Predicting Returns with Machine Learning Across Horizons, Firms Size, and Time

Abstract

Researchers and practitioners hope that machine learning strategies will deliver better performance than traditional methods. But do they? This study documents that stock return predictability with machine learning depends critically on three dimensions: forecast horizon, firm size, and time. It works well for short-term returns, small firms, and early historical data; however, it disappoints in opposite cases. Consequently, annual return forecasts have failed to produce substantial economic gains within most of the U.S. market in the last two decades. These findings challenge the practical utility of predicting returns with machine learning models.

Keywords: machine learning, return predictability, the cross-section of stock returns, asset pricing, firm size, equity anomalies, long-short portfolios, long-run returns

JEL Classification: C52, G10, G12, G15