Implications of Herding Behavior for Earnings Estimation, Risk Assessment, and Stock Returns

Abstract

Experts' earnings predictions exhibit positive bias and disappointing accuracy. These shortcomings are usually attributed to some combination of incomplete knowledge, incompetence, and/or misrepresentation. This article suggests that the human desire for consensus leads to herding behavior among earnings forecasters. Herding results in a reduction in the dispersion and an increase in the mean of the distribution of expert forecasts, creating positive bias and inaccuracy in published earnings estimates. Investors mistake reduced dispersion for reduced risk and positive bias for high future returns. These misperceptions lead to abnormally low returns from stocks with unpredictable earning streams.

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