Do Machine Learning Models Need to Be Sector Experts?

Abstract

We examine heterogeneous return predictability at the industry level using machine learning models trained on a comprehensive set of firm characteristics. We compare uniform ("Generalist") models with industry-specific ("Specialist") models and introduce a "Hybrid" model that incorporates industry membership. The Hybrid model outperforms the Specialist model in out-of-sample performance, yielding higher Sharpe ratios and lower risk compared to both alternatives. Additional analyses using international data corroborate these findings. Our results indicate that the Hybrid model benefits from a better signal-to-noise ratio by combining industry awareness with broader sample sizes, improving both estimation precision and learning efficiency.

Keywords: Asset pricing, return predictability, machine learning

JEL Classification: G10, G12, G14, G23