Asset allocation with factor-based covariance matrices

Thomas Conlon, John Cotter and Iason Kynigakis

European Journal of Operational Research, 2025, vol. 325, issue 1, 189-203

Abstract: We examine whether a factor-based framework to construct the covariance matrix can enhance the performance of minimum-variance portfolios. We conduct a comprehensive comparative analysis of a wide range of factor models, which can differ based on the machine learning dimensionality reduction approach used to construct the latent factors and whether the covariance matrix is static or dynamic. The results indicate that factor models exhibit superior predictive accuracy compared to several covariance benchmarks, which can be attributed to the reduced degree of over predictions. Factor-based portfolios generate statistically significant outperformance with respect to standard deviation and Sharpe ratio relative to multiple portfolio benchmarks. After accounting for transaction costs strategies based on static covariance matrices outperform dynamic specifications in terms of risk-adjusted returns.

Keywords: Covariance matrix; Dimensionality reduction; Factor models; Machine learning; Minimum-variance portfolio (search for similar items in EconPapers)
JEL-codes: C38 C58 G11 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:325:y:2025:i:1:p:189-203

DOI: 10.1016/j.ejor.2025.03.015

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