Appropriate t-stat hurdles in large-scale testing

49 Pages Posted: 22 Jun 2021 Last revised: 17 Aug 2021

See all articles by Min Zhu

Min Zhu

University of Queensland

Date Written: June 13, 2021

Abstract

Large-scale inference has become increasingly popular in financial economics. I explore an empirical Bayes approach to large-scale multiple testing. The proposed approach bases its inference on the posterior probability that the null is true given the observed data. It provides a convenient way to establish data-driven $t$-stat hurdles associated with a prespecified false discovery rate or a weighted combination of the false discovery rate and false nondiscovery rate. Applying the method to meta-analysis of market anomalies, the factor zoo that is characterized by both multiple testing and p-hacking, and fund performance evaluation, I show that appropriate t-hurdles need to be raised substantially.

Keywords: Multiple testing, False discovery rate, False nondiscovery rate, Empirical Bayes, Anomalies, Factor zoo, Performance evaluation, t-hurdle, p-hacking

JEL Classification: G12, G14, C12, C21, C22, C31, C32

Suggested Citation

Zhu, Min, Appropriate t-stat hurdles in large-scale testing (June 13, 2021). Available at SSRN: https://ssrn.com/abstract=3866076 or http://dx.doi.org/10.2139/ssrn.3866076

Min Zhu (Contact Author)

University of Queensland ( email )

St Lucia
Brisbane, Queensland 4072
Australia

HOME PAGE: http://https://www.business.uq.edu.au/staff/min-zhu

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
37
Abstract Views
241
PlumX Metrics