Transcription of Empirical Asset Pricing via Machine Learning
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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.
[16:04 6/4/2020 RFS-OP-REVF200009.tex] Page: 2225 2223–2274 Empirical Asset Pricing via Machine Learning field of asset pricing is to apply and compare the performance of each of its
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