Empirical Asset Pricing Via Machine Learning

79 Pages Posted: 26 Dec 2018

See all articles by Shihao Gu

Shihao Gu

University of Chicago - Booth School of Business

Bryan T. Kelly

Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)

Dacheng Xiu

University of Chicago - Booth School of Business

Multiple version iconThere are 2 versions of this paper

Date Written: December 2018

Abstract

We synthesize the field of machine learning with the canonical problem of empirical asset pricing: measuring asset risk premia. In the familiar empirical setting of cross section and time series stock return prediction, we perform a comparative analysis of methods in the machine learning repertoire, including generalized linear models, dimension reduction, boosted regression trees, random forests, and neural networks. At the broadest level, we find that machine learning offers an improved description of expected return behavior relative to traditional forecasting methods. 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. Lastly, we find that all methods agree on the same small set of dominant predictive signals that includes variations on momentum, liquidity, and volatility. Improved risk premium measurement through machine learning can simplify the investigation into economic mechanisms of asset pricing and justifies its growing role in innovative financial technologies.

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Suggested Citation

Gu, Shihao and Kelly, Bryan T. and Xiu, Dacheng, Empirical Asset Pricing Via Machine Learning (December 2018). NBER Working Paper No. w25398. Available at SSRN: https://ssrn.com/abstract=3306110

Shihao Gu (Contact Author)

University of Chicago - Booth School of Business ( email )

Chicago, IL
United States

Bryan T. Kelly

Yale SOM ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

AQR Capital Management, LLC ( email )

Greenwich, CT
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Dacheng Xiu

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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