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Mutual Funds’ Conditional Performance Free of Data Snooping Bias

Hsu, P-H., Kyriakou, I. ORCID: 0000-0001-9592-596X, Ma, T. & Sermpinis, G. (2023). Mutual Funds’ Conditional Performance Free of Data Snooping Bias. Journal of Financial and Quantitative Analysis,

Abstract

We introduce a test to assess mutual funds’ “conditional” performance that is based on updated information and corrects data snooping bias. Our method, named the functional False Discovery Rate “plus” (fF DR+), incorporates fund characteristics in estimating fund performance free of data snooping bias. Simulations suggest that the fF DR+ controls well the ratio of false discoveries and gains considerable power over prior methods that do not account for extra information. Portfolios of funds selected by the fF DR+ outperform other tests not accounting for information updating, highlighting the importance of evaluating mutual funds from a conditional perspective.

Publication Type: Article
Additional Information: This article has been published in a revised form in Journal of Financial and Quantitative Analysis https://jfqa.org/2023/12/22/mutual-fundsae-conditional-performance-free-of-data-snooping-bias/#:~:text=We%20introduce%20a%20test%20to,free%20of%20data%20snooping%20bias. This version is free to view and download for private research and study only. Not for re-distribution or re-use. © copyright holder.
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Bayes Business School
Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:
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