Empirical Asset Pricing via Machine Learning
Shihao Gu
Booth School of Business,
University of Chicago
Bryan Kelly
Yale University, AQR Capital
Management, and NBER
Dacheng Xiu
Booth School of Business,
University of Chicago
January 22, 2020
Abstract
We perform a comparative analysis of machine learning methods for the canonical problem
of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains
to investors using machine learning forecasts, in some cases doubling the performance of leading
regression-based strategies from the literature. We identify the best-performing methods (trees
and neural networks) and trace their predictive gains to allowing nonlinear predictor interactions
missed by other methods. All methods agree on the same set of dominant predictive signals, a
set that includes variations on momentum, liquidity, and volatility.