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Does Industry Timing Ability of Hedge Funds Predict Their Future Performance, Survival, and Fund Flows?

Published online by Cambridge University Press:  13 October 2020

Turan G. Bali ,
Stephen J. Brown ,
Mustafa O. Caglayan  and
Umut Celiker
Turan G. Bali
Affiliation:
Georgetown University McDonough School of Businessturan.bali@georgetown.edu
Stephen J. Brown
Affiliation:
Monash Business School and NYU Stern School of Businessstephen.brown@monash.edu
Mustafa O. Caglayan*
Affiliation:
Florida International University College of Business
Umut Celiker
Affiliation:
Cleveland State University Monte Ahuja College of Businessu.celiker@csuohio.edu
*
mustafa.caglayan@fiu.edu (corresponding author)
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Abstract

This paper investigates hedge funds’ ability to time industry-specific returns and shows that funds’ timing ability in the manufacturing industry improves their future performance, probability of survival, and ability to attract more capital. The results indicate that the best industry-timing hedge funds in the manufacturing sector have the highest return exposure to earnings surprises. This, together with persistently sticky earnings surprises, transparent information environment in regards to earnings releases, and large post-earnings-announcement drift in the manufacturing industry, explain to a great extent why best-timing hedge funds can generate significantly larger future returns compared to worst-timing hedge funds.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 56 , Issue 6 , September 2021 , pp. 2136 - 2169
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

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Footnotes

We thank an anonymous referee, Vikas Agarwal, George Aragon, Yong Chen, Jarrad Harford (the editor), Bing Liang, and Tim Simin (a referee) for their constructive comments and suggestions. We also benefited from discussions with Michael Gallmeyer, Shahid Hamid, Qiping Huang, Qiang Kang, Edward Lawrance, Ozde Oztekin, Gokhan Sonaer, Quan Wen, and seminar participants at Florida International University and Georgetown University. We thank Kenneth French and David Hsieh for making a large amount of data publicly available in their online data library. We thank Vikas Agarwal for sharing data on options factors. All errors remain our responsibility.

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