Robust Mutual Fund Selection with False Discovery Rate Control

TL;DR

This paper introduces robust multiple testing procedures for mutual fund selection that effectively control false discovery rates, even in the presence of latent factors and varying error distributions, demonstrating superior performance through simulations and real data.

Contribution

It proposes novel spatial-sign based multiple testing methods, including a factor-adjusted approach, for reliable mutual fund selection under complex error structures.

Findings

FSS-BH outperforms existing methods in simulations.

The procedures are robust to covariance and distribution variations.

Real data application confirms the methods' effectiveness.

Abstract

In this article, we address the challenge of identifying skilled mutual funds among a large pool of candidates, utilizing the linear factor pricing model. Assuming observable factors with a weak correlation structure for the idiosyncratic error, we propose a spatial-sign based multiple testing procedure (SS-BH). When latent factors are present, we first extract them using the elliptical principle component method (He et al. 2022) and then propose a factor-adjusted spatial-sign based multiple testing procedure (FSS-BH). Simulation studies demonstrate that our proposed FSS-BH procedure performs exceptionally well across various applications and exhibits robustness to variations in the covariance structure and the distribution of the error term. Additionally, real data application further highlights the superiority of the FSS-BH procedure.