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The existence of herding behaviour challenges the validity of the “efficient market hypothesis”. This study examines herding behaviour in the Chinese and Indian stock markets; our findings suggest that herding behaviour exists in both. The level of herding depends on market conditions. In the Chinese market, herding behaviour is greater when the market is falling and the trading volume is high. On the other hand, in India the study finds that it occurs during up-swings in market conditions. Herding behaviour is more prevalent during large market movements in both markets. In relative terms, a lower prevalence of herding behaviour was detected in the Indian stock market.

Human beings have a very long history of demonstrating herding behaviour, which can be traced back to the early Iron Age, approximately 1200 BC (Wallace, 2003). At the end of the Nineteenth Century, early social psychologists coined the term to describe collective behaviour carried out by crowds with “irrational, impulsive and primitive emotions” (LeBon, 1895, as cited in Vaughan & Hogg, 2005). Following the development of social psychology in the Twentieth Century, many social psychologists believe that herding behaviour is not necessarily irrational and subconscious but is rather linked to a human need for conformity (Rook, 2006). Thus, the psychological definition of herding behaviour has evolved and been referred to as a scenario in which individuals abide by the group decision, even when they perceive the group to be wrong (Christie and Huang, 1995 and Rook, 2006). People have a tendency to maintain an interest in what others are doing and, at times, follow them while overlooking their own analytical skills. Therefore, herding is believed to be a human instinct and is always present in human decision-making processes; this is useful in explaining investors’ behaviour which cannot otherwise be understood through an Efficient Market Hypothesis (EMH) (Chang, Cheng, & Khorana, 2000). From a financial perspective, herding behaviour can be understood in two dimensions—irrational and rational. Basically, the former views herding as a tendency of investors who irrationally ignore their own analysis and information and conform to the market consensus, even if they do not agree with it (Christie & Huang, 1995). Investors do so because it reduces their uncertainty and fulfils their need to feel confident (Vaughan & Hogg, 2005). On the other hand, the latter rational behaviour is driven by the need to protect one's reputation. This occurs commonly among employees or agents (such as traders, fund managers and analysts) in a financial institution because their performance evaluation is done on a comparative basis. Therefore, they are tempted to lift their performance and reputation by intentionally ignoring their own analysis and replicating another manager who possesses a more reliable information source or better analytical skill decisions (Cote and Goodstein, 1999 and Devenow and Welch, 1996). It may also occur among individual investors. They may rationally herd in the direction of others who they believe may be better informed and possess information which is unavailable to the market. By doing so, their performance would not be below the market average (Demirer & Kutan, 2006). Therefore, both explanations of herding behaviour illustrate that investors do not base their decisions on their own analysis and information, but follow the market consensus. In addition, (Nofsinger & Sias, 1999) suggest herding behaviour occurs when investors trade in the same direction over time. The existence of herding behaviour challenges the validity of an EMH, which believes that all investors are rational and possess the same set of information and, therefore, form the expected stock price in the same way. As a result, the stock price should reflect the information available in the market and the security's true value (Fama, 1970). Nonetheless, as mentioned above, herding behaviour suggests that investors are not necessarily rational and do not always derive the share price by rational analysis of firms, but by observing and following other investors’ actions, even though not all market participants are fully informed. Thus, herding behaviour may destabilize the market by moving securities away from their fundamental value, as share prices will not only reflect the investors’ rational expectations of the shares, but also investors’ irrational decisions in the market (Demirer and Kutan, 2006 and Hott, 2009). Thus, an EMH may fail to account for the existence of herding behaviour in a stock market. This study investigates the level of herding behaviour in the Chinese and Indian stock markets. China and India share some fundamental factors which are essential in maintaining long-term economic growth and, therewith, have the ability to become giant economies. These factors include: vast geographical area, large labour forces, supportive government policies and the emergence of a middle class. The purchasing power of these two countries has grown continuously following their high-speed economic development. It is expected that by 2050 combined Chinese and Indian spending could be larger than that of the Group of Six, which is estimated to be sufficient to offset the negative impact of ageing populations and low economic growth in developed countries (Samitas, Kenourgios, & Paltalidis, 2000). Managers of large funds and other international institutional investors are recognising these profit-making opportunities coupled with the growing importance of these markets and, therefore, are putting more weight in their investment portfolios on the Chinese and Indian stock markets. Chang et al. (2000) show that the study of herding behaviour is important as share prices are substantially affected by market participants’ investment behaviour. It has been linked to market inefficiencies which cannot be explained by the rational asset pricing model, such as high market volatility and market destabilisation. Tan, Chiang, Mason, and Nelling (2008) indicate that herding behaviour can increase market volatility and arbitrage opportunities. When an investor's investment decision is based on collective information rather than private information, the fundamental value of the stock might be lower than its true value. Therefore, herding behaviour is a signal of market inefficiency. Consequently, the existence of herding behaviour suggests that the real world market is not as efficient as the rational asset pricing model would suggest. The accuracy of stock valuation using this model may be eroded by a market anomaly, such as herding behaviour, which is not taken into account during the valuation process. In addition, investigating herding allows us to further understand investors’ thought processes and its effect on their investment decisions. For example, herding among money managers is believed to be a result of their fear of being poorly assessed or judged by others if they make the wrong decision (Scharfstein & Stein, 1990). The Chinese and Indian stock markets have long been criticised as inefficient and riskier as a result of less-educated investors, cultural differences, incomplete security laws and weak law enforcement, loose accounting report requirements and low level information disclosure (see Akbar and Samii, 2005, Khanna and Palepu, 2000, Li, 2008 and Zhang and Zhao, 2004). These failures have been linked to the existence of herding behaviour and other market anomalies within emerging markets (Chang et al., 2000 and Demirer and Kutan, 2006). Examining herding behaviour can grant investors a higher degree of understanding regarding price formation in financial markets. Social psychologists believe people have a need to follow others in order to feel assured in their decision-making when they encounter uncertainty, ambiguous information and disagreements (Vaughan & Hogg, 2005). As the Chinese and Indian stock markets are always characterised as inefficient and having low standards of information disclosure, the information irregularity for different kinds of investors is more serious than in developed markets with better disclosure requirements. In addition, the Chinese and Indian cultures are collective-oriented and encourage people to follow more than criticize (Chen et al., 2007). Consequently, rather than standing by their own opinions, investors may be inclined to herd towards perceived informed investors (Demirer and Kutan, 2006). Such behaviour may move securities away from their price equilibrium and lead to abnormal volatility in those markets (Chang et al., 2000). There is vast research concerning herding behaviour in developed stock markets; very few studies, however, have investigated such behaviour in developing markets in a comparative format. Thus, this study attempts to fill the gap by investigating herding behaviour in the Chinese and Indian stock markets. Our results suggest that herding behaviour exists in both stock markets but that it is greater in the Chinese stock market. In both markets, herding behaviour is more predominant during large market movements. Asymmetric herding behaviour in Chinese stock markets is greater when the market is in decline and trading volume is high. Herding behaviour in the Indian market is mostly detected during increased market activity and seems to be unrelated to the level of trading volume. The remainder of this paper will be organized as follows: Section 2 presents a review of the previous literature concerning different issues and aspects of herding. Section 3 describes the data collection and methodology utilised. Section 4 discusses the results, and Section 5 provides the conclusion.

We have examined herding behaviour in the Chinese and Indian stock markets. Herding behaviour is a recognised psychological concept which is used in the financial world to explain the scenario in which investors, rationally or irrationally, mimic others’ decisions. Such behaviour is believed to be associated with causing market inefficiency and abnormal market volatility. Considering the increasing influence of China and India in the global financial sector, investors across the world are increasing the weight of these two markets in their investment portfolios. However, Chinese and Indian stock markets are believed to be riskier and less mature than other more developed stock markets. Consequently, investigating herding behaviour in the stock markets in these two countries may help to identify the potential risks and guide the formation of proper strategy while investing in these markets. Using the CSAD approach proposed by Tan et al. (2008), this paper measures the herding behaviour in the sampled stock markets from 1999 to 2009. The findings suggest herding behaviour is present in both the Chinese and Indian stock markets, and the results reveal that herding behaviour is greater during extreme market conditions in both markets. The study also finds that the Chinese and Indian markets exhibit different herding behaviour patterns. In the Chinese market, herding behaviour is greater when the market is falling and the trading volume is high; on the other hand, in India, the study finds it occurs during upswings in market trends. Also, unlike the Chinese market, herding behaviour seems to be irrelevant to the level of trading volume in India. Herding behaviour is robust across different-size groups of shares in the Chinese stock market, and is apparent only in middle-size shares in the Indian stock market. Further, herding behaviour in Chinese markets is stronger due to the negative impacts of the global financial crisis. These results imply that stronger governing bodies and stricter stock market regulations are needed in the Chinese stock market. Current policies have proved ineffective in promoting market efficiency and mitigating herding behaviour. The Indian stock market, on the other hand, has benefited from the huge influence of large financial institutions. Their appearance brings more rational security analysis and decreases the overall level of speculative investment activities in the stock market. Future research should attempt to distinguish between different groups of investors’ herding behaviour because different types of herding behaviour may be being exhibited.