The Predictive Content of Aggregate Analyst Recommendations

• 成果类型: Article
• 署名作者: Howe, John S.; Unlu, Emre; Yan, Xuemin (Sterling)
• 署名单位: University of Missouri System; University of Missouri Columbia; University of Nebraska System; University of Nebraska Lincoln
• 刊物名称: JOURNAL OF ACCOUNTING RESEARCH
• ISSN/ISSBN: 0021-8456
• DOI: 10.1111/j.1475-679X.2009.00337.x
• 发表日期: 2009
• 页码: 799-821
• 关键词: stock return predictability; information-content; variance decomposition; EARNINGS FORECASTS; Expected returns; dividend yields; equity; performance; investors; industry

摘要

Using more than 350,000 sell-side analyst recommendations from January 1994 to August 2006, this paper examines the predictive content of aggregate analyst recommendations. We find that changes in aggregate analyst recommendations forecast future market excess returns after controlling for macroeconomic variables that have been shown to influence market returns. Similarly, changes in industry-aggregated analyst recommendations predict future industry returns. Changes in aggregate analyst recommendations also predict one-quarter-ahead aggregate earnings growth. Overall, our results suggest that analyst recommendations contain market- and industry-level information about future returns and earnings.