Heterogeneous Predictability on Mutual Fund Alphas: A Sparse Clustering GMM Approach

Mutual fund managers’ skills, measured by risk-adjusted alphas, are predictable using fund characteristics identified by machine learning (e.g., Kaniel et al., 2023, JFE; DeMiguel et al., 2023, JFE). However, alpha’s predictive power varies across funds and periods, with most funds exhibiting negligible alphas. To model this heterogeneous predictability, this paper introduces Sparse Clustering GMM, a nonparametric approach for identifying latent fund groups. SCGMM clusters funds using estimated alphas and identifies group-specific parameters tied to market predictors. The method accounts for heterogeneity in cross-sectional grouping and time-varying mechanisms across predictors, without requiring prior cluster information or time-varying specifications. We establish consistency for group recovery and time-varying parameter estimation. Empirically, using monthly U.S. mutual fund data, we find that predictable alpha is concentrated in a small subset of funds, with the majority of funds exhibiting near-zero, weakly predictable alphas. The skilled clusters also exhibit lower benchmark R2, moderate expense ratios, and longer manager tenure.