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Man versus Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases
The Review of Financial Studies ( IF 8.414 ) Pub Date : 2022-10-31 , DOI: 10.1093/rfs/hhac085
Jules H van Binsbergen, Xiao Han, Alejandro Lopez-Lira

We introduce a real-time measure of conditional biases to 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 upward, a bias that 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 analysts provide.

中文翻译:

人与机器学习:收益预期和条件偏差的期限结构

我们引入了对公司盈利预测的条件偏差的实时测量。该度量被定义为分析师的预期与统计上最优的无偏机器学习基准之间的差异。分析师的条件预期平均而言是向上偏向的,这种偏向在预测范围内会增加。这些偏差与负的横截面回报可预测性相关,许多异常现象的短腿包含盈利预测过于乐观的公司。此外,盈利预测偏高的公司的经理更有可能发行股票。常用的线性收益模型在样本外不起作用,并且不如分析师提供的那些模型。
更新日期:2022-10-31
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