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