Who Herds? Who Doesn't? Estimates of Analysts' Herding Propensity in Forecasting Earnings

• 成果类型: Article
• 署名作者: Huang, Rong; Krishnan, Murugappa (Murgie); Shon, John; Zhou, Ping
• 署名单位: City University of New York (CUNY) System; Baruch College (CUNY); William Paterson University New Jersey; Rutgers University System; Rutgers University New Brunswick; Fordham University
• 刊物名称: CONTEMPORARY ACCOUNTING RESEARCH
• ISSN/ISSBN: 0823-9150
• DOI: 10.1111/1911-3846.12236
• 发表日期: 2017
• 页码: 374-399
• 关键词: security analysts; recommendations; BEHAVIOR; INFORMATION; revisions; management; returns

摘要

We develop parametric estimates of the imitation-driven herding propensity of analysts and their earnings forecasts. By invoking rational expectations, we solve an explicit analyst optimization problem and estimate herding propensity using two measures: First, we estimate analysts' posterior beliefs using actual earnings plus a realization drawn from a mean-zero normal distribution. Second, we estimate herding propensity without seeding a random error, and allow for nonorthogonal information signals. In doing so, we avoid using the analyst's prior forecast as the proxy for his posterior beliefs, which is a traditional criticism in the literature. We find that more than 60 percent of analysts herd toward the prevailing consensus, and herding propensity is associated with various economic factors. We also validate our herding propensity measure by confirming its predictive power in explaining the cross-sectional variation in analysts' out-of-sample herding behavior and forecast accuracy. Finally, we find that forecasts adjusted for analysts' herding propensity are less biased than the raw forecasts. This adjustment formula can help researchers and investors obtain better proxies for analysts' unbiased earnings forecasts.