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We consider controlling the false discovery rate for testing many time serieswith an unknown cross-sectional correlation structure. Given a large number ofhypotheses, false and missing discoveries can plague an analysis. While manyprocedures have been proposed to control false discovery, most of them eitherassume independent hypotheses or lack statistical power. A problem ofparticular interest is in financial asset pricing, where the goal is todetermine which ``factors" lead to excess returns out of a large number ofpotential factors. Our contribution is two-fold. First, we show the consistencyof Fama and French's prominent method under multiple testing. Second, wepropose a novel method for false discovery control using double bootstrapping.We achieve superior statistical power to existing methods and prove that thefalse discovery rate is controlled. Simulations and a real data applicationillustrate the efficacy of our method over existing methods.

Controlling False Discovery Rates under Cross-Sectional Correlations