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Holdings Data, Security Returns, and the Selection of Superior Mutual Funds

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  • Elton, Edwin J.
  • Gruber, Martin J.
  • Blake, Christopher R.

Abstract

In this paper we show that selecting mutual funds using alpha computed from a fund’s holdings and security betas produces better future alphas than selecting funds using alpha computed from a time-series regression on fund returns. This is true whether future alphas are computed using holdings and security betas or a time-series regression on fund returns. Furthermore, we show that the more frequently the holdings data are available, the greater the benefit. This has major implications for the Securities and Exchange Commission’s recent ruling on the frequency of holdings disclosure and the information plan sponsors should collect from portfolio managers. We also explore the effect of conditioning betas on macroeconomic variables as suggested by Ferson and Schadt (1996) to identify superior-performing mutual funds as well as the alternative way of employing holdings data proposed by Grinblatt and Titman (1993).

Suggested Citation

  • Elton, Edwin J. & Gruber, Martin J. & Blake, Christopher R., 2011. "Holdings Data, Security Returns, and the Selection of Superior Mutual Funds," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 46(2), pages 341-367, April.
    • Handle: RePEc:cup:jfinqa:v:46:y:2011:i:02:p:341-367_00
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    Cited by:

    1. Ammann, Manuel & Cochardt, Alexander Elmar & Straumann, Simon & Weigert, Florian, 2022. "Back to the roots: Ancestral origin and mutual fund manager portfolio choice," CFR Working Papers 22-04, University of Cologne, Centre for Financial Research (CFR).
    2. Qifei Zhu, 2020. "The Missing New Funds," Management Science, INFORMS, vol. 66(3), pages 1193-1204, March.
    3. Jing-Zhi Huang & Ying Wang, 2014. "Timing Ability of Government Bond Fund Managers: Evidence from Portfolio Holdings," Management Science, INFORMS, vol. 60(8), pages 2091-2109, August.
    4. Li, Zhiyong & Rao, Xiao, 2023. "Exploring the zoo of predictors for mutual fund performance in China," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    5. Wang, Yaping & Paek, Miyoun & Ko, Kwangsoo, 2019. "The performance of Chinese equity funds: An extension of DGTW model," Japan and the World Economy, Elsevier, vol. 51(C), pages 1-1.
    6. Martin Lettau & Ananth Madhavan, 2018. "Exchange-Traded Funds 101 for Economists," Journal of Economic Perspectives, American Economic Association, vol. 32(1), pages 135-154, Winter.
    7. Wayne Ferson & Junbo L Wang, 2021. "A Panel Regression Approach to Holdings-Based Fund Performance Measures [Multiperiod performance persistence analysis of hedge funds]," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 11(4), pages 695-734.
    8. Anastasia Petraki & Anna Zalewska, 2017. "Jumping over a low hurdle: personal pension fund performance," Review of Quantitative Finance and Accounting, Springer, vol. 48(1), pages 153-190, January.
    9. Emawtee Bissoondoyal‐Bheenick & Robert Brooks & Hung Xuan Do, 2023. "Risk Analysis of Pension Fund Investment Choices," Abacus, Accounting Foundation, University of Sydney, vol. 59(3), pages 872-898, September.
    10. Choi, Jaewon & Kronlund, Mathias & Oh, Ji Yeol Jimmy, 2022. "Sitting bucks: Stale pricing in fixed income funds," Journal of Financial Economics, Elsevier, vol. 145(2), pages 296-317.
    11. Ferson, Wayne & Mo, Haitao, 2016. "Performance measurement with selectivity, market and volatility timing," Journal of Financial Economics, Elsevier, vol. 121(1), pages 93-110.
    12. Choi, Jaewon & Richardson, Matthew, 2016. "The volatility of a firm's assets and the leverage effect," Journal of Financial Economics, Elsevier, vol. 121(2), pages 254-277.
    13. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.
    14. Srinidhi Kanuri & Robert W. McLeod, 2016. "Sustainable competitive advantage and stock performance: the case for wide moat stocks," Applied Economics, Taylor & Francis Journals, vol. 48(52), pages 5117-5127, November.
    15. Y. Chung & Thomas Kim, 2015. "The win–loss ratio as an ability signal of mutual fund managers: a measure that is less influenced by luck," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(4), pages 301-335, November.
    16. Zhe Chen & David R. Gallagher & Adrian D. Lee, 2017. "Testing the effect of portfolio holdings disclosure in an environment absent of mandatory disclosure," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57, pages 101-116, April.
    17. Cao, Charles & Iliev, Peter & Velthuis, Raisa, 2017. "Style drift: Evidence from small-cap mutual funds," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 42-57.
    18. Elton, Edwin J. & Gruber, Martin J., 2013. "Mutual Funds," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1011-1061, Elsevier.
    19. Pankaj K. Agarwal & H. K. Pradhan, 2018. "Mutual Fund Performance Using Unconditional Multifactor Models: Evidence from India," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 17(2_suppl), pages 157-184, August.
    20. Scott Bennett & David R Gallagher & Graham Harman & Geoffrey J Warren & Lihui Xi, 2016. "Alpha generation in portfolio management: Long-run Australian equity fund evidence," Australian Journal of Management, Australian School of Business, vol. 41(1), pages 107-140, February.

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