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1、Herding in trading by amateur and professional investors byItzhak Venezia*, Amrut Nashikkar* and Zur Shapira*May 14, 2008Rough draft: *The Hebrew University, Jerusalem and Rutgers Business School, Newark and New Brunswick *Stern School of Business, New York University. We acknowledge the financial s
2、upport of The Sanger Family Chair for Banking and Risk Management, The Galanter Fund, The Mordecai Zagagi Fund, the Whitcomb Center for Research in Financial Services, and The School of Accounting, The Hebrew University. The helpful comments of participants at the European Financial Management, (EFM
3、), Symposium on Behavioral Finance, 2006, and finance seminar participants at Rutgers University and Bocconi University are gratefully acknowledged. Addresses: Itzhak Venezia, School of Business, Hebrew University, E-mail: msvenezmscc.huji.ac.il. Zur Shapira, Stern School of Business, New York Unive
4、rsity, New York, NY 10012, E-mail: zshapirastern.nyu.edu, Amrut Nashikkar. anashikkstern.nyu.edu.AbstractWe study herding behavior of amateur and professional investors using a unique data-set consisting of all their daily transactions during a four year period, and explore the factors that can expl
5、ain such behavior. We distinguish between features specific to the stocks, such as their systematic risk, idiosyncratic risk, and size, on the one hand, and market factors such as total stock market volume and returns on the other hand. We find herding tendencies among both types of investors and th
6、at this tendency is higher for amateurs. It is shown that herding is affected by both types of factors: it is a decreasing function of the size of the firm, and an increasing function of its risk. Idiosyncratic risk tends to positively affect herding but this effect is significantly lower for profes
7、sionals. Systematic risk however influences positively only the herding of professionals. Our data also reveal that herding behavior of the two groups is a persistent phenomenon, and that it is closely related to the volatility of market returns. Amateurs herding is weakly related to market returns.
8、 In general most of the results are consistent with the theory that herding is information based. Herding in trading by amateur and professional investorsI. Introduction Herding behavior by investors has intrigued researchers for a long time because of its potential effects on fluctuations in return
9、s For examples showing how herding and other demand fluctuations affect prices see, e.g., Eichengreen et.al. ,1998a,b, Furman and Stiglitz, 1998, Nofsinger and Sias, 1999, Froot, OConnell, and Seasholes, 2001, Choe, Kho, and Stulz, 1997, Edelen and Warner, 2001, Goetzmann and Massa, 2002, Shapira an
10、d Venezia, 2006, and Sias and Starks, 1997. For a comprehensive review of Herding literature see Bickchandani and Sharma, 2000, and Hirshleifer and Teoh, 2001. In many cases researchers argue that herding is a by product of information availability or the lack of it. Different groups of investors re
11、ceive various types of information and of diverse quality. Differences between the group characteristics and different information available to their members may cause such groups of investors to behave differently from each other and yet to exhibit herding within each group. Understanding the bases
12、 of herding behavior of each of such groups may lead to better understanding of price variations since diverse groups trading may correlate differently with the market. The following examples, although not directly involving herding, but closely related to it, highlight the need to better understand
13、 the differences between individuals and institutions trading behavior and their influences on the market. Ofek and Richardson, 2003, suggest that increased activity of individuals could have contributed to the bubble of the 1990s. On the other hand, Griffin, Harris and Topaloglu, 2003a and 2003b ar
14、gue, using daily data from NASDAQ, that institutions were the main driving force during the crashes of the 1990s. In recent papers Kaniel, Saar, and Titman, 2007, and Kaniel, Liu, Saar, and Titman, 2007, show that trading buy imbalances by individuals provide market returns predictive ability. While
15、 investors can be classified into groups in various ways for analyzing their behavior, such classification and analysis will be useful only if the groups are sufficiently large for their actions to potentially affect key market features. Groups that are too large are counterproductive for the study
16、of herds. If the groups cover the whole market, then the sell of one group would be the buy of the other, hence the behavior of the groups would mirror each other. Groups should be somewhat homogeneous and the members of the groups should have some possibility of guessing or observing the actions of other members. Whereas we are considering the behavior of amateurs vs. professiona
