Investor learning about analyst predictive ability

• 成果类型: Article
• 署名作者: Chen, Q; Francis, J; Jiang, W
• 署名单位: Duke University; Columbia University
• 刊物名称: JOURNAL OF ACCOUNTING & ECONOMICS
• ISSN/ISSBN: 0165-4101
• DOI: 10.1016/j.jacceco.2004.01.002
• 发表日期: 2005
• 关键词: EXCESS VOLATILITY; FORECAST ACCURACY; career concerns; earnings; predictability; performance

摘要

Bayesian learning implies decreasing weights on prior beliefs and increasing weights on the accuracy of the analyst's past forecast record, as the number of forecast errors comprising her forecast record (its length) increases. Consistent with this model of investor learning, empirical tests show that investors' reactions to forecast news are increasing in the product of the accuracy and length of analysts' forecast records. Moreover, the Bayesian learning predicted by our model is more descriptive of investor reactions than is a static model which predicts that investors' responses condition only on the prior accuracy of the analyst. (c) 2004 Elsevier B.V. All rights reserved.