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

van Binsbergen, J. H., Han, X. & Lopez-Lira, A. (2022). Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases. The Review of Financial Studies, 36(6), pp. 2361-2396. doi: 10.1093/rfs/hhac085


We introduce a real-time measure of conditional biases in 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 upwards, and the bias 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 provided by analysts.

Publication Type: Article
Additional Information: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords: Earnings Forecasts, Machine Learning, Investment Strategies
Subjects: H Social Sciences > HB Economic Theory
H Social Sciences > HG Finance
Departments: Bayes Business School > Finance
SWORD Depositor:
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