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Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases / Jules H. van Binsbergen, Xiao Han, Alejandro Lopez-Lira.
- Format:
- Book
- Author/Creator:
- van Binsbergen, Jules H.
- Series:
- Working Paper Series (National Bureau of Economic Research) no. w27843.
- NBER working paper series no. w27843
- Language:
- English
- Physical Description:
- 1 online resource: illustrations (black and white);
- Place of Publication:
- Cambridge, Mass. National Bureau of Economic Research 2020.
- Summary:
- 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.
- Notes:
- Print version record
- September 2020.
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