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False (and Missed) Discoveries in Financial Economics
Author(s) -
HARVEY CAMPBELL R.,
LIU YAN
Publication year - 2020
Publication title -
the journal of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.151
H-Index - 299
eISSN - 1540-6261
pISSN - 0022-1082
DOI - 10.1111/jofi.12951
Subject(s) - type i and type ii errors , statistic , false discovery rate , computer science , multiple comparisons problem , type (biology) , econometrics , selection (genetic algorithm) , actuarial science , finance , statistics , economics , machine learning , mathematics , ecology , biochemistry , chemistry , biology , gene
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.