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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, doi: 10.1093/rfs/hhac085

Abstract

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 (http://creativecommons.org/licenses/by/4.0/), 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
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