Empirical Asset Pricing via Machine Learning
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.
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Copy CitationShihao Gu, Bryan Kelly, and Dacheng Xiu, "Empirical Asset Pricing via Machine Learning," NBER Working Paper 25398 (2018), https://doi.org/10.3386/w25398.Download Citation
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Published Versions
Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, vol 33(5), pages 2223-2273. citation courtesy of 
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