Herding behavior in Chinese stock markets: An examination of A and B shares (original) (raw)
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Market Regimes and Herding Behavior in Chinese A and B Shares
This paper presents a dynamic analysis of herding behavior in China's segmented stock markets in a regime-changing framework. Using firm-level data on the A-shares (traded only by domestic investors) and B-shares (dominated by foreign institutional investors), we examine information flows between market segments during low, high and extreme (or crash) volatility regimes. The findings suggest that herding behavior is asymmetric, observed during the high and crash volatility periods only. We also find significant cross market herding effects between the A-and B-share markets, suggesting that foreign institutional investors (in the B-share markets) engage in herding behavior by following domestic investors (in the A share markets). Finally, we find that domestic investors in China herd around the Hong Kong market during the high and crash volatility regimes, whereas no significant U.S. herding effect is observed. JEL Classification Code: C32, G11, G15
Journal of International Financial Markets, Institutions and Money, 2020
This paper examines the influence of China's cross-sectional dispersion of returns on local markets, as well as its major trading partners. With the cross-sectional average deviation method, we have reported some significant and insignificant results, as well as for tranquil and turbulent phases of the Chinese stock market. It seems that the cross-sectional dispersion of returns in China has influenced markets in countries in the greater China region and some other Asian countries but not markets in Europe or the United States. Although China's cross-sectional dispersion of returns plays a role in influencing the country's trading partners, which are categorised as having high, medium or low trade volumes, it does not cause any of these groups to herd around its market. Thus we conclude that although China is the world's second-largest economy after that of the United States, China's role in stock trading is still unmatched by the United States.
Investor herding behaviour of Chinese stock market
International Review of Economics & Finance, 2014
This paper examines the existence and prevalence of investor herding behaviour in a segmented market setting, the Chinese A and B stock markets. It is the first study to detail the difference in herding behaviour across A and B markets. The results indicate that investors exhibit different levels of herding behaviour, in particular, herding strongly exists in the B-share markets. We also find that across markets herding behaviour is more prevalent at industry-level, is stronger for the largest and smallest stocks, and is stronger for growth stocks relative to value stocks. Herding behaviour is also more pronounced under conditions of declining markets. Over the sample period we are examining, herding behaviour diminishes over time. The results provide some indication to the effectiveness of regulatory reforms in China aimed at improving information efficiency and market integration.
Does herding behavior exist in Chinese stock markets?
Journal of International Financial Markets, Institutions and Money, 2006
This paper examines the presence of herd formation in Chinese markets using both individual firm-and sector-level data. We analyze the behavior of return dispersions during periods of unusually large upward and downward changes in the market index. We also distinguish between the Shanghai and Shenzhen stock exchanges at the sector-level. Our findings indicate that herd formation does not exist in Chinese markets. We find that equity return dispersions are significantly higher during periods of large changes in the aggregate market index. However, comparing return dispersions for upside and downside movements of the market, we observe that return dispersions during extreme downside movements of the market are much lower than those for upside movements, indicating that stock returns behave more similarly during down markets. The findings support rational asset pricing models and market efficiency. Policy implications of the results for policymakers are discussed.
When Will Investors Herd?: Evidence from the Chinese Stock Markets
The institutional characteristics of the Chinese stock markets provide a unique perspective to study the herding behavior of investors. If domestic investors are more knowledgeable or informed about individual stocks than foreign investors, herding behavior is most likely to occur among foreign investors. Our empirical results indicate that during periods of extreme price movements, the relative equity return dispersions for both Shanghai-B and Shenzhen-B actually have decreased, which provides evidence for herd behavior. This result is robust to a different specification which controls for informational trading. However, for both Shanghai-A and Shenzhen-A, we find mixed and weaker results to support for herding. Since B-share investors are foreign investors, the differential herding behavior of local and foreign investors suggest that in the presence of inefficient information disclosure, foreign participants tend to trade according to other signals and to herd due to lack of fundamental and private information on firms. We also propose an alternative approach that involves trading volume to detect herding behavior. After controlling for informational effect, we continue to find strong support for herding activities in the B-share markets. Our findings are robust in terms of portfolio size, industry grouping, and GARCH specifications.
The impact of idiosyncratic volatility on the investors' herd behavior in the Chinese Stock Market
Zenodo (CERN European Organization for Nuclear Research), 2022
This study provides a comprehensive study of herding behavior in the Chinese Stock Market using the cross-sectional absolute deviation of returns method (CSAD) proposed by (Chang et al., 2000), which captures the non-linearity relationship between the dispersion of individual returns and market return. According to (Christie & Huang, 1995) and (Chang et al., 2000), in a stock market, herding behavior occurs when individual returns begin to converge towards the consensus of the market, leading to a decrease in the dispersion of stock return from the market return. More particularly, this study inspects the impact of idiosyncratic volatility on the investors' herd behavior in the Chinese Stock Market by delving deeper into the nature of herding and its asymmetric effect under extreme market conditions and at various stages of idiosyncratic volatility, as well as herding frequency and its asymmetric effect in increasing and falling markets. The results of this study indicate that idiosyncratic volatility is an essential component and determinant of herding conduct. The findings indicate that herding occurs in the Chinese stock market, and exhibits diverse patterns under different equity portfolios according to the levels of idiosyncratic volatility as well as the market trend, and that investment behavior tends to be different during three subperiods. Moreover, the findings document that Financial Crisis period increases herding, especially within stock portfolios with higher idiosyncratic volatility.
Time-Varying Herding Behavior, Global Financial Crisis, and the Chinese Stock Market
Review of Pacific Basin Financial Markets and Policies, 2015
In this paper, we examine evidence of herding behavior on the Chinese stock market. Our main findings are as follows. First, we find strong evidence of herding behavior on both the Shanghai and Shenzhen stock exchanges. Second, we document evidence of asymmetric herding behavior with greater magnitude of herding behavior on up markets than on down markets. Third, our findings suggest that herding behavior is sectorspecific and predominant in the industrial and properties sectors. Finally, we unravel strong evidence suggesting that herding behavior is time-varying and in some sectors time-varying herding behavior is more prevalent than in other sectors.
Empirical Analysis of Herding Behavior in Asian Stock Markets
Chapman & Hall/CRC Finance Series, 2009
Christie and Huang (1995) noted that the investment decision-making process used by market participants depends on overall market conditions. In particular, during normal periods, rational asset-pricing models
Regime-switching herd behavior: Novel evidence from the Chinese A-share market
Finance Research Letters, 2020
We examine time-variations of herd behavior by proposing a Markov regime-switching model. Our model can not only infer the hidden market state which drives the time-varying herd behavior, but can also capture the empirical characteristics. We conduct a comprehensive empirical analysis of the Chinese A-share market. We find evidence that herding is prominent in volatile regimes, while adverse herding is prevalent during tranquil regimes. Moreover, we conduct a simulation example to explain why previous studies on herding presented conflicting results. Finally, we check for herding effects at factor and industry levels, and employ multiple testing to integrate all the results. 1. Introduction Understanding the behavior of financial market participants has always been a topic of strong interest for both academics and practitioners. Keynes (1936) likened professional investors to beauty-competition participants who make their decisions according to what other competitors think instead of the absolute beauty of the contestants. This comparison vividly reflects how investors tend to mimic each other and follow the group behavior instead of making independent investment decisions. In the study of behavioral finance, this type of investor behavior is referred to as herd behavior. Analyzing herd behavior can enhance our understanding of the stock markets bubble accumulation and the microstructure behind it. Such investigation also motivates investment strategies, provides guidance to risk management and policy making. Currently two research approaches exist to study herd behavior in stock markets. Researchers in the first domain construct behavioral finance models or make use of detailed transaction data to study the behavior of fund managers, speculators, and market makers, etc.