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

Jules van Binsbergen, Xiao Han and Alejandro Lopez-Lira

No 27843, NBER Working Papers from National Bureau of Economic Research, Inc

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. 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.

JEL-codes: D22 D83 D84 G11 G12 G14 G31 G4 (search for similar items in EconPapers)
Date: 2020-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
Note: AP CF
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