A Stepwise Cauchy Combination Test for Multiple Testing Problems with Financial Applications

Abstract We propose the stepwise Cauchy combination test (StepC), a new procedure for multiple testing with dependent test statistics and sparse signals. Unlike the global version, StepC pinpoints which p-values drive rejections, while maintaining strong familywise error control. It is less conservative under dependence and more powerful than conventional multiple testing corrections. In simulations and in applications to drift burst detection and testing for nonzero alphas, StepC consistently boosts power and yields more meaningful rejections, making it a practical alternative for large-scale financial datasets.