Document Type

Article

Publication Date

2026

DOI

10.3390/jrfm19040274

Publication Title

Journal of Risk and Financial Management

Volume

19

Issue

4

Pages

274

Abstract

This study examines the predictability of monthly excess returns for the “Magnificent Seven” U.S. technology firms using machine learning and economically motivated thematic feature grouping. Framed as a focused study of the most systemically consequential equity panel in modern markets—seven firms representing over 30% of the S&P 500—the analysis confronts a small-N, large-P environment where economically structured dimensionality reduction is essential. Using 154 firm-level characteristics categorized into 13 economic themes, we evaluate linear, penalized, tree-based, and neural network models in a small-N, large-P setting. Unrestricted models suffer substantial overfitting and fail to outperform the historical average benchmark out-of-sample. In contrast, theme-based models generate economically meaningful and regime-dependent predictive gains. Short-Term Reversal and seasonality exhibit stronger expansion-period predictability, while size and profitability perform better during recessions. Regularized linear models provide the most stable performance in limited-data environments, whereas nonlinear ensemble methods improve only when training windows are extended. The findings underscore the importance of economically structured dimensionality reduction and adaptive factor allocation in managing concentration risk among systemically important mega-cap firms.

Rights

© 2026 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Comments

Data availability statement: "The firm-level characteristics data used in this study are available through the Wharton Research Data Services (WRDS) platform, based on the dataset of Jensen et al. (2023). NBER recession dates are publicly available."

ORCID

0009-0009-2329-9117 (Jalali), 0000-0003-4889-3007 (Najand), 0000-0003-3112-4295 (Cohen)

Original Publication Citation

Jalali, M., Najand, M., & Cohen, A. (2026). Machine learning, thematic feature grouping, and the magnificent seven: A forecasting analysis. Journal of Risk and Financial Management, 19(4), Article 274. https://doi.org/10.3390/jrfm19040274

Download

Share

COinS