Variable selection for minimum-variance portfolios

TL;DR

This paper explores the use of machine learning to select variables for constructing minimum-variance portfolios, demonstrating significant risk reduction and improved performance through non-linear predictor transformations.

Contribution

It introduces a large predictor set with non-linear terms for portfolio optimization, showing ML's effectiveness in variable selection for risk minimization.

Findings

ML-based variable selection reduces portfolio risk substantially

Non-linear predictor transformations improve portfolio performance

Selected predictors can enhance returns and risk-adjusted measures

Abstract

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.