False (and Missed) Discoveries in Financial Economics

Published

Journal Article

© 2020 the American Finance Association Multiple testing plagues many important questions in finance such as fund and factor selection. We propose a new way to calibrate both Type I and Type II errors. Next, using a double-bootstrap method, we establish a t-statistic hurdle that is associated with a specific false discovery rate (e.g., 5%). We also establish a hurdle that is associated with a certain acceptable ratio of misses to false discoveries (Type II error scaled by Type I error), which effectively allows for differential costs of the two types of mistakes. Evaluating current methods, we find that they lack power to detect outperforming managers.

Full Text

Duke Authors

Cited Authors

  • Harvey, CR; Liu, Y

Published Date

  • October 1, 2020

Published In

Volume / Issue

  • 75 / 5

Start / End Page

  • 2503 - 2553

Electronic International Standard Serial Number (EISSN)

  • 1540-6261

International Standard Serial Number (ISSN)

  • 0022-1082

Digital Object Identifier (DOI)

  • 10.1111/jofi.12951

Citation Source

  • Scopus