Empirical Asset Pricing via Machine Learning

[16:04 6/4/2020 ]Page: 2223 2223 2274 Empirical Asset Pricing via MachineLearning Shihao GuBooth School of Business, University of ChicagoBryan KellyYale University, AQR Capital Management, and NBERD acheng XiuBooth School of Business, University of ChicagoWe perform a comparative analysis of Machine Learning methods for the canonical problemof Empirical Asset Pricing : measuring Asset risk premiums. We demonstrate large economicgains to investors using Machine Learning forecasts, in some cases doubling the performanceof leading regression-based strategies from the literature. We identify the best-performingmethods (trees and neural networks) and trace their predictive gains to allowing nonlinearpredictor interactions missed by other methods. All methods agree on the same set ofdominant predictive signals, a set that includes variations on momentum, liquidity, andvolatility.

benchmarks for the predictive accuracy of machine learning methods in measuring risk premiums of the aggregate market and individual stocks. This accuracy is summarized two ways. The first is a high out-of-sample predictive R2 relative to preceding literature that is robust across a variety of machine learning specifications.

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  Machine, Market, Learning, Machine learning

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