Machine Learning for Out-of-Sample Prediction of Industry Portfolio Returns Within Multi-Factor Asset Pricing Models

Accurately predicting asset returns remains a central challenge in finance, with significant implications for portfolio optimization and risk management. In response to the challenge, this study evaluates the predictive performance of machine learning algorithms in estimating excess returns of U.S. industry portfolios, within the out-of-sample prediction framework of the Fama–French three-, four-, five- and six-factor asset pricing models. In the analysis, Support Vector Regression, Multilayer Perceptron, Linear Regression, and k-Nearest Neighbor were employed using monthly return data from 1992 to 2022, covering 5-, 10-, 12-, 17-, 30-, 38-, 48-, and 49-portfolio configurations composed of NYSE, AMEX, and NASDAQ-listed firms. The findings reveal that support vector regression achieved the highest number of top-ranked results, producing the most successful outcomes in 305 out of 836 model–portfolio combinations. However, multilayer perceptron achieved the best fit in the largest number of portfolios, ranking first in all groups except the 5-industry configuration. Furthermore, the Fama–French five-factor model outperformed other specifications across all groupings, confirming the value of incorporating profitability and investment information. Predictive performance also varied by industry, as wholesale and manufacturing sectors exhibited strong alignment, whereas utilities and energy-related sectors, likely constrained by structural or regulatory features, remained less responsive and exposed to long-term risks.