An examination of herd behavior in equity markets: An international perspective

We examine the investment behavior of market participants within di€erent international markets (i.e., US, Hong Kong, Japan, South Korea, and Taiwan), speci®cally with regard to their tendency to exhibit herd behavior. We ®nd no evidence of herding on the part of market participants in the US and Hong Kong and partial evidence of herding in Japan. However, for South Korea and Taiwan, the two emerging markets in our sample, we document signi®cant evidence of herding. The results are robust across various size-based portfolios and over time. Furthermore, macroeconomic information rather than ®rm-speci®c information tends to have a more signi®cant impact on investor behavior in markets which exhibit herding. In all ®ve markets, the rate of increase in security return dispersion as a function of the aggregate market return is higher in up market, relative to down market days. This is consistent with the directional asymmetry documented by McQueen et al. (1996) (McQueen, G., Pinegar, M.A., Thorley, S., 1996. Journal of Finance 51, 889±919). Ó 2000 Elsevier Science B.V. All rights reserved. JEL classi®cation: G15 Keywords: International capital markets; Herd behavior; Equity return dispersion; International ®nance Journal of Banking & Finance 24 (2000) 1651±1679 www.elsevier.com/locate/econbase * Corresponding author. Tel.: +1-404-894-5110; fax: +1-404-894-6030. E-mail address: [email protected] (A. Khorana). 0378-4266/00/$ see front matter Ó 2000 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 4 2 6 6 ( 9 9 ) 0 0 0 9 6 5 1. Introduction Academic researchers have devoted considerable e€ort in understanding the investment behavior of market participants and its ensuing impact on security prices. The investment behavior of market participants has been linked to factors such as investorÕs investment horizons, the benchmarks used to measure performance, the behavior of other market participants, the degree of underlying market volatility, and the presence of fads and speculative trading activity in the ®nancial markets. In this paper, we investigate the investment behavior of market participants within di€erent international markets, speci®cally with regard to their tendency to mimic the actions of others, i.e., engage in herd behavior. Herding can be construed as being either a rational or irrational form of investor behavior. According to Devenow and Welch (1996), the irrational view focuses on investor psychology where investors disregard their prior beliefs and follow other investors blindly. The rational view, on the other hand, focuses on the principal±agent problem in which managers mimic the actions of others, completely ignoring their own private information to maintain their reputational capital in the market (Scharfstein and Stein, 1990; Rajan, 1994). 1 Bikhchandani et al. (1992) and Welch (1992) refer to this behavior as an informational cascade. In a recent empirical study, Christie and Huang (1995) examine the investment behavior of market participants in the US equity market. By utilizing the cross-sectional standard deviation of returns (CSSD) as a measure of the average proximity of individual asset returns to the realized market average, they develop a test of herd behavior. In particular, they examine the behavior of CSSD under various market conditions. They argue that if market participants suppress their own predictions about asset prices during periods of large market movements and base their investment decisions solely on aggregate market behavior, individual asset returns will not diverge substantially from the overall market return, hence resulting in a smaller than normal CSSD. In this paper, we extend the work of Christie and Huang (1995) along three dimensions. First, we propose a new and more powerful approach to detect herding based on equity return behavior. Using a non-linear regression speci®cation, we examine the relation between the level of equity return dispersions (as measured by the cross-sectional absolute deviation of returns, i.e., CSAD), and the overall market return. In the presence of severe (moderate) herding, we 1 Herd behavior can become increasingly important when the market is dominated by large institutional investors. Since institutional investors are evaluated with respect to the performance of a peer group, they have to be cautious about basing their decisions on their own priors and ignoring the decisions of other managers. In fact, Shiller and Pound (1989) document that institutional investors place signi®cant weight on the advice of other professionals with regard to their buy and sell decisions for more volatile stock investments. 1652 E.C. Chang et al. / Journal of Banking & Finance 24 (2000) 1651±1679 expect that return dispersions will decrease (or increase at a decreasing rate) with an increase in the market return. Second, we examine the presence of herding across both developed and developing ®nancial markets including the US, Hong Kong, Japan, South Korea, and Taiwan. Examining herding is interesting in an international context since di€erences in factors such as the relative importance of institutional versus individual investors, the quality and level of information disclosure, the level of sophistication of derivatives markets, etc., can a€ect investor behavior in these markets. Third, we test for shifts in herd behavior subsequent to the liberalization of Asian ®nancial markets. Our empirical tests indicate that during periods of extreme price movements, equity return dispersions for the US and Hong Kong continue to increase linearly, hence providing evidence against the presence of herd behavior. The results for the US are consistent with those documented by Christie and Huang (1995). However, for South Korea and Taiwan, the two emerging markets in our sample, we ®nd a signi®cant non-linear relation between equity return dispersions and the underlying market price movement, i.e., the equity return dispersions either increase at a decreasing rate or decrease with an increase in the absolute value of the market return. Interestingly, in all ®ve markets, the rate of increase in return dispersion (as measured by CSAD) as a function of the aggregate market return, is higher when the market is advancing than when it is declining. This is consistent with the directional asymmetry documented by McQueen et al. (1996) where all stocks tend to react quickly to negative macroeconomic news, but small stocks tend to exhibit delayed reaction to positive macroeconomic news. We also document that in South Korea and Taiwan, where the evidence in favor of herding is most pronounced, systematic risk accounts for a relatively large portion of overall security risk. This evidence is consistent with the view that the relative scarcity of rapid and accurate ®rm-speci®c information in emerging ®nancial markets may cause investors to focus more on macroeconomic information. However, to the extent that investors react to any useful information, whether it is ®rm speci®c or market related, such type of behavior can be viewed as being rational. Furthermore, results of the size, i.e., market capitalization based portfolio tests, indicate that our herding results are not driven by either large or small capitalization stocks. In addition, the results for both South Korea and Taiwan remain relatively robust in various sub-period tests designed to capture shifts in investment behavior associated with the liberalization of these economies. We also conduct tests to examine whether the presence of daily price limits imposed on stocks in South Korea and Taiwan, are impacting our ®ndings. Our additional tests do not alter the overall evidence in favor of herding in the equity markets of South Korea and Taiwan. An important implication of investing in a ®nancial market where market participants tend to herd around the aggregate market consensus, is that a E.C. Chang et al. / Journal of Banking & Finance 24 (2000) 1651±1679 1653 larger number of securities are needed to achieve the same level of diversi®cation than in an otherwise normal market. The remainder of the paper is organized as follows. In Section 2, we provide methodological details and a description of the data. In Section 3, we provide a discussion of the empirical results and in Section 4 we provide concluding remarks and discuss implications of our ®ndings. 2. Methodology and data description 2.1. Methodology In this section, we develop an empirical methodology to detect the presence of herd behavior in international equity markets. Speci®cally, we propose an alternative, less stringent approach to the one suggested by Christie and Huang (1995) (henceforth referred as CH). While the two methods are similar in spirit, they do not always reach the same conclusion. We discuss the rationale behind the formulation of our approach and compare and contrast the two methods. CH suggest the use of cross-sectional standard deviation of returns (CSSD) to detect herd behavior in a market setting. The CSSD measure is de®ned as CSSDt ˆ  PN iˆ1…Ri;t ÿ Rm;t† N ÿ 1 s ; …1† where Ri;t is the observed stock return on ®rm i at time t and Rm;t is the crosssectional average of the N returns in the aggregate market portfolio at time t. This dispersion measure quanti®es the average proximity of individual returns to the realized average. 2 CH argue that rational asset pricing models predict that the dispersion will increase with the absolute value of the market return since individual assets di€er in their sensitivity to the market return. On the other hand, in the presence of herd behavior (where individuals suppress their own beliefs and base their investment decisions solely on the collective actions of the market), security returns will not deviate too far from the overall market return. This behavior will lead to an increase in dispersion at a decreasing rate, and if the herding is severe, it may lead to a decrease in dispersion. Therefore, 2 Other academic studies have also used variants of the return dispersion measure. For example, Bessembinder et al. (1996) use the absolute deviation of individual ®rm returns from the marketmodel expected returns as a proxy for ®rm-speci®c information ̄ows. Connolly and Stivers (1998) use the stock marketÕs cross-sectional dispersion to measure the uncertainty with regard to the underlying market fundamentals. Stivers (1998) also employs the cross-sectional return dispersion as a measure of the uncertainty faced by imperfectly informed traders in attempting to infer common factor innovations from news and prices. 1654 E.C. Chang et al. / Journal of Banking & Finance 24 (2000) 1651±1679 herd behavior and rational asset pricing models o€er con ̄icting predictions with regard to the behavior of security return dispersions. CH suggest that individuals are most likely to suppress their own beliefs in favor of the market consensus during periods of extreme market movements. Hence, CH empirically examine whether equity return dispersions are signi®cantly lower than average during periods of extreme market movements. They estimate the following empirical speci®cation: CSSDt ˆ a‡ bLDt ‡ bUDt ‡ et; …2† Dt ˆ 1, if the market return on day t lies in the extreme lower tail of the distribution; and equal to zero otherwise, and Dt ˆ 1, if the market return on day t lies in the extreme upper tail of the distribution; and equal to zero otherwise. The dummy variables are designed to capture di€erences in investor behavior in extreme up or down versus relatively normal markets. The presence of negative and statistically signi®cant b and b coecients would be indicative of herd behavior. CH use one or ®ve percent of the observations in the upper and lower tail of the market return distribution to de®ne extreme price movement days. In this paper, using the cross-sectional absolute deviation of returns (CSAD) as the measure of dispersion, we demonstrate that rational asset pricing models predict not only that equity return dispersions are an increasing function of the market return but also that the relation is linear. If market participants tend to follow aggregate market behavior and ignore their own priors during periods of large average price movements, then the linear and increasing relation between dispersion and market return will no longer hold. Instead, the relation can become non-linearly increasing or even decreasing. Our empirical model builds on this intuition. As a starting point in the analysis, we illustrate the relation between CSAD and the market return. Let Ri denote the return on any asset i, Rm be the return on the market portfolio, and Et… † denote the expectation in period t. A conditional version of the Black (1972) CAPM can be expressed as follows: Et…Ri† ˆ c0 ‡ biEt…Rm ÿ c0†; …3† where c0 is the return on the zero-beta portfolio, bi is the time-invariant systematic risk measure of the security, i ˆ 1; . . . ;N and t ˆ 1; . . . ; T . Also, let bm be the systematic risk of an equally-weighted market portfolio. Hence, bm ˆ 1 N XN iˆ1 bi: The absolute value of the deviation (AVD) of security i's expected return in period t from the tth period portfolio expected return can be expressed as E.C. Chang et al. / Journal of Banking & Finance 24 (2000) 1651±1679 1655 AVDi;t ˆ bi j ÿ bmjEt…Rm ÿ c0†: …4† Hence, we can de®ne the expected cross-sectional absolute deviation of stock returns (ECSAD) in period t as follows: ECSADt ˆ 1 N XN iˆ1 AVDi;t ˆ 1 N XN iˆ1 bi j ÿ bmjEt…Rm ÿ c0†: …5† The increasing and linear relation between dispersion and the time-varying market expected returns can be easily shown as follows: oECSADt oEt…Rm† ˆ 1 N XN iˆ1 bi j ÿ bmj > 0; …6† o ECSADt oEt…Rm† ˆ 0: …7† Based on the above results we propose an alternate test of herding which requires an additional regression parameter to capture any possible non-linear relation between security return dispersions and the market return. In fact, our empirical test is similar in spirit to the market timing model proposed by Treynor and Mazuy (1966). We use the CSADt and Rm;t to proxy for the unobservable ECSADt and Et(Rm;t). If market participants are more likely to herd during periods of large price movements, there would be a less than proportional increase (or even decrease) in the CSAD measure. Note that we are using the conditional version of the CAPM merely to establish the presence of a linear relation between ECSADt and Et(Rm;t). We use ex post data to test for the presence of herd behavior in our sample via the average relationship between realized CSADt and Rm;t. CSAD is not a measure of herding, instead the relationship between CSADt and Rm;t is used to detect herd behavior. To allow for the possibility that the degree of herding may be asymmetric in the up-versus the down-market, we run the following empirical speci®cation: CSAD t ˆ a‡ c 1 R m;t ‡ c 2 …RUP m;t † ‡ et; …8† CSAD t ˆ a‡ c 1 R m;t ‡ c 2 …RDOWN m;t † ‡ et; …9† where CSADt is the average AVDt of each stock relative to the return of the equally-weighted market portfolio, Rm;t in period t, and jRUP m;t j…jRDOWN m;t j† is the absolute value of an equally-weighted realized return of all available securities on day t when the market is up (down). Both variables are computed on a daily basis. Note that to facilitate a comparison of the coecients of the linear term, absolute values are used in Eqs. (8) and (9). If during periods of relatively large 1656 E.C. Chang et al. / Journal of Banking & Finance 24 (2000) 1651±1679 price swings, market participants do indeed herd around indicators such as the average consensus of all market constituents, a non-linear relation between CSADt and the average market return would result. The non-linearity would be captured by a negative and statistically signi®cant c2 coecient. 3 For a comparison of the two methods, in Fig. 1, we plot the CSAD measure for each day and the corresponding equally-weighted market return for Hong Kong using stock return data over the period from January 1981 to December 1995. The CSAD-market return relation does indeed appear to be linearly positive. Focusing on the right hand side area where realized average daily returns were all positive, the estimated coecients and the corresponding t-statistics for our model are: CSADt ˆ 0:0143‡ 0:3562 …14:08† R UP m;t ÿ 0:0515 …ÿ0:11† …R UP m;t † ‡ et: The results indicate the presence of a positive and statistically signi®cant linear term. However, since the non-linear term is not signi®cantly negative, CSADt has not increased at a decreasing rate or decreased as the average price movement increases. Hence, the prediction of rational asset pricing models (as suggested by the above analysis) has not been violated. The same conclusion can also be reached using the methodology suggested by CH. Using the one percent criterion, the estimated coecients for their model are: CSADt ˆ 0:0171‡ 0:0254 …5:06† D L t ‡ 0:0239 …5:73† D U t ‡ et: Both estimates of the dummy variable coecients are positive and statistically signi®cant. Thus the CH method also provides no evidence of herd behavior in Hong Kong. However, the two methods may provide con ̄icting results with regard to the presence of herd behavior. For illustration purposes, for all positive Rm;t values, let us consider a general quadratic relationship between CSADt and Rm;t of the following form: CSADt ˆ a‡ c1Rm;t ‡ c2R2m;t; …10† where the presence of a negative c2 parameter is an indication of herd behavior in our model. The quadratic relation suggests that CSADt reaches its maximum value when R m;t ˆ ÿ…c1=2c2†. That is, as Rm;t increases, over the range where realized average daily returns are less (greater) than R m;t, CSADt is trending up (down). Unless some, if not all, of the Rm;t values during periods of 3 An alternative explanation to the herding argument, could be the presence of a non-linear market model. E.C. Chang et al. / Journal of Banking & Finance 24 (2000) 1651±1679 1657