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Mutual Funds’ Conditional Performance Free of Data Snooping Bias

Published online by Cambridge University Press:  15 February 2024

Po-Hsuan Hsu Open the ORCID record for Po-Hsuan Hsu [Opens in a new window] ,
Ioannis Kyriakou Open the ORCID record for Ioannis Kyriakou [Opens in a new window] ,
Tren Ma  and
Georgios Sermpinis
Po-Hsuan Hsu*
Affiliation:
National Tsing Hua University, College of Technology Management
Ioannis Kyriakou
Affiliation:
City, University of London, Bayes Business School ioannis.kyriakou.2@city.ac.uk
Tren Ma
Affiliation:
University of Nottingham, Nottingham University Business School tren.ma@nottingham.ac.uk
Georgios Sermpinis
Affiliation:
University of Glasgow, Adam Smith Business School georgios.sermpinis@glasgow.ac.uk
*
pohsuanhsu@mx.nthu.edu.tw (corresponding author)
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Abstract

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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” ($ {\mathrm{fFDR}}^{+} $), incorporates fund characteristics in estimating fund performance free of data snooping bias. Simulations suggest that the $ {\mathrm{fFDR}}^{+} $ 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 $ {\mathrm{fFDR}}^{+} $ outperform other tests not accounting for information updating, highlighting the importance of evaluating mutual funds from a conditional perspective.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , First View , pp. 1 - 28
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We would like to thank Hendrik Bessembinder (the editor), Keith Cuthbertson, Jens Perch Nielsen, Olivier Scaillet (the reviewer), Zheng Sun, Qifei Zhu, and the audience of the American Finance Association (AFA) student poster session and the China International Conference in Finance (CICF) for their comments. We appreciate the replies and discussions of Gery Geenens, Kuangyu Wen, and Ximing Wu on their works on density estimations, as well as Laurent Barras and Russ Wermers on the mutual fund data. Hsu acknowledges the Ministry of Education and the Ministry of Science and Technology in Taiwan for financial support (Yushan Fellow Program and MOST109-2628-H-007-001-MY4). All errors are our own.

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