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Deep Learning in Characteristics-Sorted Factor Models

Published online by Cambridge University Press:  24 July 2023

Guanhao Feng Open the ORCID record for Guanhao Feng [Opens in a new window] ,
Jingyu He ,
Nicholas G. Polson  and
Jianeng Xu
Guanhao Feng
Affiliation:
Department of Management Sciences, City University of Hong Kong [email protected]
Jingyu He
Affiliation:
Department of Management Sciences, City University of Hong Kong [email protected]
Nicholas G. Polson
Affiliation:
Booth School of Business, University of Chicago [email protected]
Jianeng Xu*
Affiliation:
Booth School of Business, University of Chicago
*
[email protected] (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

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This article presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing. The conventional security sorting on firm characteristics for constructing long–short factor portfolio weights is nonlinear modeling, while factors are treated as inputs in linear models. We provide a structural deep-learning framework to generalize the complete mechanism for fitting cross-sectional returns by firm characteristics through generating risk factors (hidden layers). Our model has an economic-guided objective function that minimizes aggregated realized pricing errors. Empirical results on high-dimensional characteristics demonstrate robust asset pricing performance and strong investment improvements by identifying important raw characteristic sources.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , First View , pp. 1 - 36
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We appreciate insightful comments from Will Cong, Serge Darolles, Victor DeMiguel, Li Deng, Jin-Chuan Duan, Thierry Foucault (the editor), Shihao Gu (discussant), Bryan Kelly, Soohun Kim (discussant), Markus Pelger (the referee), Weidong Tian (discussant), Dacheng Xiu, and Chu Zhang. We are also grateful to helpful comments from the seminar and conference participants at Boston University, CUHK, CityU HK, Jinan University, SHUFE, SUSTech, ESSEC Business School, 2019 China International Conference in Finance, Bloomberg, 2019 CQAsia Conference, 2019 EcoSta Conference, 2019 Informs Annual Conference, PanAgora Asset Management, 2019 SoFiE Annual Conference, Schroders, 2019 Wolfe Research Conference, 2019 Unigestion Factor Investing Conference, 2019 Autumn Seminar of Inquire Europe, 2018 Australian Finance & Banking Conference, and 2018 New Zealand Finance Meeting. We acknowledge Unigestion Alternative Risk Premia Research Academy, and INQUIRE Europe research awards. Feng’s research is partly supported by HK RGC grants (ECS-21506318 and GRF-11502721) and an NSFC grant (NSFC-72203190). He’s research is partly supported by HK RGC grants (ECS-21504921 and GRF-11507022). Feng and He are partly supported by the InnoHK initiative and the Laboratory for AI-Powered Financial Technologies.

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