Man versus Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases

• 成果类型: Article
• 署名作者: van Binsbergen, Jules H.; Han, Xiao; Lopez-Lira, Alejandro
• 署名单位: National Bureau of Economic Research; University of Pennsylvania; Center for Economic & Policy Research (CEPR); University of London; State University System of Florida; University of Florida
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhac085
• 发表日期: 2023
• 页码: 2361
• 关键词: cross-section; INFORMATION; forecasts; heteroskedasticity; profitability; INVESTMENT; anomalies; ISSUANCE; analysts; returns

摘要

We introduce a real-time measure of conditional biases to firms' earnings forecasts. The measure is defined as the difference between analysts' expectations and a statistically optimal unbiased machine-learning benchmark. Analysts' conditional expectations are, on average, biased upward, a bias that increases in the forecast horizon. These biases are associated with negative cross-sectional return predictability, and the short legs of many anomalies contain firms with excessively optimistic earnings forecasts. Further, managers of companies with the greatest upward-biased earnings forecasts are more likely to issue stocks. Commonly used linear earnings models do not work out-of-sample and are inferior to those analysts provide.