Deep Learning in Asset Pricing

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