Herding tendency among investors with heterogeneous information: Evidence from China’s equity markets

J. of Multi. Fin. Manag. 47–48 (2018) 60–75

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Herding tendency among investors with heterogeneous information: Evidence from China’s equity markets夽 Yaseen S. Alhaj-Yaseen a,∗ , Siu-Kong Yau b a b

School of Business, Middle Georgia State University, Macon, GA, 31206, United States College of Business, University of Findlay, Findlay, OH, 45840, United States

a r t i c l e

i n f o

Article history: Received 12 March 2018 Received in revised form 8 November 2018 Accepted 11 November 2018 Available online 15 November 2018 JEL classification: G02 G14 G15 Keywords: Chinese equity market Herding behavior Heterogeneous information Market liberalization

a b s t r a c t We investigate herding behavior using daily data of 87 Chinese stocks cross-listed on the A- and B-markets between 1996–2012, and shed light on herding within and between groups of investors with heterogeneous information. In addition to examining unconditional herding before and after the 2001-02 Chinese market liberalization, we explore different patterns of herd formation under different information environments. The results show that herding is present in both markets; however, while its intensity dropped significantly in the B-market after liberalization, it remains relatively unchanged in the A-market. Investors in the A-market exhibit intentional (non-fundamental) and unintentional (fundamental) herding over the entire period, while investors in the B-market exhibit intentional herding prior to market liberalization as well as both intentional and unintentional herding thereafter. Additionally, the results reveal possible interactions among and between arbitrageurs and noise traders in both markets. © 2018 Elsevier B.V. All rights reserved.

1. Introduction This study investigates whether herding tendencies vary among investors trading identical securities on different markets. Specifically, this study examines changes in the extent of herding tendencies in relation to 78 dual-listed stocks traded on the A- and B-markets in China, around the 2001-02 market reforms. Since the establishment of the A- and B-markets in the early 1990s, Chinese authorities have kept them segmented. Domestic investors were only allowed to trade on the A-market, while foreign investors were only allowed to trade on the B-market. Stocks listed on both the A- and B-markets have identical rights in all aspects, including dividends and voting rights. The only exception is that while A-shares are denominated in Chinese Yuan, B-shares are denominated in U.S. dollars on the Shanghai Stock Exchange (SHSE) and Hong Kong dollars on the Shenzhen Stock Exchange (SZSE). However, segmentation between the two markets led B-shares to trade at a significant discount relative to A-shares, an anomaly referred to in the literature as the “B-share discount puzzle.”1 In 2001, the Chinese authorities lifted trade restrictions on B-shares to allow domestic investors to enter the B-market.

夽 "Most of this study was conducted while the first author was at the University of Findlay. We are indebted to Zainah Asfoor for her excellent editorial help. All remaining errors are ours." ∗ Corresponding author. E-mail addresses: [email protected] (Y.S. Alhaj-Yaseen), yaus@findlay.edu (S.-K. Yau). 1 Mei et al. (2009) argue that in a market with different equity ownership restrictions, unrestricted shares should be traded at a premium relative to restricted shares. In China, however, unrestricted B-shares trade at a significant discount of approximately 80% compared to restricted A-shares (Chan et al., 2008). https://doi.org/10.1016/j.mulfin.2018.11.001 1042-444X/© 2018 Elsevier B.V. All rights reserved.

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Similarly, in 2002, they lifted trade restrictions on A-shares as well, allowing foreign investors to enter the A-market under the Qualified Foreign Institutional Investor (QFII) program. These reforms reduced the scale of the B-share discount, but did not eliminate it completely. The institutional background of the A- and B-markets in China provides a unique environment to examine differences in herding behavior by investors with heterogeneous information trading the same securities in different markets.2 The persistence of the B-share discount, even after lifting trade restrictions on both markets, is a clear indication that there must be an information imbalance between the two markets (Chan et al., 2008). However, it is unclear which group of investors is better informed. Several studies address the information disparity between domestic and foreign investors in China, but their results are inconclusive. For example, Chan et al. (2008) argue that different accounting standards and disclosure requirements in China, along with language and cultural barriers, leave foreign investors at an information disadvantage relative to domestic investors. Alternatively, Doukas and Wang (2013) claim that foreign institutional investors in China have far more experience in collecting, processing, and analyzing value-relevant information and consequently have information superiority over domestic investors. In this context, to the best of our knowledge, our study is the first to explore how changes in market fundamentals (i.e., market liberalization) can influence the extent of herding tendency among Chinese investors trading the same stocks in two different markets. Learning more about this relationship should, on the one hand, enhance our understanding of the determinants of herding behavior among market participants, and on the other, provide insights on whether domestic or foreign investors are better informed. Herding behavior describes a condition where the economic decision-making process of a large number of investors is based on market consensus rather than individual reasoning. Such behavior can exist when numerous investors choose to ignore their private information and mimic market movements (Christie and Huang, 1995). Therefore, in a multimarket set-up, such as the one highlighted in this study, herding is more likely to be observed in a market with more informationally-disadvantaged investors than one with better-informed investors (Chen et al., 2003). However, as indicated earlier, it is unclear whether domestic investors were better informed than foreign investors during the pre-liberalization period in China, when they were separate. It is also unclear whether A- or B-market participants are better informed after the liberalization of their respective markets. Additionally, more recent studies show that investors’ level of sophistication may yield different levels of herding tendencies (Dang and Lin, 2016). Given that foreign institutional investors (known for being highly sophisticated) dominated the B-market before liberalization, and individual domestic investors (known for being less sophisticated novices) dominated the A-market before and after liberalization, it is interesting to learn about changes in herding tendencies resulting from market liberalization among and between both groups. We first examine unconditional herding tendencies at the market level in both A- and B-markets around market liberalization. Trading the same securities simultaneously in different markets within the same country offers a unique setting for examining herding behavior by investors. We employ two models for this purpose, as proposed by Christie and Huang (1995) and Chang et al. (2000). Subsequently, assuming an improved information environment in China post liberalization,3 we examine different aspects of herding behavior in each market to better understand the net impact of market reforms on investors in both markets. Specifically, we account for the cross-market effect between the two markets around liberalization, as activities or events that occur in one market may have a significant impact on herding behavior in another market (Chiang and Zheng, 2010). Furthermore, following the recent work of Galariotis et al. (2015), we decompose our return dispersion measures into deviations due to fundamental and non-fundamental information, which allows us to distinguish between informational (spurious) and non-informational (intentional) herding. We distinguish between deviations in reaction to fundamental and non-fundamental information using common risk factors including size, book-to-market, and momentum. Adopting this approach is particularly important because when return dispersions are not decomposed into their fundamental and non-fundamental components, we risk not capturing herding activities, perhaps due to cancelling-out or averaging effects (Galariotis et al., 2015). Additionally, we employ another recently developed approach by Dang and Lin (2016) to examine herding within and between highly sophisticated arbitrageurs and unsophisticated noise traders in Chinese markets. Noise traders, also known as liquidity traders, are known to be less sophisticated as they find it costly to collect and process information (Dang and Lin, 2016). They are more vulnerable to systematic biases as they are largely driven by expectations and sentiments that are not necessarily justified by fundamental information. On the other hand, arbitrageurs, also called smart money and rational speculators, are assumed to be rational investors that are not driven by such sentiments. Instead, arbitrageurs form fully rational expectations about security returns, bringing prices closer to their fundamental values (Shleifer and Summers, 1990). This implies that arbitrageurs have the ability to pick securities that outperform the market (positive alpha) while noise investors are likely to pick securities that underperform the market (negative alpha). Following the footsteps of Dang and Lin (2016), we analyzed herding behavior both within and between each group of investors. Moreover, we evaluated

2 Given the price disparity between A- and B-markets, some argue that, after the opening of B-market access, some domestic investors may trade in both markets simultaneously to arbitrage. Several studies, however, investigated this possibility and concluded that it is impossible (Chung and Wei, 2005; Fernald and Rogers, 2002; Mei et al., 2009; Zhang and Zhao, 2004). 3 Several studies examine Chinese market efficiency around the 2001-02 market reforms and conclude that these reforms helped improve the Chinese information environment significantly (Carpenter et al., 2015; Chan et al., 2008; Chi, 2014).

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herding patterns before and after market liberalization to clarify the role of information in determining the extent of herding tendency. Our empirical evidence shows that herding tendency was higher in the B-market prior to market liberalization, but decreased significantly when domestic institutional investors started trading B-shares. Surprisingly, the herding tendency on the A-market remained relatively stable around market liberalization. In the following section, we argue that changes in the structure of market participants in both markets around the market reforms may yield some useful insights into the observed patterns of herding tendencies. Before market liberalization, domestic individual investors dominated the A-market while foreign institutional investors dominated the B-market. After the opening of the B-market, only local investors with free access to the foreign exchange market had full access to the B-market, since B-shares are denominated in US dollars in SHSE and Hong Kong dollars in SZSE. Of course, only domestic institutional investors have such access to the foreign exchange market; therefore, institutional investors continue to dominate the B-market, but the market now contains both domestic and foreign institutional investors. On the other hand, domestic individual investors continue to dominate the A-market. Thus, the arrival of sophisticated domestic institutional investors (Chi, 2014) can explain the decline in herding tendency in the B-market. Simultaneously, although foreign institutional investors could access the A-market post liberalization, they exerted a minor impact on the market since unsophisticated domestic individual investors (Chan et al., 2008; Mei et al., 2009) continued to dominate. Additionally, empirical evidence from our analysis of the changes in herding behavior patterns around market liberalization yields remarkable results. After controlling for cross-market information, our primary results remained unchanged. Moreover, our analyses of herding driven by fundamental versus non-fundamental information and herding within and between groups show that the A-market exhibits informational and non-informational herding before and after market liberalization. However, while the extent of informational herding increased after market liberalization, the extent of noninformational herding declined over the same period, which could be attributed to the improved information environment in China post-market reforms. On the other hand, our results show that investors in the B-market exhibit informational, but not non-informational, herding during the pre-liberalization period, which confirms the view that foreign institutional investors are highly sophisticated and rational traders who react to changes in market fundamentals but do not intentionally imitate other investors in the market. Lastly, our analysis of within- and between-group herding indicates that while herding within and between arbitrageurs (sophisticated, rational investors) and noise traders (unsophisticated, irrational investors) is evident around market liberalization in the A-market, it is less apparent in the B-market. In general, our results illustrate the role that the information environment can play in determining the extent and type of herding behavior. This study contributes to the literature in three ways. First, we shed light on the role played by market participants’ characteristics in determining herding behavior. Specifically, the unique set-up of the Chinese capital market and the type of investors who participate in it allow us to conduct a comparative analysis of herding behavior between the A- and Bmarkets. The informational flow between these two markets with completely different investment cultures (at least prior to market liberalization) can further contribute to herding behavior. Therefore, studying herding behavior by investors with heterogeneous information and who are trading the same securities in different markets within the same country should enhance our understanding of the drivers of herding behavior of individual investors. Second, we conduct our analysis over an extended period around the Chinese market liberalization (1996–2012), which allows us to examine herding patterns under different information environments. Although the quality and availability of information seem to play a major role in motivating herding behavior, the literature seems to lack empirical evidence of this relationship. Consequently, our study addresses this gap by focusing on changes in information environment around the period of the Chinese market liberalization. Third, to the best of our knowledge, we are the first to conduct a comparative analysis of herding behavior between individual and institutional investors, using the same measures. Previously, many studies investigate herding behavior among individual investors using approaches developed by Chang et al. (2000) and Christie and Huang (1995), or a hybrid of the two (Yao et al., 2014). Alternatively, to examine herding behavior among institutional investors, many studies analyze their portfolio holdings (Gompers and Metrick, 2001; Lakonishok et al., 1991; Nofsinger and Sias, 1999; Wermers, 1994). Unlike previous studies, we use consistent measures to examine herding behaviors among both groups, which should provide more accurate results and allow us to draw more meaningful conclusions about the differences in herding tendencies between the two groups. The remainder of this paper proceeds as follows. In Section 2, we provide an institutional background of the A- and B-markets in China. Sections 3 and 4 introduce our methodology and data, respectively. The empirical results are presented and discussed in Section 5. Finally, in Section 6, we summarize our main findings and provide some remarks. 2. Chinese equity markets There are three types of investors on the Chinese equity markets: domestic individual, domestic institutional, and foreign institutional investors. In the early years of the Chinese market, common shares were only available to domestic investors (i.e., A-shares). However, to attract additional foreign capital, the Chinese authorities introduced a second class of shares in 1992 (i.e., B-shares). The introduction of B-shares on the Chinese market created two segmented markets, namely the Aand B-markets. While domestic investors could trade restricted shares (A-shares) on the A-market, foreign investors could trade unrestricted shares (B-shares) on the B-market. However, it is worth noting that both A- and B-shares have equivalent rights in all aspects. That is, both share-classes enjoy the same voting and dividend rights. Nonetheless, the only difference

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between the two classes is that A-shares are denominated in Chinese yuan, while B-shares are denominated in U.S. dollars on the SHSE and Hong Kong dollars on the SZSE. In an attempt to liberalize the equity markets upon joining the World Trade Organization (WTO) in 2001, the Chinese authorities opened the B-market for domestic traders and, soon thereafter, announced the opening of the A-market for foreign traders. During the pre-liberalization period, foreign institutional investors dominated the B-market since individual foreign investors were prohibited from investing directly in that market. At the same time, domestic individual investors dominated the A-market.4 However, lifting trade restrictions on A- and B-shares did not give all domestic investors a free pass to trade on the B-market. Since B-shares are denominated in foreign currencies, only those with free access to the foreign exchange market (i.e., domestic institutional investors) could enjoy full access to the B-market. Additionally, empirical evidence from several studies suggest that arbitraging the pricing differences between A- and B-shares to exploit the price disparity between the two share-classes, is impossible (Chung and Wei, 2005; Fernald and Rogers, 2002; Mei et al., 2009; Zhang and Zhao, 2004).5 Therefore, domestic institutional investors in China are unlikely to trade on both A- and B-market simultaneously. This discussion suggests that while only foreign institutional investors had access to the B-market prior to market liberalization, post-liberalization domestic institutional investors gained access to this market while domestic individual investors did not. Instead, the latter continued to dominate the A-market post-liberalization. Several studies examine how informed each investor group is in the Chinese market. In terms of performance, foreign institutional investors in China are the least informed group for reasons including cultural and language barriers as well as differences in disclosure requirements and accounting standards. Chan et al. (2008) analyze both A- and B-markets in China, and conclude that foreign investors are informationally disadvantaged relative to domestic investors, in general. Additionally, empirical evidence from the literature indicates the superior performance of domestic institutional investors over domestic individual investors (Chi, 2014). Generally, highly skilled managers of mutual funds and their stock selection abilities contributed to this superior performance. Moreover, Chi (2014) finds that institutional investors in China trade in the same direction as insiders, suggesting that they possess private information based on which they trade. Finally, most individual investors in China are novice traders with limited investment opportunities; therefore, they continuously speculate in the market (Mei et al., 2009), mostly on the basis of rumors rather than market fundamentals (Chan et al., 2008). This indicates that domestic institutional investors are likely to be the most informed investor group on the Chinese market, while foreign institutional investors are likely to be the least informed group. Empirical evidence about herding behavior in China, in particular, is conflicting and inconclusive. Several studies examine herding behaviors on the Chinese market, but their findings are conflicting. For example, while Yao et al. (2014) find that herding strongly exists on the B-market and not the A-market, Chiang and Zheng (2010) reach the opposite conclusion for both SHSE and SZSE. Another study by Demirer and Kutan (2006) investigates herding behavior for 375 Chinese stocks traded on SHSE and SZSE and concludes that herding is absent at both individual and sector levels. Finally, Tan et al. (2008) find significant evidence in favor of herding in both A- and B-markets. Nonetheless, the literature suggests that herding is generally more likely to be present in less developed markets than in more developed and well-established ones (Blasco and Ferreruela, 2008; Chang et al., 2000). This is consistent with the view that developed markets have better informational efficiency than developing markets (Kim and Singal, 2000a, 2000b). Therefore, at the market level, it is reasonable to assume that herding is more likely to be present in a market where information is not equally available to all investors (i.e., a market with a high degree of information asymmetry). In such markets, investors are likely to either rely on macroeconomic (market related) information, since firm-specific information is not accessible, or imitate each other’s trades. While the former is called information-based (spurious) herding, the latter is called non-information-based (intentional) herding. Conversely, a market with low information asymmetry and, in turn, better-informed investors, is less likely to exhibit herding.

3. Research methodology 3.1. Unconditional herding We employ two approaches to investigate herding behaviors in the A- and B-markets. Both methods investigate the relationship between return dispersion and market return. Developed by Christie and Huang (1995), the first approach measures return dispersions using cross-sectional standard deviations at time t (CSSDt ), as follows:

 CSSDt =

4

N  i=1

Ri,t − Rm,t

N−1

2 (1)

As of 2003, individual investors owned more than 87% of the total stock market capitalization (Chi, 2014). In China, unrestricted B-shares trade at a significant discount relative to the restricted A-shares (Bailey et al., 1999). Prior to the B-market opening, this discount was as high as 80%, but decreased significantly thereafter. The price disparity between A- and B-shares (the B-share discount puzzle) presented investors with many potential opportunities for profitable arbitrage. Nonetheless, several studies explore this possibility and conclude that it is not possible. 5

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where N is the total number of firms in the market portfolio at time t, Ri, t is the observed stock return of firm i at time t, and Rm, t is the cross-sectional average of the N returns in the market portfolio at time t. If herding behavior is present in the market, stock returns will not deviate from market overall returns. However, if herding behavior is absent, stock returns deviate significantly from overall market returns. Therefore, CSSDt can be considered a proxy for the dispersion of individual stock returns around the market average. The underlying rationale of this measure is that herding behavior leads investors to suppress their own beliefs about individual stocks and base investment decisions solely on market collective actions, regardless of personal judgment. Thus, the presence of herding suggests that stock returns cluster around market returns and CSSDt is expected to be low as a result. Conversely, the absence of herding implies that stock returns deviate from the overall market returns and CSSDt is anticipated to be high. This also suggests that herding behavior should be more pronounced during periods of abnormally large average price movements. Consequently, investors are most likely to follow market consensus, which reduces the extent of return dispersion. However, rational asset-pricing models suggest that stock sensitivity toward market returns, namely betas, tend to vary across individual firms. As a result, periods of extreme market movements are likely to induce increased dispersion. To differentiate between the two conflicting hypotheses, Christie and Huang (1995) isolated the level of dispersion for individual stocks, CSSDt , in the extreme tails of the market returns distribution and examined if they differed significantly from the average levels of dispersion (excluding outliers). To do so, they estimated the following linear regression equation: CSSDt = ˛ + ˇL DtL + ˇU DtU + εt

(2)

where DtL = 1 if the return on market portfolio at time t lies in the extreme lower tail of the return distribution, and 0 otherwise. Similarly, DtU = 1 if the return on market portfolio at time t lies in the extreme upper tail of the return distribution, and 0 otherwise. The two dummy variables, DtL and DtU , capture differences in return dispersions during market stress periods. In Eq. (2), the coefficient ˛ denotes the average dispersion of the sample excluding the regions covered by the two dummy variables. Negative and statistically significant ˇL and ˇU would be consistent with the presence of herding behavior, since they imply that dispersions decrease during periods of market stress. However, positive and statistically significant ˇL and ˇU are consistent with the predictions of rational asset pricing models, since they suggest that dispersions increase during market stress periods. The second approach was developed by Chang et al. (2000), and uses the cross-sectional absolute deviation (CSADt ) of individual stock returns around market average as a proxy for return dispersion, as follows: CSADt =

⁄N

1

N   Ri,t − Rm,t 

(3)

i=1

While CSSDt can be extremely sensitive to outliers, since it is calculated using squared return-deviations, CSADt does not exhibit such sensitivity. However, the underlying rationale of Chang et al.’s approach is that rational asset pricing models imply a linear relationship between the dispersion measure, CSADt , and market portfolio returns. Therefore, in the presence of herding behavior, this relationship becomes non-linear. In their study, they estimate the following two models:







2







2

D  D CSADt = ˛ + 1D Rm,t + 2D Rm,t

U  U + 2U Rm,t CSADt = ˛ + 1U Rm,t

+ εt

(4)

+ εt

(5)

U D where Rm, t and Rm, t are the cross-sectional averages of the N returns in the market portfolio at time t, when the market is down and up, respectively. In this model, if herding behavior exists, we expect a non-linear relationship between the dispersion measure, CSADt , and market portfolio returns, Rm, t . Thus, the coefficient 2 should be negative and statistically significant. However, the absence of herding suggests that Eqs. (4) and (5) should demonstrate linearity. That is, the value of 2 should not be statistically different from 0.

3.2. Cross-market effect between A- and B-markets In this section, we consider the cross-market effect between A- and B-market. Prior studies show that events in one market may have a significant influence on herding behavior in other markets. Chiang and Zheng (2010) examine herding behavior in 18 markets, concluding that events in the US market play a significant role in explaining herding activities in other markets, which signifies the importance of information flow between markets when evaluating herding tendency. Several studies explore the information flow between the A- and B-share markets in China; however, their findings are inconclusive. While some studies find that the B-share market is the source of more important information and therefore leads price discovery (Chiu and Hung, 2007; Chui and Kwok, 1998; Doukas and Wang, 2013), other studies show that it is the A-market that leads price discovery between the two markets (Chakravarty et al., 1998; Chan et al., 2008; Guo et al., 2008; Yiming, 2003). Nonetheless, all these studies recognize a bilateral information feedback between the two markets. Accordingly, we investigate whether A-market investors’ activities can influence the decisions of their counterpart in the Bmarket. To do so, we examine the relationship between return dispersion measure and cross-market information, following

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the work of Tan et al. (2008); Chiang and Zheng (2010) and Galariotis et al. (2015). Equations (4) and (5) are modified to account for cross-market information flow from B- to A- market, as follows:







D  D CSADA,t = ˛ + 1D RA,t + 2D RA,t

CSADA,t

2



D + 3D RB,t

2

+ εt

(6)

 U   U 2  U 2  + 2U RA,t = ˛ + 1U RA,t + 3U RB,t + εt

(7)

We also modify the same equations to account for cross-market information flow from A- to B- market, as follows:







D  D CSADB,t = ˛ + 1D RB,t + 2D RB,t

CSADB,t

2



D + 3D RA,t

2

+ εt

(8)

 U   U 2  U 2  + 2U RB,t = ˛ + 1U RB,t + 3U RA,t + εt

(9)

D Here, CSADA,t and CSADB,t represent the return dispersions in the A- and B-markets, respectively. Additionally, while RA, t U are the A-market portfolio returns in the down- and up-markets, respectively, RD and RU are the corresponding and RA, B, t B, t t portfolio returns in the B-market. Furthermore, following the work of Chakravarty et al. (1998), we examine the lead-lag relationship between the return dispersion measure (CSADt ).6 Chakravarty et al. (1998) test whether A-share returns lead B-share returns. They argue that returns of A- and B-shares should be contemporaneously correlated if both markets respond similarly to new information. Otherwise, a lead-lag relationship should be observed between them. They find that although a two-way information flow is present between the two markets, A-share returns are more likely to lead B-share returns. Given the lead-lag relationship between A- and B-share returns, we explore the lead-lag cross-market herding relationship between the two markets. To do so, we estimate the following regression:

CSADB,t = d0 +

4 

dk CSADA,t+k + εB,t

(10)

k=−4

where CSADB,t are B-shares’ cross-sectional absolute deviation for day t and CSADA,t+k are A-shares’ cross-sectional absolute deviation for day t + k. In this equation, the lead coefficients are the ones with negative subscripts (d−1 , d−2 , d−3 , d−4 ). If these coefficients are significant, then current B-shares’ return dispersions are related to those of A-shares. This implies that A-shares’ return dispersions lead B-shares’ return dispersions. Conversely, if A-shares’ return dispersions lag B-shares’ return dispersions, the lag coefficients (d+1 , d+2 , d+3 , d+4 ) will be statistically significant. 3.3. Herding with fundamental and non-fundamental information Next, we explore the difference between informational (spurious) and non-informational (intentional) herding in the Chinese market using Galariotis et al.’s (2015) approach. While spurious herding can be viewed as investors’ reaction to changes in fundamental information (Bikhchandani and Sharma, 2000), intentional herding occurs when investors imitate each other’s actions with intent. In order to distinguish between the two types, Galariotis et al. (2015) decompose the CSADt measure into deviations due to common fundamental and non-fundamental factors by estimating the following regression: CSADt = ˇ0 + ˇ1 (Rm,t − RF ) + ˇ2 HMLt + ˇ3 SMBt + ˇ4 MOM t + εt

(11)

In Eq. (11), HMLt is the High Minus Low return factor, SMBt is the Small Minus Big return factor, and MOM t is the Momentum factor. Galariotis et al. (2015) interpret the residual εt as the cross-sectional absolute deviations without the effect of fundamental information, which implies that return dispersion due to fundamental information may be calculated as follows: CSADFUND,t = CSADt − CSADNONFUND,t where CSADFUND and CSADNONFUND are cross-sectional absolute deviations due to fundamental and non-fundamental information, respectively. Galariotis et al. (2015) argue that one can use this approach to identify deviations due to non-fundamental information (CSADNONFUND ) and use them as a proxy for intentional herding. Similarly, this approach allows us to identify deviations due to fundamental information (CSADFUND ) and use them to proxy spurious herding. Therefore, we estimate the following two regressions:





CSADFUND,t = ˛ + 1 Rm,t + 2 Rm,t  + 3 (Rm,t )2 + εt

(12)

CSADNONFUND,t

(13)

  = ˛ + 1 Rm,t + 2 Rm,t  + 3 (Rm,t )2 + εt

Following Chiang and Zheng (2010), we include Rm,t as a regressor in both models to account for potential asymmetry in investor behavior under different market conditions.

6

We thank the anonymous referee for this suggestion.

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3.4. Herding within- and between-group Finally, we distinguish between arbitrageurs and noise traders following Dang and Lin (2016). In their study, Dang and Lin (2016) argue that arbitrageurs are sophisticated and are able to identify stocks that outperform the market (stocks with positive alphas). Noise traders, on the other hand, are less sophisticated and not fully rational, implying that they are more likely to rely on their sentiments when selecting and trading stocks. Shleifer and Summers (1990) suggest that noise traders follow trading strategies based on pseudo-signals that are highly correlated with each other. Thus, these trading strategies are likely to drive noise traders to trade stocks that underperform the market (stocks with negative alphas). Dang and Lin (2016) argue that herding may be present within (within-group herding) or between (between-group herding) these groups of investors. To examine herding within- and between-groups, we follow Dang and Lin (2016) by splitting stocks in our full sample into two groups based on their alpha values. The alpha value for each stock can be obtained using Carhart’s (1997) four-factor model, as follows: Rt = ˛0 + ˛1 (Rm,t − RF ) + ˛2 HMLt + ˛3 SMBt + ˛4 MOM t + εt

(14)

Once we identify the sign of alpha (˛0 ), we compute our return dispersion measure CSAD for stocks with positive alphas (CSAD+,t ) and those with negative alphas (CSAD−,t ), as follows:





2 2 CSAD+,t = 0 + 1 R+m,t + 2 R+m,t  + 3 R+m,t + 4 R−m,t + 5 CSAD−,t + et

(15)

CSAD−,t

(16)

  2 2 = 6 + 7 R−m,t + 8 R−m,t  + 9 R−m,t + 10 R+m,t + 11 CSAD+,t + et

2 2 where R+m,t and R−m,t are the returns of the (sub)market equally weighted portfolio for stocks with positive and negative alphas, respectively. Following the work of Dang and Lin (2016), alpha values are calculated on a yearly basis, and positive and negative alpha portfolios are rebalanced accordingly. The presence of herding within-group is consistent with negative and significant estimates for 3 and 9 , while the presence of herding between groups is consistent with negative and significant estimates for 4 and 10 . Moreover, the presence of interactions between the two groups is consistent with significant estimates for 5 and 11 .

4. Data Daily data from SHSE and SZSE are obtained from China Stock Market & Accounting Research (CSMAR) Database for the period between 1992 and 2012. Our analysis first matches B-shares with their corresponding A-shares. We subsequently exclude firms with only B-shares from our sample,7 the final sample consisting of 45 firms from SHSE and 42 from SZSE. Third, we eliminate the period between 1992 and 1995, when Chinese equity markets were still immature and experiencing a considerably high level of returns volatility. To determine the liberalization impact on the herding propensity among market participants, we analyze data in the context of major market events. In February 2001, the China Securities Regulatory Commission (CSRC) lifted trading restrictions on B-shares by allowing domestic traders onto the B-market. In November 2002, the CSRC lifted trading restrictions on A-shares by allowing qualified foreign institutional investors (QFII) access to the domestic Chinese market (i.e., the A-market). However, it was not until May 2003 that the first two QFII licenses were issued to UBS AG and Nomura Securities. By July 2003, only five financial institutions were QFII approved by the CSRC: UBS, Nomura Securities, Morgan Stanley, Citigroup Global, and Goldman Sachs. Consequently, our final sample spans from January 1, 1996 to December 31, 2012. The significant drop of the B-share discount after market liberalization suggests that fundamental changes took place as a result of opening the B-market to domestic traders. We are interested in learning more about the impact of these fundamental changes on herding tendencies on both A- and B-markets. We consider the period between January 1, 2001 and June 30, 2003 to be our liberalization window, and design the experiment around it. This period covers the two major market reforms (A- and B-market opening) during their evolution. The pre-liberalization period covers the period between January 1, 1996 and December 31, 2000, while the post-liberalization period covers the period between July 1, 2003 and December 31, 2012.8 Additionally, we account for thin trading in the B-market relative to the A-market. To do so, we remove firm-day price observations from stocks with a daily trading volume below 10,000 when calculating our return dispersion measures. We apply the same filter to stocks in the A-market to standardize the process across both markets. This should eliminate any potential bias that could result from including stocks with low liquidity. In Table 1, we present the descriptive statistics for dual-listed shares on SHSE and SZSE over the full period, and the pre- and post-liberalization periods. All daily stock prices are converted to U.S. dollars, using daily foreign exchange rates obtained from the State Administration of Foreign

7 Domestic Chinese firms wishing to access the equity market on mainland China can issue two different share classes, A or B, or both. We find nine firms from SHSE and 11 from SZSE with only B-shares but not A-shares. Firms with only B-shares are dropped from our final sample. 8 In Section 5, we conduct further robustness tests by considering a shorter post-liberalization window to avoid the potential impact of the 2008 financial crisis.

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Table 1 Descriptive Statistics of SHSE A- and B-Markets. Pre-Liberalization Market SHSE A

SHSE B

SZSE A

SZSE B

Mean Std. Dev. Min Median Max Skewness kurtosis Mean Std. Dev. Min Median Max Skewness kurtosis Mean Std. Dev. Min Median Max Skewness kurtosis Mean Std. Dev. Min Median Max Skewness kurtosis

Post-Liberalization

Rt

CSSDt

CSADt

Rt

CSSDt

CSADt

0.06% 2.10% −10.48% 0.09% 9.77% −0.35 7.21 0.05% 2.74% −14.41% −0.12% 16.00% 0.48 7.14 0.08% 2.27% −11.57% 0.14% 9.25% −0.69 7.02 0.02% 2.88% −20.49% −0.07% 16.90% 0.20 8.69

2.37% 1.07% 0.59% 2.15% 11.32% 2.40 13.86 3.00% 1.20% 0.27% 2.86% 12.34% 2.00 11.81 2.39% 1.09% 0.02% 2.17% 11.84% 2.47 14.76 2.83% 1.31% 0.06% 2.61% 16.32% 3.03 23.71

1.69% 0.66% 0.45% 1.54% 5.18% 1.40 6.22 2.19% 0.79% 0.21% 2.10% 6.84% 0.99 5.54 1.74% 0.73% 0.02% 1.60% 6.23% 1.58 7.35 2.07% 0.84% 0.04% 1.94% 8.23% 1.33 7.55

0.00% 2.15% −9.23% 0.15% 9.11% −0.67 5.30 0.00% 2.12% −9.51% 0.09% 10.52% −0.35 7.43 0.01% 2.03% −9.68% 0.15% 8.82% −0.59 5.20 0.01% 1.88% −9.69% 0.11% 9.09% −0.37 6.35

2.35% 1.17% 0.65% 2.16% 18.18% 4.43 40.49 1.70% 1.08% 0.42% 1.48% 14.98% 4.87 40.56 2.32% 1.14% 0.75% 2.12% 22.15% 5.27 58.28 1.93% 1.12% 0.62% 1.73% 24.78% 7.04 96.56

1.67% 0.66% 0.50% 1.55% 5.90% 1.25 5.71 1.16% 0.57% 0.33% 1.03% 6.06% 2.37 12.93 1.66% 0.62% 0.00% 1.55% 8.10% 1.70 9.98 1.35% 0.55% 0.00% 1.24% 9.86% 2.98 29.02

This table presents summary statistics of firms with A- and B-shares in SHSE. Rt is the average daily stock returns. CSSDt is the average daily cross-sectional standard deviation. CSADt is the cross-sectional absolute standard deviation. Summary statistics are provided for the pre- and post-liberalization periods (1/1/1996 – 12/31/2000) (7/1/2003 – 12/31/2012), respectively.

Exchange.9 Panel A presents the descriptive statistics for A-shares, while those for B-shares are in Panel B. This table includes the descriptive statistics of the daily averages of stock returns (Rt ), cross-sectional standard deviations (CSSDt ), and crosssectional absolute deviations (CSADt ). The descriptive statistics presented in Table 1 have one important implication. While the average values of the return dispersion measures CSSDt and CSADt seem to remain stable on the A-market post-liberalization, they drop significantly on the B-market. These measures show the clustering of individual stock returns around market returns. High values of CSSDt or CSADt , imply less clustered (more scattered) returns around market returns. Conversely, low values of CSSDt or CSADt imply more clustered (less scattered) returns around market returns. Finally, in Fig. 1, we view the relationship between cross-sectional absolute deviations (CSADt ) and their corresponding returns of an equally weighted portfolio of our confined sample of dual-listed stocks (Rm, t ) in SHSE. We present this relationship for A- and B-markets before and after market liberalization. Similar to Fig. 1, we present the same relationship for SZSE in Fig. 2. From both figures, it is clear that a non-linear relationship exists between CSADt and Rm, t in either the preor the post-liberalization periods.10 This can be observed on the A- and B-markets in SHSE and SZSE, which is, according to Chang et al. (2000), an indication of herding behavior in these markets during both periods.11 5. Empirical results 5.1. Herding in A- and B-share markets around market reforms Table 2 presents the results from Eq. (2) using the 5% and 1% criteria when determining returns in the upper and lower tails of the market returns distribution. The results in this table are from SHSE and SZSE, and cover the entire period as well

9 Although the exchange rate between the Chinese yuan and the U.S. dollar was fixed between 1994 and 2005, in 2005, China adopted an exchange rate system that references a basket of currencies. The system allows the Chinese currency to fluctuate daily up to 0.5%. To account for daily fluctuations in exchange rates, daily exchange rates are used to convert all share classes to U.S. dollars. 10 We do not draw any inferences from our descriptive statistics here. In the following section, we discuss results from our empirical analysis of herding tendencies on the A- and B-markets using the models discussed in Subsection 3.1. 11 Chang et al. (2000) presented this relationship for Hong Kong between January 1981 and December 1995, finding a linear relationship between CSADt and Rm, t (see pp. 1,658).

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Fig. 1. Relationship between daily cross-sectional absolute deviation (CSADt ) and the corresponding returns from an equally weighted portfolio of our confined sample of dual-listed stocks in A- and B-Share markets in SHSE during the pre- and post-liberalization periods.

as pre- and post-liberalization periods. The results shown in Panel A indicate that herding is present in all four markets (SHSE-A, SHSE-B, SZSE-A, and SZSE-B). The coefficients ˇL and ˇU are negative and statistically significant, suggesting that return dispersions are likely to decline during extreme market movements. An analysis of the extent of herding around market liberalization shows that herding propensity is stronger in the Bmarket than in the A-market pre-liberalization. Post-liberalization, it decreases significantly in the B-market, but increases in the A-market. For example, the pre-liberalization coefficients ˇL and ˇU in SHSE-B are -0.0023 and -0.0022, respectively. However, their magnitudes decrease post-liberalization to -0.0012 and -0.0010, respectively. At the same time, the values of ˇL and ˇU on SHSE-A during the pre-liberalization period are -0.0017 and -0.0015, respectively. Post-liberalization, these coefficients are higher at -0.0020 and -0.0016, respectively. This implies that opening up the B-market to domestic investors reduced the herding tendency among its participants. Meanwhile, the opening up of the A-market increased the herding tendency among its participants. For both up- and down-markets, two-sided Wald tests reject the null hypothesis that H0 : ˇPre = ˇPost at the 1% level for the B-market, but not the A-market. Moreover, one-sided Wald tests reject the alternative hypothesis, Ha : ˇPre < ˇPost , at the 5% level for both the A- and B-markets (results not shown). These results imply that the extent of herding declined among B-market participants, but remained unchanged for the A-market, post-liberalization. We interpret these results as evidence that the B-market investors are becoming better informed while the A-market investors are becoming less informed as a result of market liberalization.12 Panel B of the same table presents similar results when using the 1% criterion in defining market extreme returns. In general, institutional investors are known for being highly sophisticated. Their ability to collect, process, and analyze value-relevant information gives them a comparative advantage over individual investors. Many studies have used institutional holding to proxy sophisticated investors (e.g., Bartov et al., 2000; Bushee, 1998; and Miao et al., 2016). However, when a market is dominated by novice individual investors who trade on rumors rather than fundamentals, as in the case of the Chinese market (Alhaj-Yaseen et al., 2016; Chan et al., 2008), institutional investors may not have a relative advantage. This is particularly true of foreign institutional investors who must simultaneously overcome language and cultural barriers as

12

We clarify this finding in the discussion below.

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Fig. 2. Relationship between daily cross-sectional absolute deviation (CSADt ) and the corresponding returns from an equally weighted portfolio of our confined sample of dual-listed stocks in A- and B-Share markets in SZSE during the pre- and post-liberalization periods.

well as differences in accounting standards and disclosure requirements. In this case, it is not surprising that foreign institutional investors are informationally disadvantaged relative to domestic investors, especially before market liberalization. This finding is consistent with the findings of Chan et al. (2008), who attributed the B-share discount puzzle to information asymmetry and concluded that foreign institutional investors were indeed informationally disadvantaged prior to market liberalization.13 Table 3 provides summary statistics from Eqs. (4) and (5). In these two equations, we test the extent of herding using CSADt as a proxy for return dispersions. Panel A presents results from Eq. (4), which measures herding tendency on down-markets, while Panel B presents results from Eq. (5), which measures herding tendency on up-markets. Chang et al. (2000) argue that the herding tendency should exhibit a non-linear relationship between CSADt and Rm, t . That is, if herding is present, the coefficients of 2D in Eq. (4) and 2U in Eq. (5) should be negative and statistically significant. Consistent with our findings from Table 2, we find that herding is present on all four markets. Additionally, since the extent of convexity (the magnitude of 2D and 2U ) can exhibit the propensity of herding, our results imply that herding was more severe in the B-market than the A-market pre-liberalization. However, the opening of both markets reduced the herding tendency in the B-market, but increased it in the A-market. Generally, the results based on Eqs. (4) and (5) are consistent with those based on Eq. (2). The results obtained from both models for the pre-liberalization period imply that the herding tendency is much stronger in the B-market than the A-market. This finding may imply that foreign investors, who exclusively traded in the B-market during that period, are less informed than domestic investors in emerging markets. As such, it makes sense to assume that the herding tendency should thrive in a market where investors are informationally disadvantaged. Additionally, according to empirical evidence from the post-liberalization period, the herding tendency during this period dropped significantly in the B-market, but slightly increased in the A-market. Results from two-sided Wald tests reject the null hypothesis H0 : 2Pre = 2Post at the 1% level for the B-market but not the A-market. Moreover, a one-sided hypothesis 2Pre < 2Post is rejected at the 1% level for both the A- and B-markets (results not shown). These results are consistent for the up- and down-markets. This change in herding tendency is associated with the movement of domestic traders into the B-market. Surprisingly, this may imply that traders on the B-market have become better informed than those on the A-market post-liberalization. A care-

13 Several other studies have also concluded that foreign investors in China are less informed than domestic investors (see, e.g., Sjöö and Zhang, 2000; Chen et al., 2003; and Balcilar and Hammoudeh, 2012).

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Table 2 Testing Herding Behavior using CSSD in A- and B-share markets in China. Panel A: Market return in the extreme upper/lower 5% of the return distribution. Market

SHSE A SHSE B SZSE A SZSE B

Pre-Liberalization

Post-Liberalization

Pre- vs. Post-Liberalization

˛

ˇL

ˇU

R2

˛

ˇL

ˇU

R2

21

p − Value1

22

p − Value2

0.0249*** (204.68) 0.0313*** (246.94) 0.0245*** (188.45) 0.0295*** (185.46)

−0.0017*** (-12.66) −0.0023*** (-21.16) −0.0019*** (-10.32) −0.0023*** (-20.84)

−0.0015*** (-9.12) −0.0022*** (-21.36) −0.0016*** (-8.48) −0.0021*** (-18.45)

0.005

0.0250*** (242.92) 0.0186*** (199.73) 0.0246*** (234.41) 0.0209*** (205.31)

−0.0020*** (-17.72) −0.0012*** (-12.51) −0.0020*** (-17.85) −0.0009*** (-5.95)

−0.0016*** (-16.82) −0.0010*** (-10.03) −0.0017*** (-14.64) −0.0009*** (-5.44)

0.005

1.39

0.211

0.69

0.441

0.008

8.97***

0.004

12.13***

0.001

0.006

1.18

0.297

0.78

0.425

0.008

11.09***

0.002

14.29***

0.000

0.004 0.001 0.004

Panel B: Market return in the extreme upper/lower 1% of the return distribution. Market Pre-Liberalization

SHSE A SHSE B SZSE A SZSE B

Post-Liberalization

˛

ˇL

ˇU

R2

0.0247 (225.43***) 0.0311 (270.33***) 0.0245 (208.25***) 0.0293 (200.33***)

−0.0016*** (-12.36) −0.0026*** (-20.51) −0.0017*** (-9.35) −0.0024*** (-23.60)

−0.0014*** (-9.32) −0.0021*** (-21.33) −0.0015*** (-8.28) −0.0022*** (-21.00)

0.0045 0.02489*** (268.06) 0.0042 0.0184*** (222.06) 0.0014 0.0246*** (258.87) 0.0032 0.0209*** (222.15)

˛

Pre- vs. Post-Liberalization

ˇL

ˇU

R2

p − Value1

22

p − Value2

−0.0018*** (-17.99) −0.0011*** (-12.3) −0.0019*** (-19.49) −0.0009*** (-6.25)

−0.0016*** (-17.66) −0.0009*** (-9.79) −0.0017*** (-16.21) −0.0008*** (-5.42)

0.0054 0.84

0.378

0.79

0.401

0.0072 7.61**

0.06

12.28*** 0.001

0.0062 0.62

0.515

0.54

21

0.0096 13.18*** 0.001

0.674

16.33*** 0.000

Regression coefficients for Eq. (2), CSSDt = ˛ + ˇL DtL + ˇU DtU + εt , using daily returns for firms listed on A- and B-markets in SHSE and SZSE. Full period spans from 1/1/1996 to 12/31/2012. Pre- and post-liberalization periods span over the periods 1/1/1996-12/31/2000 and 7/1/2003-12/31/2012, respectively. Following Christie and Huang (1995), two criteria are used to define extreme market movements when estimating Eq. (2). In Panel A, we use the 5% criterion to restrict DtL and DtU to 5% of the lower tail and 5% of the upper tail of the market return distribution. Similarly, we use the 1% criterion to restrict market return distribution of the lower and upper tails. 21 and p − Value1 are the Chi-squared statistic for the Wald test for the null hypothesis H0 : ˇLPre = ˇLPost , while 22 and p − Value2 are the Chi-squared statistic for the Wald test for the null hypothesis H0 : ˇUPre = ˇUPost . *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. t-statistics are presented in parenthesis and they are based on Newey and West (1987)’s heteroscedasticity and autocorrelation consistent with standard errors.

ful examination of the market participants of both A- and B-markets may provide further insights. After the B-market opened, only domestic traders with access to the foreign exchange market, namely domestic institutional investors, were able to access the B-market freely. Simultaneously, domestic individual investors, who dominated the A-market pre-liberalization, are now controlling a larger share of the A-market after the movement of domestic institutional investors onto the B-market. While we do not eliminate other interpretations, this appears to be a plausible explanation. 5.2. Cross-market effect analysis Next, we turn our attention to the cross-market effect between the A- and B-share markets. Table 4 presents the results from Eqs. (6)–(9). Similar to Table 3, our coefficient of interest is 2 , which should be negative and statistically significant if herding is present. For both SHSE and SZSE, the coefficient 2 is negative and statistically in up- and down-markets. At the same time, when controlling for the cross-market information flow from A-to-B, results show that the coefficient 3 (cross-market effect coefficient) is negative and statistically significant for both SHSE and SZSE during both the pre- and post-liberalization periods. However, results from the other direction, B-to-A, show that while 3 is negative and statistically significant during the post-liberalization period, it was not so during the pre-liberalization period. In general, these results imply that herding intensity among investors in each market does not change upon receiving cross-market information. Results from our investigation of the lead-lag cross market herding relationship are presented in Table 5. We use the corrected version of Akaike Information Criterion (AIC) and the Schwarz Information Criterion (SIC) to determine the appropriate lag length. Both lag length selection criteria suggest that four leads and four lags are appropriate. In this table, we report results from the pre- and post-liberalization periods. For the lead coefficients (d−1 , d−2 , d−3 , d−4 ), the positive and statistically significant correlation of d−1 and d−2 indicates that the A-shares’ return dispersions have predictive ability for the B-shares’ return dispersions; this relationship holds before and after market liberalization. This finding is confirmed by the F-statistics of the null hypothesis that suggests that the lead coefficients (d−1 , d−2 , d−3 , d−4 ) are jointly zero. As for the lag coefficients, d+1 , d+2 , d+3 and d+4 are positive, while only d+1 is statically significant during the post-liberalization period but not before market liberalization. These results suggest that while B-shares’ return dispersions have predictive ability for the A-shares’ return dispersions during the post-liberalization period, they did not beforehand. This conclusion is confirmed by the F-statistics of the null hypothesis that the lag coefficients (d+1 , d+2 , d+3 , d+4 ) are jointly zero; the Flag is statistically significant at the 5% level only during the post-liberalization period.

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Table 3 Testing Herding Formation using CSAD in A- and B-share Markets in China. Panel A: Down-Market Market

SHSE A SHSE B SZSE A SZSE B

Pre-Liberalization

Post-Liberalization

Pre- vs. Post-Liberalization

˛

1D

2D

R2

˛

1D

2D

R2

2

p-Value

0.0146*** (185.57) 0.0201*** (164.04) 0.0151*** (143.09) 0.0188*** (132.35)

0.2413 (30.29) 0.1568*** (12.82) 0.1902*** (18.28) 0.1655*** (11.38)

−1.8859*** (-26.91) −2.3006*** (-45.71) −1.4687*** (-20.52) −2.1889*** (-31.85)

0.070

0.01406*** (265.53) 0.00833*** (233.87) 0.01440*** (291.14) 0.01062*** (283.89)

0.2812*** (61.94) 0.3157*** (82.37) 0.2617*** (53.94) 0.3015*** (67.14)

−2.0361*** (-19.38) −0.7687*** (-3.17) −1.6194*** (-10.04) −0.5411*** (-2.67)

0.198

1.51

0.188

0.244

12.66***

0.001

0.177

1.34

0.221

0.212

18.17***

0.000

0.080 0.038 0.092

Panel B: Up-Market. Market

SHSE A SHSE B SZSE A SZSE B

Pre-Liberalization

Post-Liberalization

Pre- vs. Post-Liberalization

˛

1U

2U

R2

˛

1U

2U

R2

2

p − Value

0.0135*** (188.11) 0.0197*** (113.88) 0.0145*** (176.08) 0.0177*** (184.01)

0.2729*** (32.12) 0.1109*** (5.21) 0.2108*** (23.70) 0.1437*** (14.58)

−1.0798*** (-7.62) −1.3068*** (-14.07) −1.1212*** (-14.05) −1.8103*** (-9.23)

0.137

0.0139*** (263.57) 0.0092*** (233.42) 0.0147*** (308.82) 0.0117*** (301.24)

0.1463*** (24.26) 0.2044*** (38.32) 0.0912*** (20.94) 0.1313*** (29.58)

−1.1151*** (-12.19) −0.5545** (-2.32) −1.1445*** (-3.97) −0.4471*** (-3.69)

0.037

0.86

0.372

0.112

10.95***

0.002

0.010

0.90

0.394

0.046

13.82***

0.000

0.021 0.128 0.078

   2 + εt (4).    U 2 U  + 2U Rm,t + εt (5). CSADt = ˛ + 1U Rm,t

Regression coefficients for Eqs. (4) and (5), using daily data returns for firms listed on A- and B-markets in SHSE and SZSE.

D  D + 2D Rm,t CSADt = ˛ + 1D Rm,t

Full period spans from 1/1/1996 to 12/31/2012. The pre- and post-liberalization periods span over the periods 1/1/1996-12/31/2000 and 7/1/200312/31/2012, respectively. Following Chang et al. (2000), we control for herding asymmetry in up- versus down-market by estimating Eqs. (4) and (5). In Panel A, we present results from down-market, Eq. (4), and Panel B presents results from the up-market, Eq. (5). 21 and p − Value1 are the Chi-squared statistic for the Wald test for the null hypothesis H0 : 2Pre = 2Post . *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. t-statistics are presented in parenthesis and they are based on Newey and West (1987)’s heteroscedasticity and autocorrelation consistent with standard errors. Table 4 Controlling for cross-market effect between the A- and B-share Markets. Market

Pre-Liberalization

SHSE A-to-B

Down UP

B-to-A

Down UP

SZSE A-to-B

Down UP

B-to-A

Down UP

Post-Liberalization 2

0

1

2

3

R

0.0147*** (143.12) 0.0134*** (143.49) 0.0207*** (142.77) 0.0205*** (97.83)

0.2519*** (25.07) 0.2697*** (25.67) 0.1669*** (11.77) 0.1253*** (14.92)

−1.0911*** (-14.05) −1.8086*** (-9.89) −1.6986*** (-23.98) −0.6784*** (-8.69)

−0.3654*** (-8.01) −0.2198*** (-7.29) −0.0027 (-0.34) −0.0017 (-0.50)

0.126

0.0149*** (103.48) 0.0142*** (121.63) 0.0196*** (104.74) 0.0179*** (153.53)

0.1999*** (14.86) 0.2279*** (19.17) 0.1288*** (6.89) 0.1479*** (14.61)

−1.2648*** (-16.12) −0.6826*** (-3.38) −0.9507*** (-21.28) −0.5070*** (-12.35)

−0.2238*** (-7.41) −0.2092*** (-7.86) −0.0032 (-0.29) 0.0012 (0.03)

0.044

0.136 0.094 0.029

0.148 0.092 0.102

0

1

2

3

R2

0.0121*** (108.22) 0.0125*** (63.77) 0.0079*** (85.13) 0.0092*** (95.08)

0.2859*** (21.76) 0.0539 (1.44) 0.3430*** (30.32) 0.1779*** (11.65)

−2.0697*** (-19.87) −2.7115*** (-3.61) −0.5125*** (-12.01) −0.7926*** (-5.73)

−0.0178*** (-5.92) −0.0244*** (-3.13) −0.1851*** (-6.24) −0.1136*** (-5.59)

0.192

0.0143*** (122.53) 0.0134*** (148.68) 0.0111*** (132.27) 0.0116*** (156.59)

0.1903*** (13.08) 0.1288*** (14.23) 0.2764*** (26.87) 0.1695*** (20.82)

−1.7131*** (-12.12) −1.6939*** (-11.68) −0.1082*** (-18.33) −0.2729*** (-6.02)

−0.0174*** (-3.92) −0.0182*** (-4.23) −0.2354*** (-5.13) −0.1717*** (-4.48)

0.181

0.158 0.281 0.132

0.027 0.226 0.068

In this table, we present   regression coefficients for the following equations.

CSADA,t = 0 + 1 RA,t  + 2 (RA,t )2 + 3 (RB,t )2 + εt





CSADB,t = 0 + 1 RB,t  + 2 (RB,t )2 + 3 (RA,t )2 + εt

We use daily data returns for firms listed on A- and B-markets in SHSE and SZSE. Full period spans from 1/1/1996 to 12/31/2012. Pre- and post-liberalization periods span over the periods 1/1/1996-12/31/2000 and 7/1/2003-12/31/2012, respectively. Both equations are estimated for down and up markets. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. t-statistics are presented in parenthesis and they are based on Newey and West (1987)’s heteroscedasticity and autocorrelation consistent with standard errors.

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Table 5 Lead-lag cross market herding relationship between A- and B-market Coefficient

Pre-Liberalization

Post-Liberalization

d−4

0.0168 (0.58) 0.0207 (1.08) 0.0314* (2.11) 0.0330* (2.55) 0.0537* (3.18) 0.0221 (1.41) 0.0156 (0.95) 0.0082 (0.23) 0.0037 (0.58) 1.91 5.67*

0.0154 (1.17) 0.0238 (1.49) 0.0445* (2.68) 0.0492* (3.40) 0.0866* (4.27) 0.0339* (2.01) 0.0218 (1.39) 0.0140 (0.88) 0.0022 (0.46) 2.80* 8.09*

d−3 d−2 d−1 d0 d+1 d+2 d+3 d+4 Flag Flead

In this table we report results from Eq. (10): CSADB,t = d0 +

+4 

dk CSADA,t+k + εB,t (10)

k=−4

where CSADB,t are B shares’ cross-sectional absolute deviation for day t, and CSADA,t+k are A shares’ cross-sectional absolute deviation for day t + k. The t -statistics are reported in parentheses. Flead is the F -statistic that tests whether d−1 = d−2 = d−3 = d−4 = 0. Similarly, Flag is the F -statistic that tests whether d+1 = d+2 = d+3 = d+4 = 0. Coefficients are reported for the pre- and post-liberalization periods. * indicate statistical significance at the 5% level. t-statistics are presented in parenthesis. Table 6 Testing for Herding with Fundamental and Non-Fundamental Information SHSE & SZSE

Fundamental

Non-Fundamental Post-Lib.

Pre-Lib.

A-market B-market

Pre- vs. Post-Lib. 2

Pre-Lib.

Post-Lib.

Pre- vs. Post-Lib.

3

3



p-Value

3

3

2

p-Value

−1.2073*** (-16.24) −0.1838*** (-5.64)

−1.9681*** (-9.30) −0.5114*** (-4.77)

16.08***

0.000

0.001

0.001

−0.7864*** (-8.75) −0.1230*** (-4.58)

12.31***

12.94***

−1.7518*** (-5.29) −0.0384 (-0.973)

17.85***

0.000

In this table, we present results from Eqs. (12) and (13). While Eq. (12) tests for herding due fundamental information, Eq. (13) tests for herding due to non-fundamental information.   CSADFUND,t = ˛ + 1 Rm,t + 2 Rm,t  + 3 (Rm,t )2 + εt , (12)





CSADNONFUND,t = ˛ + 1 Rm,t + 2 Rm,t  + 3 (Rm,t )2 + εt , (13)

To calculate CSADFUND,t and CSADNONFUND,t , we decompose the CSADt measure as follows: CSADt = ˇ0 + ˇ1 (Rm,t − RF ) + ˇ2 HMLt + ˇ3 SMBt + ˇ4 MOM t + εt . Knowing that εt represents return dispersion due to investors responding to non-fundamental information, we can conclude that return dispersion due to fundamental information as follows: CSADFUND,t = CSADt − CSADNONFUND,t . We use daily data returns for firms listed on A- and B-markets in SHSE and SZSE. Pre- and post-liberalization periods span over the periods 1/1/1996-12/31/2000 and 7/1/2003-12/31/2012, respectively. 21 and p − Value1 are the Chi-squared statistic for the Wald test for the null hypothesis H0 : 3Pre = 3Post . *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. t-statistics are presented in parenthesis and they are based on Newey and West (1987)’s heteroscedasticity and autocorrelation consistent with standard errors.

5.3. Herding due to fundamental and non-fundamental information In Table 6, we provide the results from our analysis of herding behavior due to fundamental and non-fundamental information. The decomposition process requires the estimation of Eq. (11). Given the small number of firms with A- and B-shares in SHSE (45 firms) and SZSE (42 firms), we combined firms from both markets to enhance the accuracy of the estimates from Eq. (11). For comparison purposes, we report the results from the pre- and post-liberalization periods in this table side-by-side. In the case of the A-market, investors seem to herd due to fundamental and non-fundamental information before and after market reforms. Nonetheless, while the magnitude of herding due to fundamental information—spurious herding—increases after liberalization, herding due to non-fundamental information—intentional herding—decreases during the same period. Results from two-sided Wald tests reject the null hypothesis H0 : 2Pre = 2Post at the 1% level for both fundamental and non-fundamental herding. However, a one-sided test of the hypothesis 2Pre < 2Post is rejected at the 5% level for only non-fundamental herding (results not shown). These findings may indicate an informationally richer environment

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Table 7 Testing for Within- and Between-Group Herding in the A- and B-share Markets SHSE & SZSE

Pre-Liberalization

Post-Liberalization

Panel A: positive alpha stocks regression 3 2 A-Market −0.0016*** −0.0140*** (-2.79) (-4.46) B-Market −0.0020*** −0.0115*** (-5.51) (-8.05)

4 −0.0762*** (-8.32) −0.2793 (-0.23)

5 1.0459*** (12.96) 0.7306 (0.22)

2 −0.0018*** (-4.93) −0.0027*** (-4.24)

3 −0.0147*** (-3.52) −0.0126*** (-9.92)

4 0.0120*** (3.26) 0.1831 (0.42)

5 1.9432*** (7.82) 1.7061 (0.86)

Panel B: negative alpha stocks regression 9 8 A-Market −0.0016 −0.0094*** (-5.99) (-5.31) B-Market −0.0024 −0.0132 (-19.13) (-0.62)

10 0.2303*** (6.08) 0.2158 (0.89)

11 1.0862*** (9.39) 0.6965 (0.41)

8 −0.0015*** (-14.67) −0.0014*** (-7.77)

9 −0.0093*** (-8.09) −0.0086 (-0.85)

10 0.1222** (2.13) −0.0142 (-0.34)

11 1.8645*** (8.68) 1.6874 (0.94)

In this table, we report regression for Eqs. (14) and (15). In these two equations, we test for within- and between-group herding.  coefficients 

2 2 + 4 R−m,t + 5 CSAD−,t + et (14) CSAD+,t = 0 + 1 R+m,t + 2 R+m,t  + 3 R+m,t





2 2 + 10 R+m,t + 11 CSAD+,t + et (15) CSAD−,t = 6 + 7 R−m,t + 8 R−m,t  + 9 R−m,t

While CSAD+,t measures return dispersions using a portfolio with positive alphas, CSAD−,t measures return dispersions using a portfolio with negative alphas. In order to calculate stock alphas, we use Carhart (1997) four-factor model as follows: Rt = ˛0 + ˛1 (Rm,t − RF ) + ˛2 HMLt + ˛3 SMBt + ˛4 MOM t + εt . In Panel A, we report results from our analysis of herding among the portfolio with positive alphas, while Panel B includes results from the portfolio with negative alphas. We use daily data returns for firms listed on A- and B-markets in SHSE and SZSE. Pre- and post-liberalization periods span over the periods 1/1/1996-12/31/2000 and 7/1/2003-12/31/2012, respectively. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. t-statistics are presented in parenthesis and they are based on Newey and West (1987)’s heteroscedasticity and autocorrelation consistent with standard errors.

in China after market liberalization, which induces information-based trading while discouraging non-information-based herding. This implication is consistent with other studies that document improved informational efficiency in the Chinese local equity market due to market liberalization (Carpenter et al., 2015; Chan et al., 2008; Chi, 2014). Additionally, the results from the B-market show that investors exhibit herding behavior due to fundamental information before and after market liberalization. Similar to the A-market, the extent of herding due to fundamental information in the B-market has declined after the opening of the B-market. On the contrary, investors in the B-market herd due to non-fundamental information only after market liberalization. This particular finding is very interesting as it shows that non-information-based or intentional herding did not exist among B-market traders before this market opened. During that period, only foreign institutional investors were trading in B-shares. This finding may imply that foreign institutional investors are rational but uninformed, which is consistent with our earlier findings and those of other studies including Chan et al. (2008) that consider foreign institutional investors in China to be potentially informationally disadvantaged relative to local investors.

5.4. Herding within and between rational and irrational traders Table 7 presents the results based on Eqs. (14) and (15), where we examine within- and between-group herding. As indicated earlier, arbitrageurs are sophisticated traders and so are likely to pick outperforming stocks (stocks with positive alphas), Noise traders are less sophisticated and rely on sentiments when trading, making them more likely to pick underperforming stocks (stocks with negative alphas). The coefficients of interest in these two equations are 3 and 9 (measuring within-group herding), 4 and 10 (measuring between-group herding), and 5 and 11 (measuring interactions between the two groups). The results from the A-market show that 3 and 9 are negative and statistically significant during the preand post-liberalization periods, which indicates that both arbitrageurs and noise traders exhibit herding behavior among themselves, although not necessarily for the same reasons. On the other hand, the same coefficients, 3 and 9 , are negative in the B-market, but statistically significant only for arbitrageurs (positive alpha stocks). These results could be due to the absence of noise traders in the B-market; therefore, we fail to document any herding within this group. While foreign institutional investors had the exclusive right to trade B-shares prior to market liberalization, mainly local institutional investors have full access to the B-market after market liberalization, given their ability to access the foreign exchange market freely. Furthermore, the results for between-group herding in the A- and B-markets reveal some interesting findings. The coefficient 4 , representing between-group herding for arbitrageurs, is negative and statistically significant for A-shares before market liberalization but positive and statistically significant thereafter. This implies that while arbitrageurs in the A-market tended to herd with noise traders during the pre-liberalization period, they do not exhibit the same behavior postliberalization. We can attribute these results to the fact that the presence of local institutional investors (mutual funds) was

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very limited during that period (Chi, 2014).14 On the other hand, according to Dang and Lin (2016), a possible justification for a positive sign on 4 after market liberalization is that arbitrageurs can now recognize when noise traders herd. Thus, instead of trading to bring asset prices back to their fundamental values, they take advantage of the situation by adjusting their holding arbitrarily, thereby causing CSAD+,t to rise rather than fall. This finding is consistent with that of Dang and Lin (2016) who also find that 4 for investors in the Vietnamese market is positive over the period 2007-2015. The same coefficient, 4 , for investors in the B-market is statistically insignificant before and after market reforms, implying that noise traders’ transactions did not influence arbitrageurs in the B-market. Again, this result supports our earlier finding that noise traders may not have a strong presence in the B-market before or after market reforms; therefore, we fail to find any significant activities for this group of traders. The coefficient 10 , representing between-group herding for noise traders, is positive and statistically significant in the A-market before and after market liberalization. However, its magnitude drops significantly during the post-liberalization period. These results provide clear evidence that noise traders are not as sophisticated as the arbitrageurs in the A-market. The lack of ability to identify positive alpha stocks causes noise investors to trade arbitrarily without any reasonable basis, and thus their return dispersion measure CSAD−,t rises with the returns of positive alpha stocks. For the B-market, 10 is statistically insignificant in pre- and post-liberalization periods. As indicated earlier, we could attribute this to the absence of noise traders in the B-market. Finally, the coefficients measuring the co-movements of return dispersions between arbitrageurs and noise traders, 5 and 11 , are positive and statistically significant in the A-market before and after liberalization but not so in the B-market. This finding is consistent with that of Dang and Lin (2016), which implies that traders with different information interact with each other, even though they may not herd together. Moreover, we do not observe similar results for the B-market. Again, this could be because the vast majority of B-share traders are institutional investors (sophisticated investors). According to Dang and Lin (2016), a possible explanation for the positive sign of 5 and 11 in the A-market is that when arbitrageurs (rational investors) receive similar fundamental information, they make similar investing decisions, resulting in a low CSAD+,t . On the other hand, noise traders (irrational investors) are likely to disregard such similar information and instead follow market consensus, which leads to a low CSAD−,t . 6. Summary and remarks This study examines herding behavior using data of 87 Chinese stocks listed on both the A- and B-markets. We evaluate different aspects of herding behavior under different information environments. More specifically, we examine changes in unconditional herding in the A- and B-markets around the liberalization of both markets. We also account for cross-market information flow between the two markets over the same period. Additionally, following Galariotis et al.’s (2015) approach, we decompose our return dispersion measure, CSADt , into deviations due to fundamental and non-fundamental information. Last, we apply Dang & Lin’s (2016) approach to investigate herding behavior within- and between-groups depending on their level of sophistication. Our findings suggest that herding is present in both the A- and B-markets before and after market liberalization. However, while the magnitude of herding tendency remains relatively unchanged in the A-market post liberalization, it has dropped significantly in the B-market. We attribute this shift in herding tendency in the B-market to the fact that more domestic institutional investors—known for being more sophisticated—are now trading B-shares. This explanation is consistent with findings from other studies that indicate that local investors in China are better informed than foreign investors (Chan et al., 2008, among others), and they, therefore, mitigate the extent of herding upon joining the B-market. Our analysis of herding due to fundamental versus non-fundamental information also shows that investors in the Amarket exhibit information-based and non-information-based herding before and after market liberalization. However, interestingly, while the extent of information-based herding (herding with fundamentals) increased after liberalization, non-informational herding (herding with non-fundamentals) reduced significantly over the same period. Consistent with the view that market liberalization improved the information environment in China (Carpenter et al., 2015; Chan et al., 2008; Chi, 2014), our findings suggest that investors in the A-market now rely more on fundamental information when trading. Finally, when analyzing within- and between-group herding, we find strong evidence in favor of herding within and between arbitrageurs and noise traders around market liberalization in the A-market, but this is less apparent in the B-market. We attribute this to the dominance of sophisticated traders (foreign and domestic institutional investors) in the B-market and less sophisticated traders (domestic individual investors) in the A-market. The results of this study have important implications. We shed light on the reliance of herding propensity on the information environment in the Chinese market and show that investors are more likely to herd when they are informationally disadvantaged. At the same time, investors are less likely to herd when they are more knowledgeable and better informed. These findings are consistent with the view that herding propensity is likely to decrease when the proportion of informed investors relative to uninformed investors is large. Finally, the results we present here show the importance of sophisticated

14 The first mutual stock fund in China was established in 1998. Until market liberalization in 2001-2002, the ratio of these mutual funds relative to Chinese stock market capitalization was around 5%. However, it grew significantly later and peaked in 2007, reaching 21% of total market capitalization (Chi, 2014).

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Further reading Alhaj-Yaseen, Y.S., Rao, X., Jin, Y., 2017. Market liberalization and the extent of informed trading: evidence from China’s equity markets. J. Multinatl. Financ. Manag. 39, 78–99. Sias, R.W., 2004. Institutional herding. Rev. Financ. Stud. 17 (1), 165–206.