequal weights Top and Bottom L1 L1-W1 L2 L2-W2 L3 L3-W3

mean

0.001743783 0.004084894

0.004259499 0.007847792


0.032104562 0.064844529

0.035834406 0.071521365

0.063907423 0.091891736

t-value

0.590019967 0.684302971

1.29121737 1.191935317

2.281223491* 1.631843985

3-week


mean

0.004102475 0.005695053

0.007160899 0.008869076


0.041199051 0.086986348

0.045557839 0.091205309

0.078773709 0.125639534

t-value

1.081682236 0.711193367

1.707439955* 1.056329141

2.141454554* 0.943732162

1-month


mean

0.003793466 -0.00328343

0.005064808 -0.003556559

0.006339296 -0.012218766 standard deviation

0.053785595 0.099143714

0.055946685 0.102504661

0.091334401 0.146814142

t-value

0.766145352 -0.36127397

0.983398617 -0.378494762

0.753958844 -0.907889373


13

[3] Portfolio beta

Table V

portfolio beta with respect to HS Index, equal weights

L1 W1 L1-W1 1-week 0.878434 0.952023 -0.07359 2-week 0.992062 0.959434 0.032628 3-week 0.919752 1.033073 -0.11332 1-month 1.072839 1.005189 0.06765

portfolio beta with respect to HS Index, unequal weights

L2 W2 L2-W2 1-week 0.834876 0.928912 -0.09404 2-week 0.941347 0.985164 -0.04382 3-week 0.879495 1.062899 -0.1834 1-month 1.044695 0.990993 0.053702

portfolio beta with respect to HS Index, top and bottom L3 W3 L3-W3 1-week 0.779047 0.81711 -0.03806 2-week 0.948372 1.082436 -0.13406 3-week 0.825955 1.25976 -0.43381 1-month 1.031656 1.044383 -0.01273

The beta of the portfolios during the ten-year sample period is calculated and presented in

table V. To some extent, it confirms the intuition that the Ls and Ws have a beta of

approximately 1 and the beta for L-W should be quite low and near zero. The extreme beta

-0.43381 for L3-W3 with 3 weeks as holding period can be regarded as an outlier due to the

extreme weights of 100% given to the stock in L3 and W3 respectively.

[4] Beta neutral adjustment on return

With the ex-post betas calculated in part [3], it is supposed that short-selling the beta amount

of HS index can hedge away the market risk contained in the portfolios. The remaining is the

risk-adjusted beta-neutral return, shown in table VI. Here again, I focus only on Ls and the L-

W strategy.

When compared with table II, table VI shows a similar result. The portfolios, whose return is

statistically significant and positive in table II, are still profitable when the return brought by

market risk is subtracted. The only exception is the return of L2 with one-month holding

period, which becomes insignificant.

14

Table VI portfolio return hedged to be beta neutral

1-week Equal weights Unequal weights Top and Bottom L1 L1-W1 L2 L2-W2 L3 L3-W3

mean

0.00388291 0.000158882

0.006453957 0.007502203


0.024150343 0.053698812

0.024427866 0.041167359

0.045261746 0.06908525

t-value

1.746525838* 0.032140448

2.882132889** 1.987967418*

2.891980623** 1.825687339*

2-week


mean

0.001768706 0.007121987

0.004443642 0.011124886


0.032102861 0.050825871

0.035751135 0.05680983

0.063871277 0.078838894

t-value

0.601015148 1.528585925

1.355884656 2.136217992*

2.319849178* 2.402706268**

3-week


mean

0.004226847 0.007420526

0.007347663 0.010703166


0.040942462 0.059244548

0.045033201 0.061113749

0.078141894 0.09516762

t-value

1.126201261 1.366343152

1.779876708* 1.910499065*

2.205553641* 1.505895151

1-month


mean

0.003115765 0.005391254

0.004648961 0.005247897

0.006044768 -0.002796242 standard deviation

0.053589776 0.07990278

0.055875887 0.083416681

0.091312658 0.132248947

t-value

0.634243792 0.73603991

0.907621925 0.686287129

0.722141196 -0.230651336


15

The consistent results of table II and table VI indicates that the contrarian profit does not

come from a higher market risk contained in the portfolios. The profit therefore should be

attributed to the firm-specific risk of the company stocks selected.

6. Conclusion

Here are some possible implications from the research of this paper for various parties in the

stock market:

[1] Traders:

Past losers empirically outperform the market index. Longing losers and closing the position

within three weeks can be used as a short-term trading strategy. Traders using the contrarian

strategy described above can construct a portfolio based on analyzing past price movements

and make a solid profit.

[2] Arbitrageurs:

The strategy of long-buying losers and short-selling winners is significantly profitable when

practiced for one week to three weeks. Even the transaction costs cannot expel the high

profits. However, the average return of this strategy is the highest with one week as holding

period, at about 0.8% to 1.5% per week, equivalent to around 52% when annualized. Another

advantage of this strategy is that it requires minimum capital, since long and short is done at

the same time.

[3] Academia:

From the academic point of view, there are some interesting findings regarding the field of

behavioral finance and risk-return relationship.

First, it can be concluded that in Hong Kong stock market the price of past winners may not

drop but that of past losers is very likely to bounce back within one month. This implies that

investors in Hong Kong tend to overreact on bad news, but not obviously overreact upon

good news. The resulted mispricing is corrected very fast, and the price reversion for past

losers majorly takes place within one week. The price reversion is then completed in less than

one month and no robust contrarian profit can be exploited after one month. In this sense, the

Hong Kong stock market is not efficient in the weakest form in very short terms, i.e. within a

month.

16

The results from the portfolios with higher weights to amplify the contrarian effect for high

rankers appear to be better and more convincing in statistical sense than the portfolios with

equal weights. This can further prove that the stocks which have greater loss will bounce

back more robustly and that the contrarian profit does exist in Hong Kong.

When this paper tries to track the source of the contrarian profit, the ex-post beta of long-

loser short-winner portfolio turns out to be near zero and the beta of Ls is about 1. When a

market-neutral hedge is conducted, the return of the hedged portfolios Ls and L-W is still

positive, at more than 1% during the holding period. It therefore suggests that the main source

of contrarian profit is the firm-specific risk of the stocks involved, not the market risk. In

other words, the contrarian strategy works, not because the portfolios possess a higher beta

with respect to the market.

Future research may be conducted in other stock markets in the world, for example in

mainland China to testify if a similar contrarian strategy works there. The return used for

ranking and sorting out past winners and losers can also be adjusted and the shorter holding

periods can be further shortened to test the market efficiency in even shorter terms.

The results of this research paper may be limited or impaired by the two assumptions made

during the simulation. The momentum/contrarian behavior in all the stocks listed in Hong

Kong may not be exactly the same as that of the HS Index constituents. Besides, the analysis

on the source of contrarian profit can be conducted more rigorous, for instance, to use the

beta of month in which the ranking procedure is done, instead of the ex-post realized beta.

Overall, the author of this paper believes that the existence of contrarian profit in Hong Kong

stock market in very short terms is strongly proven by the research results in this paper.

17

Appendix 1

0

500

1000

1500

2000

2500

3000

2/1/

2004

8/1/

2004

2/1/

2005

8/1/

2005

2/1/

2006

8/1/

2006

2/1/

2007

8/1/

2007

2/1/

2008

8/1/

2008

2/1/

2009

8/1/

2009

2/1/

2010

8/1/

2010

2/1/

2011

8/1/

2011

2/1/

2012

8/1/

2012

2/1/

2013

8/1/

2013

equal weights, 1-week

losers

winners

HS index

L-W

0

500

1000

1500

2000

2500

3000

2/1/

2004

8/1/

2004

2/1/

2005

8/1/

2005

2/1/

2006

8/1/

2006

2/1/

2007

8/1/

2007

2/1/

2008

8/1/

2008

2/1/

2009

8/1/

2009

2/1/

2010

8/1/

2010

2/1/

2011

8/1/

2011

2/1/

2012

8/1/

2012

2/1/

2013

8/1/

2013


losers

winners

HS index

L-W

0

500

1000

1500

2000

2500


losers

winners

HS index

L-W

18

0

500

1000

1500

2000

2500

3000

35002/

1/20

04

8/1/

2004

2/1/

2005

8/1/

2005

2/1/

2006

8/1/

2006

2/1/

2007

8/1/

2007

2/1/

2008

8/1/

2008

2/1/

2009

8/1/

2009

2/1/

2010

8/1/

2010

2/1/

2011

8/1/

2011

2/1/

2012

8/1/

2012

2/1/

2013

8/1/

2013

equal weights, 1-month

losers

winners

HS index

L-W

0

500

1000

1500

2000

2500

3000

3500

2/1/

2004

8/1/

2004

2/1/

2005

8/1/

2005

2/1/

2006

8/1/

2006

2/1/

2007

8/1/

2007

2/1/

2008

8/1/

2008

2/1/

2009

8/1/

2009

2/1/

2010

8/1/

2010

2/1/

2011

8/1/

2011

2/1/

2012

8/1/

2012

2/1/

2013

8/1/

2013

unequal weights, 1-week

losers

winners

HS index

L-W

0500

10001500200025003000350040004500

2/1/

2004

8/1/

2004

2/1/

2005

8/1/

2005

2/1/

2006

8/1/

2006

2/1/

2007

8/1/

2007

2/1/

2008

8/1/

2008

2/1/

2009

8/1/

2009

2/1/

2010

8/1/

2010

2/1/

2011

8/1/

2011

2/1/

2012

8/1/

2012

2/1/

2013

8/1/

2013


losers

winners

HS index

L-W

19

Extreme case: only the top winner and top loser (top and bottom)

0

500

1000

1500

2000

2500

3000

3500


losers

winners

HS index

L-W

0500

1000150020002500300035004000

2/1/

2004

8/1/

2004

2/1/

2005

8/1/

2005

2/1/

2006

8/1/

2006

2/1/

2007

8/1/

2007

2/1/

2008

8/1/

2008

2/1/

2009

8/1/

2009

2/1/

2010

8/1/

2010

2/1/

2011

8/1/

2011

2/1/

2012

8/1/

2012

2/1/

2013

8/1/

2013

unequal weights, 1-month

losers

winners

HS index

L-W

0

1000

2000

3000

4000

5000

6000

2/1/

2004

8/1/

2004

2/1/

2005

8/1/

2005

2/1/

2006

8/1/

2006

2/1/

2007

8/1/

2007

2/1/

2008

8/1/

2008

2/1/

2009

8/1/

2009

2/1/

2010

8/1/

2010

2/1/

2011

8/1/

2011

2/1/

2012

8/1/

2012

2/1/

2013

8/1/

2013

top and bottom, 1-week

losers

winners

HS index

L-W

20

0100020003000400050006000700080009000

100002/

1/20

04

8/1/

2004

2/1/

2005

8/1/

2005

2/1/

2006

8/1/

2006

2/1/

2007

8/1/

2007

2/1/

2008

8/1/

2008

2/1/

2009

8/1/

2009

2/1/

2010

8/1/

2010

2/1/

2011

8/1/

2011

2/1/

2012

8/1/

2012

2/1/

2013

8/1/

2013


losers

winners

HS index

L-W

0

1000

2000

3000

4000

5000

6000

7000


losers

winners

HS index

L-W

0500

100015002000250030003500400045005000

2/1/

2004

8/1/

2004

2/1/

2005

8/1/

2005

2/1/

2006

8/1/

2006

2/1/

2007

8/1/

2007

2/1/

2008

8/1/

2008

2/1/

2009

8/1/

2009

2/1/

2010

8/1/

2010

2/1/

2011

8/1/

2011

2/1/

2012

8/1/

2012

2/1/

2013

8/1/

2013

top and bottom, 1-month

losers

winners

HS index

L-W

21

0

5

10

15

20

25

30

-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25

Series: L1__3_WEEKSample 2004M02 2013M12Observations 119

Mean 0.005652Median 0.015869Maximum 0.240477Minimum -0.151401Std. Dev. 0.066671Skewness 0.127670Kurtosis 4.049828

Jarque-Bera 5.788053Probability 0.055353

0

2

4

6

8

10

12

14

-0.10 -0.05 0.00 0.05 0.10 0.15




Appendix 2

Histograms

Loser-portfolio with equal weights (L1)

0

4

8

12

16

20

24

-0.10 -0.05 0.00 0.05 0.10




22

Winner-portfolio with equal weights (W1)

0

4

8

12

16

20

-0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25

Series: W1__1_WEEKSample 2004M02 2013M12Observations 119



0

4

8

12

16

20

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10


Mean -0.002341Median 0.004030Maximum 0.135628Minimum -0.204464Std. Dev. 0.054903Skewness -0.585586Kurtosis 4.059921


0

5

10

15

20

25

-0.2 -0.1 0.0 0.1 0.2 0.3

Series: L1__1_MONTHSample 2004M02 2013M12Observations 119



23

0

4

8

12

16

20

24

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20


Mean -0.001593Median -0.002146Maximum 0.194040Minimum -0.215881Std. Dev. 0.072422Skewness 0.017307Kurtosis 3.524355


0

4

8

12

16

20

24

-0.2 -0.1 0.0 0.1 0.2 0.3

Series: W1__1_MONTHSample 2004M02 2013M12Observations 119



Long-loser short-winner portfolio with equal weights (L1-W1)

0

4

8

12

16

20

24

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10

Series: L1_W1__1_WEEKSample 2004M02 2013M12Observations 119

Mean 0.003960Median 0.006635Maximum 0.122846Minimum -0.239642Std. Dev. 0.041406Skewness -1.414459Kurtosis 12.32406


24

0

4

8

12

16

20

-0.10 -0.05 0.00 0.05 0.10 0.15 0.20




0

4

8

12

16

20

-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15




0

4

8

12

16

20

24

28

-0.2 -0.1 0.0 0.1 0.2 0.3

Series: L1_W1__1_MONTHSample 2004M02 2013M12Observations 119

Mean 0.006021Median -3.42e-05Maximum 0.317670Minimum -0.232101Std. Dev. 0.080016Skewness 0.186759Kurtosis 4.853767


25

Loser-portfolio with unequal weights (L2)

0

4

8

12

16

20

-0.10 -0.05 0.00 0.05 0.10




0

2

4

6

8

10

12

14

-0.10 -0.05 0.00 0.05 0.10 0.15




0

4

8

12

16

20

24

28

-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25




26

0

4

8

12

16

20

24

-0.2 -0.1 0.0 0.1 0.2 0.3




Winner-portfolio with unequal weights (W2)

0

2

4

6

8

10

12

14

16

-0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25




0

4

8

12

16

20

24

-0.3 -0.2 -0.1 0.0 0.1


Mean -0.003588Median -0.000281Maximum 0.136072Minimum -0.287735Std. Dev. 0.060139Skewness -0.911349Kurtosis 6.291065


27

0

4

8

12

16

20

24

-0.2 -0.1 0.0 0.1 0.2


Mean -0.001708Median -0.003873Maximum 0.214081Minimum -0.213191Std. Dev. 0.075021Skewness -0.081624Kurtosis 3.381489


0

4

8

12

16

20

24

-0.2 -0.1 0.0 0.1 0.2




Long-loser short-winner portfolio with unequal weights (L2-W2)

0

4

8

12

16

20

24

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10




28

0

4

8

12

16

20

24

28

32

-0.1 0.0 0.1 0.2 0.3




0

2

4

6

8

10

12

14

-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15




0

4

8

12

16

20

-0.2 -0.1 0.0 0.1 0.2 0.3




29

Loser-portfolio with 100% weight for “top and bottom” stock (L3)

0

4

8

12

16

20

-0.10 -0.05 0.00 0.05 0.10 0.15 0.20




0

5

10

15

20

25

-0.1 0.0 0.1 0.2 0.3




0

4

8

12

16

20

-0.2 -0.1 0.0 0.1 0.2 0.3




30

0

4

8

12

16

20

-0.2 -0.1 0.0 0.1 0.2 0.3 0.4




Winner-portfolio with 100% weight for “top and bottom” stock (W3)

0

5

10

15

20

25

30

-0.1 0.0 0.1 0.2 0.3 0.4




0

4

8

12

16

20

-0.2 -0.1 0.0 0.1 0.2




31

0

4

8

12

16

20

24

-0.2 -0.1 0.0 0.1 0.2 0.3




0

2

4

6

8

10

12

14

16

-0.2 -0.1 0.0 0.1 0.2 0.3 0.4




Long-loser short-winner portfolio with 100% weight for “top and bottom” stock (L3-W3)

0

4

8

12

16

20

24

-0.3 -0.2 -0.1 0.0 0.1 0.2




32

0

4

8

12

16

20

-0.1 0.0 0.1 0.2




0

2

4

6

8

10

12

14

16

-0.2 -0.1 0.0 0.1 0.2




0

2

4

6

8

10

12

14

16

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3




33

References

[1] Crombez, John (2001). Momentum, rational agents and efficient markets. Journal of

psychology and financial markets : a publication of the Institute of Psychology and Markets

and LEA. Vol. 2, 4: p. 190-200.

[2] Hong, H., Lim, T., and Stein, J. C. (2000). Bad news travels slowly: size, analyst coverage and the profitability of momentum strategies. Journal of Finance. 55(1): p. 265–295.

[3] Doukas, J. A., and McKnight, P. J. (2005). European momentum strategies, information

diffusion, and investor conservatism. European Financial Management 11(3): p. 313–338.

[4] Bandarchuk, P. and Hilscher, J. (2013). Sources of Momentum Profits: Evidence on the

Irrelevance of Characteristics. Review of Finance (2013) 17: p. 809–845.

[5] Carhart, M. (1997). On Persistence in Mutual Fund Performance. Journal of Finance 52

(1): p. 57–82.

[6] Czaja, M., Kaufmann, P. and Scholz, H. (2012). Enhancing the profitability of earnings

momentum strategies: the role of price momentum, information diffusion and earnings

uncertainty. Journal of Investment Strategies 2(4): p. 3–57.

[7] Hens, T. (2010). Financial economics: a concise introduction to classical and behavioral

finance. Berlin; London: Springer, c2010: p. 247.

[8] Shen, Q., Szakmary, A.C. and Sharma S.C. (2007). An Examination of Momentum

Strategies in Commodity Futures Markets. The Journal of Futures Markets. Vol. 27(3): p.

227-256

[9] Chan, K., Hameed, A. and Tong, W. (2000). Profitability of Momentum Strategies in the

International Equity Markets. The Journal of Financial and Quantitative Analysis, 35(2):

p.153-172

[10] Brown Consultancy Services. Is there a lock-in period for investment in mutual funds?

[Web log post]. Retrieved April 27, 2014, from http://www.brownconsultancy.com/mffaq-

lockinperiod.aspx

[11] Hang Seng Indexes Company Limited. Total Return Index Series- Methodology.

Retrieved April 27, 2014, from http://www.hsi.com.hk/HSI-Net/HSI-Net

34

http://www.brownconsultancy.com/mffaq-lockinperiod.aspx

http://www.brownconsultancy.com/mffaq-lockinperiod.aspx

http://www.hsi.com.hk/HSI-Net/HSI-Net

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