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

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 crosssectional 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.

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