Deep Learning in Characteristics-Sorted Factor Models

40 Pages Posted: 23 Sep 2018 Last revised: 22 Feb 2021

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 1, 2021

Abstract

To study the characteristics-sorted factor model in empirical asset pricing, we design a non-reduced-form feedforward neural network with the non-arbitrage objective to minimize pricing errors. Our model starts from firm characteristics [inputs], generates risk factors [intermediate features], and fits the cross-sectional returns [outputs]. A nonlinear activation in deep learning approximates the traditional security sorting on characteristics to create long-short portfolio weights, like a hidden layer, from lag characteristics to realized returns. Our model offers an alternative approach for dimension reduction in empirical asset pricing on characteristics [inputs], rather than factors [intermediate features], and allows for both nonlinearity and interactions directly through [inputs]. Our empirical findings are threefold. First, we find substantial and robust asset pricing improvements of multiple performance measures, such as Cross-Sectional R^2, in both in-sample and out-of-sample analysis. Second, the deep learning augmented models produce all positive improvements regarding return prediction over the benchmark factor models. Finally, we show significant increases in factor investing, nonlinear relationships in deep characteristics, and their importance on raw characteristics.

Keywords: Alpha, Characteristics-Sorted Factor Models, Cross-Sectional Returns, Deep Learning, Firm Characteristics, Non-Arbitrage, Pricing Errors.

JEL Classification: C1, G1

Suggested Citation

Feng, Guanhao and Polson, Nick and Xu, Jianeng, Deep Learning in Characteristics-Sorted Factor Models (February 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 (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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