A Factor-Adjusted Multiple Testing Procedure With Application to Mutual Fund Selection
提出一种基于因子调整p值的多重检验程序，用于在存在可观测和不可观测因子的线性因子模型中识别有技能的基金，并证明其错误发现比例估计一致性和模型选择一致性。
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 (Barras et al., 2010 ). The factor-adjusted p-values were obtained after extracting the latent common factors by the principal component method (Wang, 2012 ). 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.