A Factor-Adjusted Multiple Testing Procedure with Application to Mutual Fund Selection

TL;DR

This paper introduces a factor-adjusted multiple testing method for selecting skilled funds in finance, leveraging latent factor extraction and controlling false discoveries, with proven consistency and demonstrated effectiveness through simulations and real data.

Contribution

It develops a novel factor-adjusted p-value approach for multiple testing in linear factor models, ensuring model selection consistency even with correlated errors.

Findings

Method achieves consistent false discovery proportion estimation.

Simulation studies validate the method's effectiveness.

Real data analysis demonstrates practical utility.

Abstract

In this article, we propose a factor-adjusted multiple testing (FAT) procedure based on factor-adjusted p-values in a linear factor model involving some observable and unobservable factors, for the purpose of selecting skilled funds in empirical finance. The factor-adjusted p-values were obtained after extracting the latent common factors by the principal component method. Under some mild conditions, the false discovery proportion can be consistently estimated even if the idiosyncratic errors are allowed to be weakly correlated across units. Furthermore, by appropriately setting a sequence of threshold values approaching zero, the proposed FAT procedure enjoys model selection consistency. Extensive simulation studies and a real data analysis for selecting skilled funds in the U.S. financial market are presented to illustrate the practical utility of the proposed method. Supplementary materials for this article are available online.