Deep Learning in Asset Pricing

33 Pages Posted: 23 Sep 2018 Last revised: 9 Feb 2019

See all articles by Guanhao Feng

Guanhao Feng

City University of Hong Kong (CityUHK)

Nick Polson

University of Chicago - Booth School of Business

Jianeng Xu

University of Chicago, Students

Date Written: February 7, 2019

Abstract

Deep learning provides a framework for characteristics-based factor modeling in empirical asset pricing. We provide a systematic approach for long-short factor generation with a goal to minimize pricing errors in the cross section. Security sorting on firm characteristics provides a nonlinear activation function as part of a deep learning model. Our deep factors are tradable and allow for both nonlinearity and interactions between predictors. For cross-sectional return prediction, we study monthly U.S. equity returns based on lag firm characteristics and macro predictors from 1975 to 2017 with a universe of 3,000 stocks. Finally, with additional deep factors, we find the out-of-sample forecast improvements for anomaly-sorted and industry portfolios.

Keywords: Alpha, Cross-Sectional Returns, Deep Learning, Firm Characteristics, Machine Learning, Risk Factors, Security Sorting.

JEL Classification: C1, G1

Suggested Citation

Feng, Guanhao and Polson, Nick and Xu, Jianeng, Deep Learning in Asset Pricing (February 7, 2019). Available at SSRN: https://ssrn.com/abstract=3243683 or http://dx.doi.org/10.2139/ssrn.3243683

Guanhao Feng (Contact Author)

City University of Hong Kong (CityUHK) ( email )

83 Tat Chee Avenue
Kowloon Tong
Hong Kong

Nick Polson

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
773-702-7513 (Phone)
773-702-0458 (Fax)

Jianeng Xu

University of Chicago, Students ( email )

Chicago, IL
United States

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