Empirical Asset Pricing via Machine Learning

• 成果类型: Article
• 署名作者: Gu, Shihao; Kelly, Bryan; Xiu, Dacheng
• 署名单位: University of Chicago; Yale University; National Bureau of Economic Research
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhaa009
• 发表日期: 2020
• 页码: 2223
• 关键词: cross-section; least-squares; regression; returns; RISK; predictability; algorithm; networks; tests

摘要

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.