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In this article, we propose a factor-adjusted multiple testing (FAT)procedure based on factor-adjusted p-values in a linear factor model involvingsome observable and unobservable factors, for the purpose of selecting skilledfunds in empirical finance. The factor-adjusted p-values were obtained afterextracting the latent common factors by the principal component method. Undersome mild conditions, the false discovery proportion can be consistentlyestimated even if the idiosyncratic errors are allowed to be weakly correlatedacross units. Furthermore, by appropriately setting a sequence of thresholdvalues approaching zero, the proposed FAT procedure enjoys model selectionconsistency. Extensive simulation studies and a real data analysis forselecting skilled funds in the U.S. financial market are presented toillustrate the practical utility of the proposed method. Supplementarymaterials for this article are available online.

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