2025/08/20 by Guilherme V. Moura, André Pereira dos Santos, Moura, Guilherme V. +4 · 2 voices
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Machine Learning (stat.ML) #Portfolio Management (q-fin.PM) #Stock Market Forecasting Methods #q-fin.PM #stat.ML
paper · pdf · doi:10.48550/arxiv.2508.14986
openalex publication_date 2025/08/20 · arxiv published 2025/08/20 · arxiv updated 2025/08/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Machine learning (ML) methods have been successfully employed in identifying variables that can predict the equity premium of individual stocks. In this paper, we investigate if ML can also be helpful in selecting variables relevant for optimal portfolio choice. To address this question, we parameterize minimum-variance portfolio weights as a function of a large pool of firm-level characteristics as well as their second-order and cross-product transformations, yielding a total of 4,610 predictors. We find that the gains from employing ML to select relevant predictors are substantial: minimum-variance portfolios achieve lower risk relative to sparse specifications commonly considered in the literature, especially when non-linear terms are added to the predictor space. Moreover, some of the selected predictors that help decreasing portfolio risk also increase returns, leading to minimum-variance portfolios with good performance in terms of Shape ratios in some situations. Our evidence suggests that ad-hoc sparsity can be detrimental to the performance of minimum-variance characteristics-based portfolios.