Factor correlation and the cross section of asset returns: A correlation-robust machine learning approach

Abstract

This paper investigates high-dimensional factor models for cross-sectional asset returns, with a specific focus on robust estimation in the presence of (highly) correlated factors. Factor correlations can significantly compromise the robustness and credibility of commonly employed analytical methods. To address this, we utilize the stochastic discount factor (SDF) and integrate it with a recently developed Machine Learning methodology (Figueiredo and Nowak, 2016). This novel approach allows us to select factors while accounting for factor correlations and to disentangle correlated factors without imposing rigid assumptions. Our empirical findings consistently highlight the paramount role of the ‘market’ factor in driving cross-sectional asset returns. In contrast, other benchmarks, including the LASSO, the Elastic-Net, and the Fama–MacBeth regression, are adversely impacted by factor correlations, rendering the ‘market’ factor redundant. Additionally, our findings underscore the importance of ‘profitability’, ‘momentum’, and ‘liquidity’-related factors in driving cross-sectional asset returns.