Working Paper: NBER ID: w25398
Authors: Shihao Gu; Bryan Kelly; Dacheng Xiu
Abstract: We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. 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 allowance of nonlinear predictor interactions that are missed by other methods. All methods agree on the same set of dominant predictive signals which includes variations on momentum, liquidity, and volatility. Improved risk premium measurement through machine learning simplifies the investigation into economic mechanisms of asset pricing and highlights the value of machine learning in financial innovation.
Keywords: machine learning; asset pricing; risk premia; predictive accuracy
JEL Codes: C45; C55; C58; G11; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Machine learning methods (C45) | Predictive accuracy in measuring equity risk premia (C52) |
| Machine learning forecasts (C53) | Investment performance (G11) |
| Inclusion of nonlinear interactions among predictors (C32) | Improved return predictions (G17) |
| Predictor variables (price trends, liquidity, volatility) (G17) | Expected returns (G17) |