Machine learning and return predictability across firms, time and portfolios

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See all articles by Fahiz Baba Yara

Fahiz Baba Yara

New University of Lisbon - Nova School of Business and Economics

Date Written: March 25, 2020

Abstract

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 predictions from the models fail to generalize in a number of important ways, such as predicting time-series variation in market and long-short characteristic sorted portfolio returns across multiple horizons. I show this shortfall can be remedied by imposing economic restrictions in the architectural design of a neural network model and provide recommendations for using machine learning methods in asset pricing. Additionally, I shed light on the intermediate and long-run dynamics of the return forecasts generated by these models.

Keywords: Return Predictability, Long-run Returns, Machine Learning, Neural Networks

JEL Classification: E44, G10, G11, G12, G17

Suggested Citation

Baba Yara, Fahiz, Machine learning and return predictability across firms, time and portfolios (March 25, 2020). Available at SSRN: https://ssrn.com/abstract=

Fahiz Baba Yara (Contact Author)

New University of Lisbon - Nova School of Business and Economics ( email )

Campus de Campolide
Lisbon, 1099-032
Portugal

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