Contrarian Strategy and Herding Behaviour in the Chinese Stock Market

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

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

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).

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

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 a re 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).

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 1 ^st 1994 to December 31 ^st 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

3

The 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.

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}

High i , t−1

, where ^P i ,t −1 is the price of stock i

at the end of period t-1 and ^High i ,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 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 days since 52 weeks high price 364

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 (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 = γ ₀ + γ ₁ R _{m ,t} + γ ₂ | ^R m ,t | ^{+ γ} 3 R _{m , t} ² + ε _t (1)

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

CSAD _t = 1 N ∑

i=1 N

| ^R i , t − R _{m ,t} | ⁽²⁾

where N is the number of stocks included in the portfolio, and R _{i ,t} is the observed

stock return of stock i for period t. R _{m , 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 Newey-West consistent estimator (Newey West 1987). A negative coefficient for ^γ 3 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 R _{m , t} ² 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 R _{m , t} ² 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, 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 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

4The risk-adjusted return is the corresponding constant from the Fama-French five factor model.

market downturn has affected the profitability of contrarian strategies. ⁵ Table 3 shows 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 strategies ⁷ . 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

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.

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.

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 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

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.

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 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:

¿ ¿ i, t −1+ b _3kt JTH _{i , t−k} + b _4kt JTL _{i ,t−k} + b _{5 kt} BHH _{i ,t−k} +b _{6 kt} BHL _{i ,t−k} +b _{7 kt} GHH _{i , t−k} +b _{8 kt} GHL _{i ,t−k} + ε _{i ,t}

R _{i ,t} =b _{0 kt} + b _{1 kt} R _{i ,t−1} + b _{2 kt} ¿ (3)

The dependent variable is the week t return of stock i, ^R i ,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 ^R i ,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 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), b ₀ 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, b ₃ −b _{4 (} b ₅ −b _{6 ,} b ₇ −b ₈ ) can be interpreted as the return in excess of b ₀ 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 A of Table 6 shows the estimation results for the whole sample period 1994-2013. For the aggregate market, the herding parameter ^γ 3 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 γ ₃ 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

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).

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 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

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.

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 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.

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Appendix: Ranking Criteria of Momentum Strategies

Strategies Ranking criteria at any period t

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}

High _{i , 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 price 364

Table 1. Monthly profits from momentum strategies

J/K 1 3 6

BH 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

BH Winner 0.0015

(0.85)

0.0012 (0.69)

0.0011 (0.62)

Loser 0.0014 0.0016 0.0018

(0.79) (0.89) (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

1994 through December 25

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 3

BH Winner -0.0018 -0.002 -0.0021

(-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 alpha

0.0016 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 2

2008 through December 26

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 3

Panel A: January excluded

1 Winner 0.0010

(0.54)

0.0003 (0.15)

-0.0001 (-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 only

1 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

1994 through December 25

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.

Intercept 0.0281***

(3.46)

0.0309***

(3.72)

-0.0112***

(-5.31)

-0.011***

(-4.85)

R _{i ,t −1} -0.0405*** -0.0404*** -0.0547*** -0.0591***

(-8.21) (-7.79) (-10.13) (-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 4

1994 and December 25

2013, 3 (k = 2, 3, 4) Fama- MacBeth cross-sectional regressions of the following form are estimated for a (3, 3) strategy:

¿ ¿ i, t −1+ b _3kt JTH _{i , t−k} + b _4kt JTL _{i ,t−k} + b _{5 kt} BHH _{i ,t−k} +b _{6 kt} BHL _{i ,t−k} +b _{7 kt} GHH _{i , t−k} +b _{8 kt} GHL _{i ,t−k} + ε _{i ,t}

R _{i ,t} =b _{0 kt} + b _{1 kt} R _{i ,t−1} + b _{2 kt} ¿ where R _{i ,t} and ¿ ¿ i, t −1

¿ are the return and the market capitalization of stock i in week t;

JTH _{i , t−k} ( JTL _{i , 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.

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

Market

Formation=1week Formation=2 weeks Formation=3 weeks

Winner Loser Winner Loser Winner Loser

1994-2013

γ ₀ ^{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)

γ ₁ ^{-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)

γ ₂ ^{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)

γ ₃ ^{-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

γ ₀ ^{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)

γ ₁ ^{-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)

γ ₂ ^{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)

γ ₃ ^{-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 =γ ₀ + γ ₁ R _mt +γ ₂ | ^R mt | ^{+ γ} 3 R _mt ² + ε _t , where R _mt 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 4

1994 and December 25

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.

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

Herding Holding Period

1 2 3

30% breakpoint

High 0.005*

(1.90)

0.004*

(1.87)

0.006**

(2.23)

Low 0.001

(0.44)

0.002 (0.066)

0.003 (1.10)

50% breakpoint

High 0.001

(1.19)

0.004**

(2.52)

0.004***

(3.02)

Low 0.001

(1.15)

0.001 (0.65)

0.0007 (0.51) The contrarian strategies are constructed during high herding period, which is defined as when

R _{m , t} ² is above 30% (50%) percentile and CSAD _t is below 30% (50%) percentile, and

during low herding period which is defined as when R _{m , t} ² 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.