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

Haithem Awijen, Younes Ben Zaied, Béchir Ben Lahouel and Foued Khlifi

Research in International Business and Finance, 2023, vol. 64, issue C

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.

Keywords: Predictive regression; OLS post-LASSO; Post-selection inference; Industry-rotation portfolio (search for similar items in EconPapers)
JEL-codes: C22 C58 G11 G12 G14 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:64:y:2023:i:c:s0275531923000193

DOI: 10.1016/j.ribaf.2023.101893

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