Empirical Asset Pricing via Machine Learning

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ID: 291771
2020
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Abstract
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. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
Reference Key
openalex_W4205539948 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shihao Gu, Bryan Kelly, Dacheng Xiu
Journal review of financial studies
Year 2020
DOI
10.1093/rfs/hhaa009
URL
https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf
Keywords Keywords not found

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