التفاصيل البيبلوغرافية
| العنوان: |
Man versus Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases. |
| المؤلفون: |
Binsbergen, Jules H van, Han, Xiao, Lopez-Lira, Alejandro |
| المصدر: |
Review of Financial Studies; Jun2023, Vol. 36 Issue 6, p2361-2396, 36p |
| مصطلحات موضوعية: |
CORPORATE profits, EARNINGS forecasting, BUSINESS forecasting, EARNINGS trends, BUSINESS software, MACHINE learning, PERFORMANCE evaluation |
| مستخلص: |
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. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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