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Artificial Market Timing in Mutual Funds

Published online by Cambridge University Press:  28 November 2022

Jeffrey A. Busse Open the ORCID record for Jeffrey A. Busse [Opens in a new window] ,
Jing Ding ,
Lei Jiang  and
Yuehua Tang Open the ORCID record for Yuehua Tang [Opens in a new window]
Jeffrey A. Busse
Affiliation:
Emory University Goizueta Business School jbusse@emory.edu
Jing Ding*
Affiliation:
Harbin Institute of Technology School of Management
Lei Jiang
Affiliation:
Tsinghua University School of Economics and Management jianglei@sem.tsinghua.edu.cn
Yuehua Tang
Affiliation:
University of Florida Warrington College of Business yuehua.tang@warrington.ufl.edu
*
dingjingut@gmail.com (corresponding author)
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Abstract

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We document statistically significant relations between mutual fund betas and past market returns driven by fund feedback trading. Against this backdrop, evidence of “artificial” market timing emerges when standard market timing regressions are estimated across periods that span time variation in fund systematic risk levels, as is typical. Artificial timing significantly explains the inverse relation between timing model estimates of market timing and stock selectivity. A fund’s feedback trading relates to its past performance and remains significant after accounting for trading on momentum. Fund flows suggest that investors value feedback trading, which helps hedge downside risk during bear markets.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 58 , Issue 8 , December 2023 , pp. 3450 - 3481
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 (https://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), 2022. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We thank an anonymous referee and Hendrik Bessembinder (the editor) for their helpful comments. The research is supported by the National Natural Science Foundation of China (NSFC) Grant (72121001), Tsinghua University through the Initiative Scientific Research Program, Institute for Industrial Innovation and Finance, and Tsinghua National Laboratory for Information Science and Technology.

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