Empirical Asset Pricing via Machine Learning

67 Pages Posted: 9 Apr 2018 Last revised: 29 Jul 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 3 versions of this paper

Date Written: June 11, 2018


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 generalize 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 asset price behavior relative to traditional methods. Our implementation establishes a new standard for accuracy in measuring risk premia summarized by unprecedented high out-of-sample return prediction R2. We identify the best performing methods (trees and neural nets) 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 premia measurement through machine learning can simplify the investigation into economic mechanisms of asset pricing and justifies its growing role in innovative financial technologies.

Keywords: Machine Learning, Return Prediction, Cross-Section of Returns, Ridge Regression, (Group) Lasso, Elastic Net, Random Forest, Gradient Boosting, (Deep) Neural Networks, Fintech

JEL Classification: G10, G11, G14, C14, C11, C21, C22, C23, C58

Suggested Citation

Gu, Shihao and Kelly, Bryan T. and Xiu, Dacheng, Empirical Asset Pricing via Machine Learning (June 11, 2018). Chicago Booth Research Paper No. 18-04; 31st Australasian Finance and Banking Conference 2018. Available at SSRN: https://ssrn.com/abstract=3159577 or http://dx.doi.org/10.2139/ssrn.3159577

Shihao Gu

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 (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

Register to save articles to
your library


Paper statistics

Abstract Views
PlumX Metrics