Who Herds? Who Doesn'T?

49 Pages Posted: 21 Nov 2005

See all articles by Murugappa (Murgie) Krishnan

Murugappa (Murgie) Krishnan

Yeshiva University

Steve C. Lim

Texas Christian University - M.J. Neeley School of Business

Ping Zhou

City University of New York - CUNY Baruch College

Date Written: December 2005

Abstract

We build a simple model of analysts' propensity to herd. Using ideas from GMM and simulated method of moments, we estimate an analyst's herding propensity with I/B/E/S forecast data from 1989-2004. We find that, of the analysts whose herding propensity is defined by our model, 85% of them tend to herd while 5% of them tend to stand out from the crowd (i.e., 'anti-herd'). Out-of-sample tests validate our underlying model for analysts' behavior. Further cross-sectional analyses suggest that an analyst tends to herd if she issues less accurate forecasts in the past, has more analysts that issue forecasts before her, a longer forecast horizon, issues forecast less frequently, has less firm-specific experience, more general experience, follows more industries, works for a smaller brokerage house, and follows a firm with less volatile earnings and smaller size.

Keywords: herding, analyst forecasts, earnings

JEL Classification: G14, G29, D84, M41

Suggested Citation

Krishnan, Murugappa (Murgie) and Lim, Steve and Zhou, Ping, Who Herds? Who Doesn'T? (December 2005). Available at SSRN: https://ssrn.com/abstract=850625 or http://dx.doi.org/10.2139/ssrn.850625

Murugappa (Murgie) Krishnan (Contact Author)

Yeshiva University ( email )

500 West 185th Street
New York, NY 10033
United States

Steve Lim

Texas Christian University - M.J. Neeley School of Business ( email )

2900 Lubbock Street
Fort Worth, TX 76129
United States
817-257-7536 (Phone)

Ping Zhou

City University of New York - CUNY Baruch College ( email )

17 Lexington Avenue
New York, NY 10021
United States

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