Machine learning for US cross-industry return predictability under information uncertainty

Abstract

This paper investigates the association between industry information uncertainty and cross-industry return predictability using machine learning in a general predictive regression framework. We show that controlling for post-selection inference and performing multiple tests improves the in-sample predictive performance of cross-industry return predictability in industries characterized by high uncertainty. Ordinary least squares post-least absolute shrinkage and selection operator models incorporating lagged industry information uncertainty for the financial and commodity industries are critical to improving prediction performance. Furthermore, in-sample industry return forecasts establish heterogeneous predictability over US industries, in which excess returns are more predictable in sectors with medium or low uncertainty.