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Uncommon Value: The Characteristics and Investment Performance of Contrarian Funds

Author

Listed:
  • Kelsey D. Wei

    (Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

  • Russ Wermers

    (Department of Finance, Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • Tong Yao

    (Department of Finance, Henry B. Tippie College of Business, University of Iowa, Iowa City, Iowa 52242)

Abstract

Motivated by extant theories of herding behavior, this paper empirically identifies contrarian mutual funds as those trading most frequently against the crowd. We find that contrarian funds generate superior performance both when they trade against and with the herd, indicating that they possess superior private information. Furthermore, contrarians do not trade in a particularly correlated fashion with each other, consistent with these funds having disparate information. Our fund-level contrarian measure is largely unrelated to existing measures of fund strategy uniqueness, as both contrarian and herding funds score highly on such measures. Building on our finding of superior alphas for contrarian funds, we construct a stock-level contrarian score that reflects the aggregate stock selection information possessed by contrarian managers. This stock-level contrarian score significantly predicts stock returns after controlling for measures of stock-level herding, as well as a battery of return-predictive investment signals documented in prior studies. This paper was accepted by Wei Jiang, finance.

Suggested Citation

  • Kelsey D. Wei & Russ Wermers & Tong Yao, 2015. "Uncommon Value: The Characteristics and Investment Performance of Contrarian Funds," Management Science, INFORMS, vol. 61(10), pages 2394-2414, October.
    • Handle: RePEc:inm:ormnsc:v:61:y:2015:i:10:p:2394-2414
    DOI: 10.1287/mnsc.2014.1982
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    References listed on IDEAS

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    Cited by:

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    2. Grønborg, Niels S. & Lunde, Asger & Timmermann, Allan & Wermers, Russ, 2021. "Picking funds with confidence," Journal of Financial Economics, Elsevier, vol. 139(1), pages 1-28.
    3. Krokida, Styliani-Iris & Makrychoriti, Panagiota & Spyrou, Spyros, 2020. "Monetary policy and herd behavior: International evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 386-417.
    4. Qifei Zhu, 2020. "The Missing New Funds," Management Science, INFORMS, vol. 66(3), pages 1193-1204, March.
    5. Bai, John Jianqiu & Tang, Yuehua & Wan, Chi & Yüksel, H. Zafer, 2022. "Fund manager skill in an era of globalization: Offshore concentration and fund performance," Journal of Financial Economics, Elsevier, vol. 145(2), pages 18-40.
    6. Shin Kimura & Tomoki Kitamura & Kunio Nakashima, 2023. "Investment risk-taking and benefit adequacy under automatic balancing mechanism in the Japanese public pension system," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-19, December.
    7. Gu Wang & Jiaxuan Ye, 2023. "Fund Managers’ Competition for Investment Flows Based on Relative Performance," Journal of Optimization Theory and Applications, Springer, vol. 198(2), pages 605-643, August.
    8. Jiao, Yawen, 2022. "Decision-based trades: An analysis of institutional investors’ information advantages," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 104-115.
    9. Lee, John Byong-Tek & Ma, Jun & Margaritis, Dimitris & Yang, Wanyi, 2023. "Is anti-herding always a smart choice? Evidence from mutual funds," International Review of Financial Analysis, Elsevier, vol. 90(C).
    10. Poompak Kusawat & Nopadol Rompho, 2023. "Impact of Investing Characteristics on Financial Performance of Individual Investors: An Exploratory Study," Papers 2311.00384, arXiv.org.
    11. Matteo Bonetti, 2021. "Pension Fund Equity Performance: Herding Does Not Pay Off," Working Papers 729, DNB.
    12. Hu, Shiyang & Xiang, Cheng & Quan, Xiaofeng, 2023. "Salience theory and mutual fund flows: Empirical evidence from China," Emerging Markets Review, Elsevier, vol. 54(C).

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