Contrarian Strategy and Herding

Behaviour in the Chinese Stock Market

Qiwei Chen*

Xiuping Hua†

Ying Jiang†

* Department of Economics and Finance, Brunel University, UK.

† Nottingham University Business School China

This version: June, 2015

Correspondence email: ying.jiang@nottingham.edu.cn

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Abstract

This paper investigates the profitability of several types of zero-cost price momentum

and contrarian strategies in the Chinese stock market for the 1994-2013 period.

Several distinct features of Chinese market are documented. We find that contrarian

strategies that use Jegadeesh and Titman's (1993) method with weekly frequency are

profitable. However, investment strategies based on the “nearness” to of 52-week high

or the recency of the 52-week high are not profitable. Our analysis also shows that

contrarian profits are higher during the crisis period of 2008-2012. In addition, the

return reversal of the winner and loser portfolios suggests that contrarian profits can

be attributed to overreaction. Finally, we also find evidence of herding behaviour in

the Chinese market; and the degree of herding behaviour is positively correlated with

the profits of contrarian trading strategies.

JEL Classification: G12; G14; G15; G32

Keywords: Momentum; Contrarian; Herding; Overreaction; China

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1. Introduction

Over the past few decades, a vast literature has extensively examined the

profitability of momentum and contrarian trading strategies in the stock market. Since

the seminal paper of Jegadeesh and Titman's (1993, henceforth, JT), many studies

have found that a strategy that holds a long position of past winners and a short

position of loser stocks (the “momentum” strategy) often beats the market.1 Other

studies have however found that buying losers and short selling winners (the

“contrarian” strategy) may also be profitable, as prices tend to revert after periods of

extremely high and low returns (De Bondt and Thaler 1985, 1987; Chopra et al, 1992;

Baytas and Cakici 1999). Studies have found that contrarian profits can be explained

by a three-factor asset pricing model (Fama and French, 1996), as well as by the bid-

ask spread bias (Conrad and Kaul, 1993), and liquidity (Cox and Peterson 1994).

Turning to the momentum profits, they tend to be associated with several factors

including the firm size (see Clare and Thomas 1995, and Zarowin 1990, Hong, Lim

and Stein 2000), and trading volume (Connolly and Stivers 2003).

Another strandstream of the literature explains the profitability of momentum and

contrarian strategies by price under-/overreaction. According to Barberis et al. (1998)

stock prices underreact to earnings announcements, and overreact to consistent

patterns of good or bad news. Hong and Stein (1999) model a market populated by

two groups of agents, the "newswatchers" and "momentum traders". The market

initially underreacts to firm-specific news because information does not always reach

newswatchers instantly (hence the profitability of the momentum strategies). This

initial underreaction is usually followed by overreaction, as momentum traders seek to

make a profit by trend chasing, which inevitably causes prices to overshoot their long-

1 The momentum profit is robust to out-of-sample analysis (Jegadeesh and Titman 2001; Grinffin et al, 2003; Antoniou et al, 2007), and also applies to asset classes other than equities (Miffre and Rallis 2007; Jostova et al, 2010).

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run equilibrium values (hence the profitability of contrarian strategies). Therefore, it

is possible to have both profitable momentum and contrarian strategies operating

overfor different formation and holding periods.

Since JT, alternative strategies related to the price momentum have been

proposed. George and Hwang (2004) (GH henceforth) examine a strategy that

consists of buying stocks with a high current price to 52-week high price ratio. They

find that those stocks with a high ratio (i.e. a price close to the 52-week high)

outperform those with a low ratio. More recently, Bhootra and Hur (2013) (BH

henceforth) consider a trading strategy based on the recency of the 52-week high (i.e.

the number of days passed since the 52-week high occurred). It is found for the US

market that the momentum strategy based on the recency of the 52-week high is

profitable, and in addition it outperforms GH's momentum strategy. GH and BH argue

that the profitability of their strategies is due to the existence of an anchoring bias

(Tversky and Kahneman 1974) and a recency bias respectively. In the presence of

such biases, stock prices are not adjusted to their fundamental values, which gives rise

to momentum/contrarian profit.

In this paper, we investigate price momentum and contrarian profits in the

Chinese stock market usingfor the three methods discussed above (JT, GH and BH)

for the period 1994-2013. To the best of our knowledge, this is the first paper to

conduct a comparison of these trading strategies for the Chinese market. Given that

JT, GH, and BH momentum strategies are proved to be profitable in the US, it would

be interesting to know whether they are equally profitable in an emerging market with

a different composition of investors (i.e. fewer institutional investors, more individual

investors), market size, liquidity etc.

A second contribution of this paper is to investigate the association between the

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profitability of momentum/contrarian strategies and herding behaviour. Herding is a

well-established phenomenon (Grinblatt et al. 1995), including in the Chinese market

(Tan et al. 2008; Yao et al. 2014). Because of herding, investors are trading in the

same direction, which can lead to price overreaction (Avery and Zemsky 1998;

DeLong et al. 1990). This, in turn, means that herding may indirectly lead to the

profitability of momentum and contrarian strategies. In this paper we first test for

herding using the approach of Chiang and Zheng (2010), before examining whether

the profitability of the trading strategies is affected by the magnitude of herding.

Our results can be summarized as follows. First, the JT, BH, and GH momentum

strategies are all not profitable. This finding is different from prior studies for

developed markets.2 Second, the only profitable contrarian strategy is one that ranks

stocks using the JT approach, based onusing the weekly frequency. The profits of that

strategy are enhanced during the financial crisis period of 2008-2012. These results

are robust to size and bid-ask bounce effects. Third, we confirm the existence of

herding in the Chinese stock market. Herding is particularly strong for stocks with

amongst poorly performing stocks. Finally, we find the contrarian profits are higher

during high herding periods, which indicates that there is an association between

contrarian profits and herding behaviour.

This paper is organized as follows. Section 2 introduces the data and

methodology. Section 3 presents the results for the profitability of the trading

strategies. Section 4 shows the robustness of the results to Fama-MacBeth

regressions. Section 5 tests for herding behaviour, and subsequently shows the link

between contrarian profits and herding. The final section concludes.

2 For example, Karathanasis et al. (2010) document significant momentum profit for eight out of thirteen European countries including Austria, France, Germany, Greece, Italy, Netherland, Switzerland and the UK, which is broadly consistent with the results of Liew and Vassalou (2000).

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2. Data and Methodology

2.1 Data

Our data sample consists of all listed "A" stocks of the Shanghai Stock Exchange

and Shenzhen Stock Exchange over the period January 1st 1994 to December 31st

2013.3 The number of stocks increases from 167 at the beginning of the sample period

to 2467 at the end of the sample period. In order not to be affected by survivor biases,

we do not require a stock to exist during the entire holding period (Brown et al. 1995).

We are therefore using an unbalanced panel, and the number of stocks varies over

time. Firm-level data is obtained from the Wind Information Co. Ltd database, and

these include monthly/weekly/daily split and dividend adjusted closing prices, day-

high prices, monthly/weekly market values and yearly book-to-market ratios.

Logarithmic rate of returns are calculated using the closing prices for each data

frequency.

2.2 Contrarian and momentum trading strategies

We examine the profitability of three types of zero-cost contrarian/momentum

trading strategies. These are the strategies proposed by Jagedeesh and Titman (1993)

[JT], George and Hwang (2004) [GH], and Bhootra and Hur’s (2013) [BH].

2.2.1 Jagedeesh and Titman (1993)’s strategy

We first consider the approach of JT. For each period t, stocks are ranked in

ascending order according to their past performance (i.e. the cumulative return) over

the previous J periods. Based on that ranking, two equally weighted portfolios are

formed: stocks ranked in the top 10% are assigned to the winner portfolio, and stocks

in bottom 10% are assigned to the loser portfolio. We form a zero-cost momentum

portfolio by longing the winner portfolio and shorting the loser portfolio, while 33The trading rules which are set by China Securities Regulatory Commission are identical for Shanghai and Shenzhen Stock Exchanges, but the listing rules are different. The listing requirement of total share capital for Shanghai Stock Exchange is higher than that for Shenzhen Stock Exchange. However, it is important to include big, medium and small companies to draw implications for overall market.

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holding the position for K periods. We call JT momentum strategy the strategy of

investing in a momentum portfolio using the JT ranking method. Likewise, a

contrarian portfolio can be formed by longing the loser portfolio and shorting the

winner portfolio. We call JT contrarian strategy the strategy of investing in a

contrarian portfolio using the JT ranking method. For each period t of a (J, K)

strategy, the returns to winners/losers are calculated as the equally weighted average

of the period t returns from K separate winner/loser portfolios, each formed in one of

the J consecutive prior periods t – J to t – 1. The return to the overall strategy is the

difference between the return to winners and to losers in period t.

We use both the conventional monthly frequency as well as the weekly frequency.

For monthly data, the portfolios are formed at the end of each month; and, for weekly

data, the portfolios are formed each Wednesday (if the day is a non-trading day, then

the next trading day is used) in order to avoid the weekday seasonalities (e.g. Monday

effect or Friday effect). For the length of the formation and holding periods, we

choose 1, 3, 6 months for the monthly frequency, and 1, 2, 3 weeks for the weekly

frequency. We impose a gap between the formation and the holding periods to

mitigate the bid-ask bounce effects. We impose a one-month gap and a one-week gap

for the monthly frequency and weekly frequency respectively.

2.2.2 George and Hwang (2004)’s strategy

We then estimate the profitability of the trading strategies proposed by GH, which

are based on the “nearness” to the 52-week high price computed over a specific

period. The nearness is defined as the ratio of the current stock price to the 52-week

high price. This ratio is written as P i ,t −1

Highi ,t−1 , where Pi ,t−1 is the price of stock i at the

end of period t-1 and Highi ,t−1 is the highest price of stock i during the last 52 weeks.

A high value indicates that the current price is close to its 52-week high. Stocks are

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ranked based on the nearness ratio, and, similar to JT, winner and loser portfolios are

formed (with top 10% and bottom 10% stocks ranked on the nearness). We then

calculate, for weekly and monthly frequency, the return of GH momentum strategies

(long the winner portfolio, short the loser) and GH contrarian strategies (long the loser

portfolio, short the winner).

2.2.3 Bhootra and Hur’s (2013)’s strategy

Finally, we consider the strategies proposed by BH (2013), which also make use of

the 52-week high. However, and unlike GH, stocks are ranked on the basis of the

“recency ratio” or RR, which is calculated as follows:

RR=1−number of dayssince 52 weeks high price364

The recency ratio is between 0 and 1 and is inversely related to the number of days

that have passed since the stock hit the 52-week high price. The ratio is high for

stocks that have recently reached their 52-week high and low for stocks that whose

52-week price occurred many months ago. Similar to GH and JT, we form the loser

and winner portfolios and calculate the return of momentum and contrarian strategies.

The table in Appendix summarizes the ranking criteria for our three strategies. It

should be noted that the analysis of the profitability of BH and GH strategies can also

provide information about the sources of momentum profits.

2.3 Test of herding

One of the objectives of this paper is to investigate some of the linkages between

herding and the profitability of trading strategies. In the presence of herding, prices

are temporarily pushed away from their fundamentals, i.e. they overreact. For that

reason, it is believed that herding may indirectly lead to both momentum and

contrarian profits for different time windows, as prices initially deviate from their

fundamental value before ultimately mean revertsing. According to Hong and Stein

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(1999), these price fluctuations can be explained by the existence of trend chasers

who try to make a profit by buying stocks with recent positive returns and short

selling the losing ones.

We first test for herding using the herding detection model of Chiang and Zheng

(2010), which is a generalised form of the model initially proposed by Chang et al.

(2000). In the context of this study, herding is defined as a situation where investors

mimic financial gurus or follow the activities of successful traders rather than relying

on their own information (see Chiang and Zheng, 2010).

Individual assets differ in their sensitivity to the market return, and rational asset

pricing models predict a linear relationship between the market return and the cross-

section dispersion of stock returns. In the case of herding, however, investors may

suppress their own beliefs and follow the market consensus during periods of large

market movements. Therefore, herding predicts that the cross-sectional dispersion of

returns should decrease or increase less than proportionally with the market return, as

investors are drawn to the consensus of the market (Christie and Huang, 1995).

Herding can be detected using the following regression (see, Chiang and Zheng,

2010):

CSAD t=γ0+γ1 Rm,t +γ 2|Rm,t|+γ 3 Rm ,t2 +εt (1)

where CSAD, the cross-sectional absolute deviation, is a measure of return

dispersion:

CSAD t=1N ∑

i=1

N

|R i ,t−Rm,t| (2)

where N is the number of stocks included in the portfolio, and Ri , t is the observed

stock return of stock i for period t. Rm, t is the market return , i.e. the cross-sectional

average of stock returns in the portfolio for period t. We estimate equation (1) with a

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Newey-West consistent estimator (Newey West 1987). A negative coefficient for γ3

shows that the dispersion of returns is increasinges at a decreasing rate with the

market return, which is conventionally interpreted as evidence of herding.

We estimate model (1) using the whole sample to test for market-wide herding. We

also estimate the model separately for the winner and loser portfolios to capture

possible differences in the degree of herding in the two sub-groups. As is standard in

the literature, the herding-detection model is estimated at a in daily frequency, and the

portfolios are re-balanced each week (on Wednesdays). Each Wednesday we take the

daily returns of the winner and loser portfolios selected for that week, and switch to a

new set of portfolios’ daily returns the following Wednesday, so as to obtain a

continuous time series of daily returns.

In order to investigate the relationship between momentum/contrarian profits and

herding, we divide the sample into high and low herding periods. Because herding

behaviour results in abnormally low stock dispersion during periods of large price

movements, we propose to define high herding periods as periods where both the

value of Rm , t2 is above the 30% (or 50%) percentile and CSAD t is below the 30% (or

50%) percentile. These are periods during which return dispersion is low in spite of

the large absolute market return. Likewise, low herding periods are periods where

both Rm, t2 is above 30% (or 50%) percentile, and CSAD t is above 30% (or 50%)

percentile. Note that the 30% and 50% cutoff points are somewhat arbitrary, but our

results would still hold using alternative cutoff points. We then compare

momentum/contrarian profits in high and low herding periods to detect a possible

association between the trading strategies’ profits and herding.

3. Profitability of trading strategies

In this section we report the profitability of the JT, GH, and BH trading strategies,

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first for monthly returns and subsequently for weekly returns.

3.1 Trading strategies using monthly returns

Table 1 shows the profitability of the trading strategies using the monthly

frequency, for 1, 3 and 6 months holding periods. For JT, we consider formation

periods of J=1, 3, and 6 months (JT-1, JT-3, JT-6), as explained above. Portfolios for

the GH and BH strategies are based on the end-of-month nearness and recency ratios,

respectively. Table 1 reveals that none of the strategies (JT, GH and BH) generates

statistically significant momentum or contrarian profits using the monthly frequency.

For instance, for strategies with 6-month holding period, the momentum profit for BH

is -0.0002, and that for GH is 0.0029, both insignificant. This findings contrast with

prior studies showing the profitability of momentum strategies on the US market.

However, our results are consistent with Chen et al. (2012), which find no momentum

profits for the Chinese stock market for the monthly frequency. This suggests that

there is no price underreaction.

[Insert Table 1 here]

Table 1 also shows the returns for the winner and loser portfolios separately. BH

(2013) find that the GH momentum profit can be attributed to the extremely low loser

portfolio returns, while with the recency ratio measure, the winners contribute more to

the overall raw momentum profits. In our sample, however, we find no significant

difference between winner and loser portfolios for all momentum strategies.

3.2 Trading strategies using weekly returns

Next, we examine the profitability of contrarian and momentum strategies for the

weekly frequency of a week. Table 2 shows the average weekly returns of the JT, GH,

and BH strategies for 1-, 2-, and 3-week holding periods. GH and BH’s portfolios are

formed based on the ranking of defined ratios on each Wednesday, while portfolios

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with JT measure are formed based on the stocks past performance for the previous

one week, two weeks and three weeks.

[Insert Table 2 here]

We report from Table 2 onwards the profit of the contrarian strategies, i.e. loser

minus winner. Similar to the results with monthly frequencyAs with our results for

monthly frequency, we find no significant profits for both the GH and BH strategies.

For investors in the Chinese market, neither the nearness ratio nor the recency ratio

can be used as anchor to generate trading profits. We however find a significant

contrarian profit for the JT strategy, for all holding periods. For example, with a (3,3)

strategy, the difference between loser and winner portfolios is 0.27% per week

(equivalent to roughly 1.20% per month and 14.04% per annum). The results are

robust to risk-adjusted returns that are calculated by the new Fama-French five-factors

model (Fama and French 2015).4

Table 2 also shows the average returns of winner and loser portfolios. Taken

separetelyseparately, neither the winner nor the loser portfolios have significant

profits. It is however important to note that, for the JT strategies, the average

magnitude of the mean reversal is smaller for winners than for losers. For the (3,3) JT

strategy, for instance, the mean reversal is -0.0007 for winner stocks and 0.0020 for

loser stocks (corresponding to 10.4% per annum). This suggests that the JT contrarian

profits can be for the most part attributable to the short-term reversal of the loser

portfolio.

We also examine separately the crisis period of 2008-2012, to assess how the

market downturn has affected the profitability of contrarian strategies.5 Table 3 shows

4The risk-adjusted return is the corresponding constant from the Fama-French five factor model.5 We also test the sub-period of 1994-2007. The results are similar to those of whole sample period but with smaller contrarian profits. Results are available upon request.

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that, in general, contrarian profits are higher during the financial crisis than for the

whole sample period. For instance, the most profitable contrarian strategy (i.e. JT

(3,2)) can achieve a profit 0.45% per week, which is equivalent to 1.8% a month and

25.2% a year. For strategies with a holding period of two to three weeks, the average

weekly return of the winners groupportfolio is about -0.5%, while loser portfolios are

relatively stable with weekly returns around -0.1% to 0.2%. In other words, the

contrarian profit during this period can be largely attributed to price reversal of

winner groupsportfolios. A possible explanation is that, during a downturn, investors

have more incentive to realize profit.6 The winner stocks are therefore confronted with

more selling pressure, resulting in large mean reversals. As people are more reluctant

to realize losses during a downturn, loser groups will face less selling pressure, and a

smaller price drop is observed. This leads to the profitability of contrarian strategies7.

The increased profitability of contrarian strategies during the financial crisis suggests

that contrarian strategies can act as a shelter during bear markets provided that short

selling would beis allowed.

[Insert Table 3 here]

With regards to the profitability of the contrarian strategies, it is important to note

that securities margin trading has been allowed since 2010, when the China Shanghai-

Shenzhen 200 Stock Index Futures was introduced. However, it is important to note

that short-selling in China still faces significant restrictions, and has not been widely

permitted yet for individual stocks.8 Given existing regulations, it is at the moment

difficult for investors to short sell stock on the Chinese stock markets. Our results

therefore suggest that to further relax restrictions of short-selling may benefit

6 The report on the structure and behaviour of investors in the Shanghai Stock Market (2013) documents a disposition bias, which means investors tend to sell winners too soon and ride too long with loser stocks.. 7 This can also be due to the convert of state support, according to The Economist (The Economist, 2011)8 There are very strict trading rules. For example, traders have to borrow stocks from the brokers in advance with high margin (50%-130% of the trading amount) then sell them to the market. Also, the selling price should be higher than previous closing price. etc.

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investors by mitigating the impact of bear markets.

It is also important to check that our results are not driven by seasonal factors.

Previous studies have found that contrarian strategies tend to earn positive returns in

January, and momentum strategies are typically less profitable during that month (JT

1993; GH 2004; Yao 2012). We therefore investigate whether the January seasonality

can explain our results. Table 4 shows the profitability for the trading strategies

excluding January (panel A), and for January only (panel B).9 Unlike studies focusing

on the US market (e.g. JT 1993; GH 2004), we find no evidence of a seasonality in

January. The difference between the performances of contrarian strategies outside

January and those of all calendar month is non-significant. This finding contradicts

the argument that contrarian profits can be attributed entirely to the January effect in

the Chinese market (Yao 2012). In our view, the absence of capital gain tax for the

profits achieved in the Chinese stock market may explaincould be the reason to

explain why there is no bounce back effect in January.10

[Insert Table 4 here]

In order to validate the hypothesis that the contrarian profits are due to

overreaction, we follow Jagadeesh and Titman (2001) and we calculate the post-

holding period performance of the contrarian portfolios over 156 weeks. Figure 1

shows that the contrarian profits reach a maximum at week 3, then they sharply

decrease and stabilize around week 24. After that, the contrarian profits remain low

and close to zero until week 156. This profit reversal pattern suggests that there is

indeed overreaction in prices during the formation period, which leads to contrarian

9We only report JT’s method since it’s only profitable strategy. We also investigate other possible calendar month seasonality as well, such as October. No seasonality has been found.10According to tax-selling hypothesis, investors sell stocks that made losses before year-end to offset the capital gain made elsewhere to lower the tax liability. The prices were depressed due to the selling pressure. While due to the release of the selling pressure, the prices rebound in January, resulting in large January returns (Branch 1977; Roll 1983; Griffiths and White 1993 ). Whether the absence of capital gain tax could explain the absence of seasonality in January could be tested with viability of "tax-loss selling", but we would not include the test here.

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profits.

[Insert Figure 1 here]

Overall, this section has shown that the only profitable strategies are the JT

contrarian strategies that use weekly returns, which we explain by short term

overreaction. The profitability of these strategies is larger during the 2008-2012

period, and cannot be attributed to the seasonal effects.

4. Fama-MacBeth regressions

In this section we provide additional evidence about the relative performances of

the JT, GH, and BH trading strategies. Following GH (2004), we use Fama-Macbeth

(1973) type of cross-sectional regressions, which control for the effects of firm size

and bid-ask bounce. Because we found that the trading strategies based using the

monthly frequency are not profitable, we choose to focus on weekly returns.

Furthermore, having established that the (3,3) JT strategy is the most profitable, we

restrict our attention to (3,3) strategies, i.e. those with a 3-week formation period and

3-week holding period. For each week t, the following cross-section regression model

is estimated to compare the profitability of the trading strategies:

Ri , t=b0 kt+b1kt Ri , t−1+b2 kt ¿¿ i , t−1+b3 kt JTH i ,t−k+b4 kt JTLi , t−k+b5kt BHH i ,t−k+b6 kt BHLi , t−k+b7 kt GHH i ,t−k+b8 kt GHLi , t−k+εi ,t ¿

(3)

The dependent variable is the week t return of stock i, Ri , t. The control variable

¿¿ i ,t−1¿ is the market capitalisation of stock i in week t-1. We control for the

previous week's return Ri , t−1 to account for the bid-ask bounce on the coefficient

estimate (see JT 1993, Lo and MacKinlay 1990). JTH (JTL) is a dummy variable that

equals 1 if the past 3-week return for stock i is ranked in the top (bottom) 10% in

week t-k (we use three weeks holding period, then k= 2 to 4 to skip a week between

formation and holding periods), and 0 otherwise. BHH (BHL) is a dummy variable

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that equals 1 if stock i is ranked in the top (bottom) 10% by the recency ratio in week

t-k, and 0 otherwise. Similarly GHH (GHL) takes the value of 1 if stock i is ranked in

the top (bottom) 10% by the nearness to the 52-week high prices. According to Fama

(1976), b0 can be interpreted as the return to a neutral portfolio that has hedged out the

effects of size, bid-ask bounce and momentum identified by all the three strategies

involved. Furthermore, b3−b4 (b5−b6 , b7−b8 ) can be interpreted as the return in

excess of b0 that can be earned by taking a long position in a pure JT (BH, GH)

winner portfolio and short position in a pure JT (BH, GH) loser portfolio if short

selling would be allowed in the Chinese market.

For each week t, three cross-sectional regressions (for k=2 to k=4) are estimated.

The coefficients of three regressions are averaged each week. The time-series

averages of the week-by-week estimates are reported in Table 5 for raw and risk-

adjusted returns. The raw returns of the winner and loser portfolios are the time-series

average of the corresponding weekly returns. The risk-adjusted returns are the

intercepts from a Fama-French five-factor model.

[Insert Table 5 here]

For raw returns, the coefficients of GH and BH strategies are both statistically

insignificant. For JT, we find a significant coefficient of 0.0026, implying that a self-

financing JT strategy yields 0.26% per week. The results with risk-adjusted returns

are broadly similar to those with raw returns. In general, the results of Fama-MacBeth

cross-sectional regressions, which adjust size and bid-ask bounce effects, confirm the

superior profitability of contrarian JT strategies compared to GH and BH strategies in

the Chinese stock market.

5. Herding Analysis

In this section, we report the results of the herding analysis from model (1). Panel

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A of Table 6 shows the estimation results for the whole sample period 1994-2013. For

the aggregate market, the herding parameter γ3 is negative and statistically significant,

indicating that herding behaviour exists in the Chinese stock markets. These results

are consistent with previous studies showing a propensity for herding behaviour in

Chinese stock markets (Tan et al. 2008, Yao et al. 2014 etc.). Herding behaviour in

Chinese markets can be explained by the fact that investors rely extensively on

analysts' recommendation due to the unreliability of Chinese corporate financial data

(Kang et al. 2002), and by the large proportion of positive feedback traders among

Chinese investors, who buy shares when the market is booming and sell when the

market is declining. Another possible explanation is collectivism. Chiang and Zheng

(2010) indeed show that herding as being more prevalent in the culturally collectivist

sample Asian countries (including China) compared to the relatively individualistic

Latin American countries.11

[Insert Table 6 here]

Table 6 also shows the values of γ3 separately for the winner and loser portfolios.

The winner and loser portfolios are formed using the JT strategy (i.e. based on past

performance), considering 1-week, 2-week, and 3-week formation periods. We focus

on JT strategies because both GH and BH strategies are unprofitable (see above). We

find that evidence of herding for the loser portfolio only, but not for the winner

portfolio, which suggests that investors are more likely to follow the market

consensus for those stocks with poor performance than for stocks with strong

performance.

Panel B of Table 6 reports the regression estimates for the 2008-2012 sub-period.12

11 It also could be due to the speculative orientation and a low level of risk perception among Chinese investors (Wang, Shi and Fan, 2006).12 We also test the sub-period of 1994-2007. The results of all stocks and winner/loser groups are similar to those of whole sample. The herding magnitudes are in between those of whole sample and the period of 2008-2012.

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The herding parameter is larger for the 2008-2012 period than for the entire sample,

implying that market participants had a stronger tendency to herd during the financial

crisis. During that period, the interrupted stream of bad news may have shaken

investors’ confidence, and it is probably not surprising that investors suppressed their

own beliefs and cleaved to the market consensus.

By causing short-term mispricing, herding could indirectly lead to

momentum/contrarian profits. Having established that herding exists on the Chinese

stock market, we therefore examine whether the profitability of the investment

strategies can be connected to the herding intensity. Rather than assessing all

investment strategies, we focus on the JT contrarian strategies that were found in

Section 3 to be the most profitable. Table 7 shows the profitability of that strategy for

high and low herding periods. We find that contrarian profits are larger in high

herding periods than in low herding periods. In addition, contrarian profits are not

significant during low herding periods. In other words, herding seems to play an

important role in explaining the profitability of contrarian strategies. This finding

suggests that the mispricing of individual stocks caused by herding provides investors

with the opportunity realize a profit by investing in the loser portfolio and shorting the

winner portfolio if short selling would be allowed.

[Insert Table 7 here]

6. Conclusion

This study examines the profitability of zero-cost contrarian and momentum

strategies in the Chinese stock market for the 1994-2013 period. We find that

contrarian strategies based on the stocks’ past performance (à la Jagadeesh and

Titman, 1993) are profitable with weekly frequency but not with monthly frequency.

In contrast with previous studies conducted for the U.S. and European markets, we

18

find that strategies based on the “nearness” and “recency” to the 52-week high are all

not profitable. These findings are robust when we estimate Fama-MacBeth cross-

sectional regressions that control for firm charactristics.

The results for the sub-sample period of 2008-2012 show that contrarian profits

tend to be larger during the global financial crisis period, which we attribute to the

mean reversals of winner stocks. Hence, our research provides a profitable investment

strategy to mitigate the impact of bear markets, provided that the constraints on short

selling are loosened on the Chinese stock markets.

Finally, we find that herding behaviour exists in Chinese stock market, and during

the financial crisis period a stronger degree of herding is evident, which implies that

investors tend to follow the market consensus. Most importantly, the magnitude of a

contrarian strategy is positively related to the magnitude of herding behaviour in the

Chinese market. The finding provides a possible explanation for the source of

contrarian profits.

19

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Appendix: Ranking Criteria of Momentum Strategies

Strategies Ranking criteria at any period t

23

JT's individual stock momentum cumulative return of a stock of past t-J to t-1 periods

GH's individual stock momentum Proximity of current price to 52-week high price, work

as:GH=P i ,t−1

Highi ,t−1

BH's individual stock momentum Recency ratio, which is the timing when 52-week high

price has been achieved, work as:

RR=1−number of days since 52 week high price364

Table 1. Monthly profits from momentum strategies

24

J/K 1 3 6BH Winner 0.0042

(0.57)0.0074(1.03)

0.0105(1.33)

Loser 0.0069(0.89)

0.0082(1.10)

0.0107(1.37)

Winner-Loser -0.0027 -0.0008 -0.0002(-0.87) (-0.35) (-0.09)

GH Winner 0.0053(0.72)

0.0085(1.17)

0.0114(1.46)

Loser 0.0028(0.37)

0.0022(0.29)

0.0055(0.69)

Winner-Loser 0.0025 0.0033 0.0029(0.71) (0.90) (1.00)

JT-1 Winner 0.0054(0.70)

0.0051(0.71)

0.0052(0.71)

Loser 0.0046(0.59)

0.0061(0.80)

0.0044(0.60)

Winner-Loser 0.0009 -0.0009 0.0008(0.24) (-0.5) (0.53)

JT-3 Winner 0.0045(0.63)

0.0071(0.94)

0.0054(0.73)

Loser 0.0040(0.52)

0.0058(0.74)

0.0043(0.59)

Winner-Loser 0.0006 0.0013 0.0011(0.17) (0.53) (0.61)

JT-6 Winner 0.0036(0.49)

0.0050(0.68)

0.0059(0.80)

Loser 0.0030(0.41)

0.0036(0.50)

0.0046(0.64)

Winner-Loser 0.0006 0.0013 0.0013(0.17) (0.42) (0.65)

Notes: This table reports the average monthly portfolio returns from January1994 through December 2013 for three different momentum investing strategies. Bhootra-Hur (BH) portfolios are based on recency ratio, which captures the recency of 52-week high price and equals [1 - (current date - date of 52-week high)/364]. George-Hwang (GH) portfolios are formed based on the ratio of current price to the highest price achieved within the past 12 months. Jegadeesh–Titman (JT) portfolios are formed based on past 1,3,6-month returns. All portfolios are held for1,3, and 6 months. The winner (loser) portfolio for the recency ratio and 52- week high strategy is the equally weighted portfolio of the 10% of stocks with the highest (lowest) ratio of recency ratio and current price to 52-week high respectively. The winner (loser) portfolio in JT’s strategy is the equally weighted portfolio of 10% of stocks with the highest (lowest) past 1,3, 6-month return. Portfolios are rebalanced end of each month. The sample includes all ‘A’ share stocks listed on Shanghai and Shenzhen stock exchanges; Newey-West (1987) t-statistics are in parentheses.

Table 2. Weekly profits from contrarian strategies for whole sample

J/K 1 2 3

25

BH Winner 0.0015(0.85)

0.0012(0.69)

0.0011(0.62)

Loser 0.0014(0.79)

0.0016(0.89)

0.0018(1.01)

Loser-Winner -0.0001 0.0004 0.0007(-0.14) (0.47) (0.90)

GH Winner 0.0026(1.51)

0.0022(1.27)

0.0018(1.06)

Loser 0.0011(0.57)

0.0012(0.67)

0.0013(0.69)

Loser-Winner -0.0016 -0.0009 -0.0006(-1.54) (-0.95) (-0.58)

JT-1 Winner 0.0011(0.62)

0.0004(0.22)

0.0001(0.05)

Loser -0.0002(-0.10)

0.0006(0.36)

0.0009(0.53)

Loser-Winner -0.0012** 0.0002 0.0008*(-1.99) (0.45) (1.82)

Fama-French alpha

0.0004 0.0008 0.002(0.48) (1.57) (3.95***)

JT-2 Winner 0.0002(0.12)

-0.0003(-0.16)

-0.0004(-0.23)

Loser 0.0009(0.53)

0.0015(0.86)

0.0016(0.94)

Loser-Winner 0.0007 0.0018** 0.0020***(0.87) (2.33) (3.21)

Fama-French alpha

0.0013 0.0036 0.0038(1.59) (4.30***) (4.89***)

JT-3 Winner -0.0002(-0.09)

-0.0007(-0.38)

-0.0007(-0.38)

Loser 0.0016(0.91)

0.0019(1.05)

0.0020(1.13)

Loser-Winner 0.0018** 0.0025*** 0.0027***(2.11) (3.23) (3.67)

Fama-French alpha

0.0031 0.0044 0.0042(3.27***) (4.77***) (4.79***)

Notes: This table reports the average weekly portfolio returns from January 5 th 1994 through December 25th 2013 for three different contrarian investing strategies. Bhootra-Hur (BH) portfolios are based on recency ratio, which captures the recency of 52-week high price and equals [1 - (current date - date of 52-week high)/364]. George-Hwang (GH) portfolios are formed based on the ratio of current price to the highest price achieved within the past 12 months. Jegadeesh–Titman (JT) portfolios are formed based on past 1,2,3-week returns. All portfolios are held for1,2, and 3 weeks. The winner (loser) portfolio for the recency ratio and 52- week high strategy is the equally weighted portfolio of the 10% of stocks with the highest (lowest) ratio of recency ratio and current price to 52-week high respectively. The winner (loser) portfolio in JT’s strategy is the equally weighted portfolio of 10% of stocks with the highest (lowest) past 1,2, 3-week return. Portfolios are rebalanced on each Wednesday. Table also reports the corresponding alphas of JT’s strategy from the Fama–French five-factor model. The sample includes all ‘A’ share stocks listed on Shanghai and Shenzhen stock exchanges; Newey-West (1987) t-statistics are in parentheses. . ***,**,* denotes statistical significance at the 1%, 5% and 10% significance level respectively.

Table 3. Weekly profits from contrarian strategies for 2008-2012

J/K 1 2 3BH Winner -0.0018 -0.002 -0.0021

26

(-0.5) (-0.58) (-0.59)Loser -0.0014 -0.0013 -0.0013

(-0.42) (-0.39) (-0.38)Loser-Winner 0.0003 0.0007 0.0008

(0.34) (0.71) (0.84)GH Winner -0.0032 -0.0036 -0.0038

(-0.96) (-1.09) (-1.15)Loser -0.0004 -0.00063 -0.0007

(-0.11) (-0.17) (-0.18)Loser-Winner 0.0027 0.003* 0.0031*

(1.61) (1.78) (1.90)JT-1 Winner -0.0038 -0.0044 -0.0042

(-1.08) (-1.26) (-1.22)Loser -0.0031 -0.0023 -0.0021

(-0.89) (-0.67) (-0.61)Loser-Winner 0.0007 0.0020** 0.0020***

(0.66) (2.49) (2.90)Fama-French

alpha0.0016 0.0028 0.0029(1.43) (2.91***) (3.45***)

JT-2 Winner -0.0051 -0.0051 -0.0049(-1.47) (-1.5) (-1.43)

Loser -0.0018 -0.0015 -0.0013(-0.51) (-0.42) (-0.36)

Loser-Winner 0.0033*** 0.0037*** 0.0036***(2.80) (3.24) (3.61)

Fama-French alpha

0.0040 0.0045 0.0046(3.09***) (3.53***) (3.94***)

JT-3 Winner -0.0057 -0.0056 -0.0053(-1.63) (-1.64) (-1.55)

Loser -0.0015 -0.0012 -0.0011(-0.41) (-0.33) (-0.3)

Loser-Winner 0.0042*** 0.0045*** 0.0042***(3.26) (3.56) (3.58)

Fama-French alpha

0.0056 0.0057 0.0053(3.68***) (3.94***) (3.95***)

Notes: This table reports the average weekly portfolio returns from January 2nd 2008 through December 26th 2012 for three different contrarian investing strategies. Bhootra-Hur (BH) portfolios are based on recency ratio, which captures the recency of 52-week high price and equals [1 - (current date - date of 52-week high)/364]. George-Hwang (GH) portfolios are formed based on the ratio of current price to the highest price achieved within the past 12 months. Jegadeesh–Titman (JT) portfolios are formed based on past 1,2,3-week returns. All portfolios are held for1,2, and 3 weeks. The winner (loser) portfolio for the recency ratio and 52- week high strategy is the equally weighted portfolio of the 10% of stocks with the highest (lowest) ratio of recency ratio and current price to 52-week high respectively. The winner (loser) portfolio in JT’s strategy is the equally weighted portfolio of 10% of stocks with the highest (lowest) past 1,2, 3-week return. Portfolios are rebalanced on each Wednesday. Table also reports the corresponding alphas of JT’s strategy from the Fama–French five-factor model. The sample includes all ‘A’ share stocks listed on Shanghai and Shenzhen stock exchanges; Newey-West (1987) t-statistics are in parentheses. ***,**,* denotes statistical significance at the 1%, 5% and 10% significance level respectively.

Tables 4. Weekly returns of JT portfolios: January and non-January months

J/K 1 2 3Panel A: January excluded

1 Winner 0.0010 0.0003 -0.0001

27

(0.54) (0.15) (-0.05)Loser -0.0002

(-0.14)0.0005(0.27)

0.0008(0.47)

Loser-Winner -0.0012* 0.0002 0.0009**(-1.82) (0.37) (2.02)

2 Winner 0.0000(-0.01)

-0.0005(-0.27)

-0.0006(-0.35)

Loser 0.0007(0.38)

0.0013(0.75)

0.0015(0.86)

Loser-Winner 0.0007 0.0018** 0.0022***(0.81) (2.35) (3.32)

3 Winner -0.0004(-0.22)

-0.0009(-0.48)

-0.0009(-0.50)

Loser 0.0013(0.72)

0.0018(0.98)

0.0019(1.07)

Loser-Winner 0.0017** 0.0026*** 0.0028***(2.00) (3.26) (3.74)

Panel B: January only1 Winner 0.0025

(0.46)0.0018(0.31)

0.0021(0.37)

Loser 0.0007(0.11)

0.0024(0.39)

0.0018(0.30)

Loser-Winner -0.0019(-0.68)

0.0006(0.29)

-0.0003(-0.15)

2 Winner 0.0028(0.48)

0.0020(0.35)

0.0023(0.40)

Loser 0.0038(0.59)

0.0033(0.52)

0.0029(0.48)

Loser-Winner 0.0010(0.31)

0.0012(0.46)

0.0006(0.27)

3 Winner 0.0026(0.42)

0.0016(0.28)

0.0021(0.36)

Loser 0.0052(0.78)

0.0030(0.49)

0.0029(0.48)

Loser-Winner 0.0026(0.65)

0.0014(0.54)

0.0007(0.31)

Notes: This table reports the average weekly portfolio returns from January 4 th 1994 through December 25th 2013, excluding Januaries (Panel A) or Januaries only (Panel B), for Jegadeesh–Titman (JT) contrarian investing strategy, in which portfolios are formed based on past 1, 2, 3-week returns. All portfolios are held for 1, 2, 3 weeks. The winner (loser) portfolio in JT’s strategy is the equally weighted portfolio of 10% of stocks with the highest (lowest) past 1,2,3-week return. The sample includes all ‘A’ share stocks listed on Shanghai and Shenzhen stock exchanges; Newey-West (1987) t-statistics are in parentheses. ***,**,* denotes statistical significance at the 1%, 5% and 10% significance level respectively.

Table 5. Comparison of JT, GH, and BH Strategies, weekly frequency

Raw returns from (3,3) strategy Risk-adjusted returns from (3,3) strategy

January Incl. January Excl. January Incl. January Excl.

28

Intercept 0.0281***(3.46)

0.0309***(3.72)

-0.0112***(-5.31)

-0.011***(-4.85)

Ri , t−1 -0.0405***(-8.21)

-0.0404***(-7.79)

-0.0547***(-10.13)

-0.0591***(-10.82)

Size -0.0012***(-3.52)

-0.0013***(-3.81)

0.0005***(22.92)

0.0005***(22.18)

JTH winner dummy (JH)

-0.0024***(-6.63)

-0.0025***(-6.48)

-0.0031***(-8.13)

-0.0032***(-7.55)

JTL loser dummy (JL)

0.0002(0.64)

0.0003(0.79)

-0.0005(-1.18)

-0.0006(-1.35)

BHH winner dummy(BH)

-0.0008(-1.03)

-0.0009(-.13)

-0.0007(-1.57)

-0.0005(-1.48)

BHL loser dummy(BL)

0.0001(0.20)

0.0001(0.26)

7.18E-05(0.14)

7.01E-05(0.13)

GHH winner dummy(GH)

0.0005(1.51)

0.0006(1.60)

0.0003(0.50)

0.0003(0.61)

GHL loser dummy(GL)

-0.0003(-0.64)

-0.0004(-0.81)

-0.0004(-0.80)

-0.0004(-0.82)

JTH loser dummy - JTL winner dummy

0.0026***(4.81)

0.0027***(4.78)

0.0031***(4.92)

0.0031***(4.75)

BHH loser dummy – BHL winner dummy

0.0009(1.48)

0.0010(1.57)

-0.0004(-0.67)

-0.0005(1.33)

GHH loser dummy – GHL winner dummy

-0.0012(-1.44)

-0.0010(-1.62)

-0.0002(-0.18)

-0.0002(-0.25)

Notes: Each week between January 4th 1994 and December 25th 2013, 3 (k = 2, 3, 4) Fama-MacBeth cross-sectional regressions of the following form are estimated for a (3, 3) strategy:Ri , t=b0 kt+b1kt Ri , t−1+b2 kt ¿¿ i , t−1+b3 kt JTH i ,t−k+b4 kt JTLi , t−k+b5kt BHH i ,t−k+b6 kt BHLi , t−k+b7 kt GHH i ,t−k+b8 kt GHLi , t−k+εi ,t ¿ where Ri , t and ¿¿ i ,t−1¿ are the return and the market capitalization of stock i in week t; JTH i ,t−k ( JTLi , t−k ) is the JT’s (3,3) strategy dummy that takes the value of 1 if the past 3-week return for stock i is ranked in the top (bottom) 10% in week t-k, and zero otherwise. Variables BHH, BHL, GHH and GHL are defined similarly except that the BHH(BHL) indicates a winner (loser) by recency ratio ranking, and GHH (GHL) indicates a winner (loser) by the nearness to the 52-week high price. The coefficients from three regressions are averaged each week. The numbers reported for the raw return in the tables are the time-series averages of these averages. The Newey-West t-statistics (in parentheses) are calculated from the times series. The risk-adjusted returns are obtained from the time-series regressions of each of the individual week coefficient estimates on the contemporaneous Fama-French five factors. The numbers reported for risk adjusted returns are intercepts from these time-series regressions and their Newey-West t-statistics are in parentheses. ***,**,* denotes statistical significance at the 1%, 5% and 10% significance level respectively.

29

Table 6. Estimate of herding behaviour of the whole market and JT’s portfolios

Whole Market

Formation=1week Formation=2 weeks Formation=3 weeksWinner Loser Winner Loser Winner Loser

1994-2013γ0 0.0148*** 0.0157*** 0.0139*** 0.0160*** 0.0136*** 0.0160*** 0.0136***

(30.41) (57.91) (60.96) (58.84) (60.71) (57.74) (61.04)γ1 -0.0946*** -0.0217** -0.0137 -0.0201** -0.0152* -0.0188** -0.0200***

(23.23) (-2.45) (-1.53) (-2.10) (-1.81) (-2.07) (-2.59)γ2 0.1375*** 0.1784*** 0.1685*** 0.1872*** 0.1720*** 0.2105*** 0.1638***

(5.23) (11.84) (13.91) (12.97) (14.43) (14.41) (14.73)γ3 -0.2153** -0.0616 -0.2946*** -0.0957 -0.3522*** -0.2933** -0.3210***

(-2.35) (-0.39) (-4.69) (-0.63) (-5.09) (-2.05) (-5.25)2008-2012

γ0 0.0146*** 0.0175*** 0.0148*** 0.0180*** 0.0145*** 0.0182*** 0.0144***(72.59) (56.44) (50.81) (56.73) (47.87) (55.06) (55.93)

γ1 -0.0934*** -0.0994*** -0.0970*** -0.1016*** -0.0893*** -0.0998*** -0.0950***(18.61) (-17.06) (-17.91) (-16.94) (-17.84) (-18.03) (-19.23)

γ2 0.1548*** 0.1754*** 0.1852*** 0.1659*** 0.1947*** 0.1626*** 0.1842***(8.78) (7.33) (7.62) (7.07) (7.54) (6.87) (7.77)

γ3 -1.1474*** -1.2463*** -1.5779*** -0.9760*** -1.6740*** -0.8237** -1.6231***(-4.26) (-3.29) -(4.46) (-2.54) (-4.13) -(2.22) (-4.49)

Notes: This table reports the regression results of cross-sectional absolute dispersion (CSAD) based on the following equation: CSAD t=γ0+γ1 Rmt+γ2|Rmt|+γ 3 Rmt

2 +εt, where Rmt is the value of an equally weighted daily portfolio return (all listed stocks in the market or JT’s portfolios). On each Wednesday between January 4th 1994 and December 25th 2013, JT’s portfolios are formed. The winner (loser) portfolio is the equally weighted portfolio of 10% of stocks with the highest (lowest) past 1,2, 3-week return. The numbers in the parentheses are Newey-West t-statistics. ***,**,* denotes statistical significance at the 1%, 5% and 10% significance level respectively.

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Table 7. Comparison between contrarian profits with three-week formation period during high and low herding period

Herding Holding Period1 2 3

30% breakpointHigh

0.005*(1.90)

0.004*(1.87)

0.006**(2.23)

Low0.001(0.44)

0.002(0.066)

0.003(1.10)

50% breakpointHigh

0.001(1.19)

0.004**(2.52)

0.004***(3.02)

Low0.001(1.15)

0.001(0.65)

0.0007(0.51)

The contrarian strategies are constructed during high herding period, which is defined as when

Rm, t2 is above 30% (50%) percentile and CSAD t is below 30% (50%) percentile, and during low

herding period which is defined as when Rm, t2 is above 30% (50%) percentile and CSAD t is also

above 30% (50%) percentile. The numbers in the parentheses are Newey-West t-statistics. ***,**,* denotes statistical significance at the 1%, 5% and 10% significance level respectively.

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