Behavioral Machine Learning? Computer Predictions of Corporate Earnings also Overreact

Murray Z. Frank, Jing Gao and Keer Yang

Papers from arXiv.org

Abstract: Machine learning algorithms are known to outperform human analysts in predicting corporate earnings, leading to their rapid adoption. However, we show that leading methods (XGBoost, neural nets, ChatGPT) systematically overreact to news. The overreaction is primarily due to biases in the training data and we show that it cannot be eliminated without compromising accuracy. Analysts with machine learning training overreact much less than do traditional analysts. We provide a model showing that there is a tradeoff between predictive power and rational behavior. Our findings suggest that AI tools reduce but do not eliminate behavioral biases in financial markets.

Date: 2023-03, Revised 2025-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
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