Deep Learning Factor Alpha

34 Pages Posted: 23 Sep 2018 Last revised: 1 Oct 2018

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: September 1, 2018

Abstract

Does a factor model exist to absorb all existing anomalies? We provide a deep learning automated solution to generate long-short factors using a high-dimensional firm characteristic. Sorting securities on firm characteristics is a common practice in finance and a nonlinear activation function built into deep learning. Our algorithm performs a nonlinear search and finds the optimal transformation of characteristics used for security sorting, with one asset pricing objective: minimizing alphas. Our deep factors, hidden neurons in the neural network, are trained greedily with the backward propagation feedback from the loss function that considers both time series and cross-sectional variations. Our conditional forecast generalizes a benchmark, such as CAPM, and includes Fama-French type models as special cases. We have designed a train-validation-test study for monthly U.S. equity returns from 1975 to 2017 and 57 published firm characteristics. In an out-of-sample evaluation, the conditional deep factor model shows a forecasting improvement over the benchmark with factors that offer significant alphas. The conclusion is the improvement of insignificant alphas for some anomalies as well as sorted portfolios.

Keywords: Characteristic-based Anomalies, Cross-Sectional Returns, Deep Learning, Long- Short Factors, Security Sorting, Mispricing Alpha, Neural Network

Suggested Citation

Feng, Guanhao and Polson, Nick and Xu, Jianeng, Deep Learning Factor Alpha (September 1, 2018). 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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