Deep Learning in Characteristics-Sorted Factor Models

50 Pages Posted: 23 Sep 2018 Last revised: 7 Nov 2022

See all articles by Guanhao Feng

Guanhao Feng

City University of Hong Kong (CityU)

Jingyu He

City University of Hong Kong (CityU)

Nick Polson

University of Chicago - Booth School of Business

Jianeng Xu

University of Chicago, Students

Date Written: October 1, 2021

Abstract

Many view deep learning as a "black box" used only for forecasting. However, this paper provides an alternative application by constructing a structural deep neural network to generate latent factors in asset pricing. The conventional approach of sorting firm characteristics to generate long-short factor portfolio weights is underappreciated nonlinear modeling. First, we describe the complete mechanism for fitting cross-sectional returns by firm characteristics through risk factors. Second, unlike statistical models, our model has an economic-guided objective function that minimizes pricing errors. Empirically, we find asset pricing and investment improvements using individual stocks and test portfolios for in-sample and out-of-sample analysis.

Keywords: Cross-sectional Returns, Deep Learning, Latent Factors, Pricing Errors, Security Sorting.

JEL Classification: C1, G1

Suggested Citation

Feng, Guanhao and He, Jingyu and Polson, Nick and Xu, Jianeng, Deep Learning in Characteristics-Sorted Factor Models (October 1, 2021). 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 (CityU) ( email )

83 Tat Chee Avenue
Kowloon Tong
Hong Kong

Jingyu He

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Hong Kong
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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