Tackling False Positives in Finance: A Statistical Toolbox With Applications

48 Pages Posted: 24 Jun 2018 Last revised: 22 Jul 2018

See all articles by Jae H. Kim

Jae H. Kim

La Trobe University - School of Economics and Finance

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Date Written: June 7, 2018

Abstract

Serious concerns have been raised that false positive findings are widespread in empirical research in finance. This is largely because researchers almost exclusively adopt the "p-value less than 0.05" criterion for statistical significance; and they are often not fully aware of large-sample biases which can potentially mislead their research outcomes. This paper proposes that a statistical toolbox (rather than a single hammer) be used in empirical research, which offers researchers a range of statistical instruments, including alternatives to the p-value criterion and cautionary analyses for large-sample bias. It is found that the positive results obtained under the p-value criterion cannot stand, when the toolbox is applied to three notable studies in finance.

Keywords: Optimal Significance Level, Bayes Factor, Decision Theory, Fallacy of Rejection, Large-Sample Bias

JEL Classification: C12, G10

Suggested Citation

Kim, Jae H., Tackling False Positives in Finance: A Statistical Toolbox With Applications (June 7, 2018). Available at SSRN: https://ssrn.com/abstract=3192611 or http://dx.doi.org/10.2139/ssrn.3192611

Jae H. Kim (Contact Author)

La Trobe University - School of Economics and Finance ( email )

Department of Finance
La Trobe Business School
Bundoora, IN 3086
Australia

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