On false discoveries of standard t-tests in investment management applications

Benjamin R. Auer ()
Additional contact information
Benjamin R. Auer: Brandenburg University of Technology Cottbus-Senftenberg

Review of Managerial Science, 2022, vol. 16, issue 3, No 5, 768 pages

Abstract: Abstract Financial managers routinely use the one-sample t-test to evaluate whether the mean returns of investment assets, strategies or funds are significantly different from zero. Simultaneously, however, they often ignore the fact that its application is not generally justified, in other words, that its usefulness depends on the properties of the population. We show by Monte Carlo simulation that, especially in skewed and/or autocorrelated populations, test decisions based on the t-test can be severely biased. More specifically, for sample sizes typically used in investment performance evaluation, the probability of falsely diagnosing a significant mean return–the false discovery rate–is significantly higher than the nominal error probability set in testing. We additionally illustrate that the popular empirical practices of (i) replacing the t-quantile with the standard normal quantile and (ii) removing outliers before conducting the t-test have crucial elevating impact on the false discovery rate.

Keywords: One-sample t-test; Non-normality; Autocorrelation; Normal quantile; Outliers (search for similar items in EconPapers)
JEL-codes: C12 C15 G11 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11846-021-00453-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:rvmgts:v:16:y:2022:i:3:d:10.1007_s11846-021-00453-0

Ordering information: This journal article can be ordered from
http://www.springer.com/business/journal/11846

DOI: 10.1007/s11846-021-00453-0

Access Statistics for this article

Review of Managerial Science is currently edited by R. Ewert and W. Kürsten

More articles in Review of Managerial Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().