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An International Meeting of American Accounting
Association
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Who Herds? Who Doesn't? |
Murugappa (Murgie) Krishnan
Yeshiva University
Steve C. Lim
Texas Christian University
Ping Zhou
CUNY - Baruch College
Abstract: We build a simple model in which analysts choose forecasts that trade off between forecast accuracy and distance from the prevailing consensus. With GMM and simulated method of moments ideas, we estimate an analyst’s herding propensity, using 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.