Machine Learning and Return Predictability Across Firms, Time and Portfolios
73 Pages Posted: 2 Dec 2020 Last revised: 7 Jan 2021
Date Written: March 25, 2020
Previous research finds that machine learning methods predict short-term return variation in the cross-section of stocks, even when these methods do not impose strict economic restrictions. However, without such restrictions, the models' predictions fail to generalize in a number of important ways, such as predicting time-series variation in returns to the market portfolio and long-short characteristic sorted portfolios. I show that this shortfall can be remedied by imposing restrictions, that reflect findings in the financial economics literature, in the architectural design of a neural network model and provide recommendations for using machine learning methods in asset pricing. Additionally, I study return predictability over multiple future horizons, thus shedding light on the dynamics of intermediate and long-run conditional expected returns.
Keywords: Return Predictability, Long-run Returns, Machine Learning, Neural Networks, Long-short portfolios
JEL Classification: E44, G10, G11, G12, G17
Suggested Citation: Suggested Citation