Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases
94 Pages Posted: 7 Jul 2020 Last revised: 18 Nov 2021
Date Written: July 14, 2021
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.
Keywords: Earnings Forecasts, Machine Learning, Investment Strategies
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