IDEAS home Printed from https://ideas.repec.org/p/gnv/wpgsem/unige110006.html
   My bibliography  Save this paper

The Cross-Sectional Distribution of Fund Skill Measures

Author

Listed:
  • Barras, Laurent
  • Gagliardini, Patrick
  • Scaillet, Olivier

Abstract

We develop a simple, non-parametric approach for estimating the entire distribution of skill. Our approach avoids the challenge of correctly specifying the distribution, and allows for a joint analysis of multiple measures–a key requirement for examining skill. Our results show that more than 85% of the funds are skilled at detecting profitable trades, but unskilled at overriding capacity constraints. Aggregating both skill dimensions using the value added, we find that around 70% of the funds are able to generate profits. The average value added after funds have reached their long-term size equals 7.1 mio. per year, which represents two thirds of the optimal value predicted by neoclassical theory. For all skill measures, the distribution is highly non-normal and reveals a strong heterogeneity across funds.

Suggested Citation

  • Barras, Laurent & Gagliardini, Patrick & Scaillet, Olivier, 2018. "The Cross-Sectional Distribution of Fund Skill Measures," Working Papers unige:110006, University of Geneva, Geneva School of Economics and Management.
  • Handle: RePEc:gnv:wpgsem:unige:110006
    as

    Download full text from publisher

    File URL: https://luniarchidoc4.unige.ch/archive-ouverte/unige:110006/ATTACHMENT01
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Patrick Gagliardini & Elisa Ossola & Olivier Scaillet, 2016. "Time‐Varying Risk Premium in Large Cross‐Sectional Equity Data Sets," Econometrica, Econometric Society, vol. 84, pages 985-1046, May.
    2. Harrison Hong & Terence Lim & Jeremy C. Stein, 2000. "Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies," Journal of Finance, American Finance Association, vol. 55(1), pages 265-295, February.
    3. Michael C. Jensen, 1968. "The Performance Of Mutual Funds In The Period 1945–1964," Journal of Finance, American Finance Association, vol. 23(2), pages 389-416, May.
    4. Amihud, Yakov & Hurvich, Clifford M., 2004. "Predictive Regressions: A Reduced-Bias Estimation Method," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(4), pages 813-841, December.
    5. Klaas P. Baks & Andrew Metrick & Jessica Wachter, 2001. "Should Investors Avoid All Actively Managed Mutual Funds? A Study in Bayesian Performance Evaluation," Journal of Finance, American Finance Association, vol. 56(1), pages 45-85, February.
    6. Paul A. Gompers & Andrew Metrick, 2001. "Institutional Investors and Equity Prices," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(1), pages 229-259.
    7. repec:bla:jfinan:v:55:y:2000:i:4:p:1655-1703 is not listed on IDEAS
    8. Susan E. K. Christoffersen & David K. Musto, 2002. "Demand Curves and the Pricing of Money Management," The Review of Financial Studies, Society for Financial Studies, vol. 15(5), pages 1499-1524.
    9. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, vol. 65(1), pages 179-216, February.
    10. Russ Wermers, 2000. "Mutual Fund Performance: An Empirical Decomposition into Stock‐Picking Talent, Style, Transactions Costs, and Expenses," Journal of Finance, American Finance Association, vol. 55(4), pages 1655-1695, August.
    11. repec:bla:jfinan:v:59:y:2004:i:1:p:261-288 is not listed on IDEAS
    12. Javier Gil‐Bazo & Pablo Ruiz‐Verdú, 2009. "The Relation between Price and Performance in the Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 64(5), pages 2153-2183, October.
    13. Elton, Edwin J, et al, 1993. "Efficiency with Costly Information: A Reinterpretation of Evidence from Managed Portfolios," The Review of Financial Studies, Society for Financial Studies, vol. 6(1), pages 1-22.
    14. Jiahua Chen, 2017. "On finite mixture models," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 1(1), pages 15-27, January.
    15. Shanken, Jay, 1992. "On the Estimation of Beta-Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 5(1), pages 1-33.
    16. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    17. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    18. Ait-Sahalia, Yacine, 1996. "Nonparametric Pricing of Interest Rate Derivative Securities," Econometrica, Econometric Society, vol. 64(3), pages 527-560, May.
    19. Jonathan B. Berk & Richard C. Green, 2004. "Mutual Fund Flows and Performance in Rational Markets," Journal of Political Economy, University of Chicago Press, vol. 112(6), pages 1269-1295, December.
    20. Habib, Michel A. & Johnsen, D. Bruce, 2016. "The quality-assuring role of mutual fund advisory fees," International Review of Law and Economics, Elsevier, vol. 46(C), pages 1-19.
    21. Grinblatt, Mark & Titman, Sheridan D, 1989. "Mutual Fund Performance: An Analysis of Quarterly Portfolio Holdings," The Journal of Business, University of Chicago Press, vol. 62(3), pages 393-416, July.
    22. Marcin Kacperczyk & Stijn Van Nieuwerburgh & Laura Veldkamp, 2014. "Time-Varying Fund Manager Skill," Journal of Finance, American Finance Association, vol. 69(4), pages 1455-1484, August.
    23. Berk, Jonathan B. & van Binsbergen, Jules H., 2015. "Measuring skill in the mutual fund industry," Journal of Financial Economics, Elsevier, vol. 118(1), pages 1-20.
    24. Avramov, Doron & Barras, Laurent & Kosowski, Robert, 2013. "Hedge Fund Return Predictability Under the Magnifying Glass," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(4), pages 1057-1083, August.
    25. Campbell R Harvey & Yan Liu, 2018. "Detecting Repeatable Performance," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2499-2552.
    26. Michel A. Habib & D. Bruce Johnsen, 2016. "The Quality-Assuring Role of Mutual Fund Advisory Fees," Swiss Finance Institute Research Paper Series 16-16, Swiss Finance Institute, revised Apr 2016.
    27. Pástor, Ľuboš & Stambaugh, Robert F. & Taylor, Lucian A., 2015. "Scale and skill in active management," Journal of Financial Economics, Elsevier, vol. 116(1), pages 23-45.
    28. Chen, Yong & Cliff, Michael & Zhao, Haibei, 2017. "Hedge Funds: The Good, the Bad, and the Lucky," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(3), pages 1081-1109, June.
    29. Jones, Christopher S. & Shanken, Jay, 2005. "Mutual fund performance with learning across funds," Journal of Financial Economics, Elsevier, vol. 78(3), pages 507-552, December.
    30. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    31. Stanton, Richard, 1997. "A Nonparametric Model of Term Structure Dynamics and the Market Price of Interest Rate Risk," Journal of Finance, American Finance Association, vol. 52(5), pages 1973-2002, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jochmans, Koen & Weidner, Martin, 2024. "Inference On A Distribution From Noisy Draws," Econometric Theory, Cambridge University Press, vol. 40(1), pages 60-97, February.
    2. Cheng, Tingting & Yan, Cheng & Yan, Yayi, 2021. "Improved inference for fund alphas using high-dimensional cross-sectional tests," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 57-81.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Laurent Barras & Patrick Gagliardini & Olivier Scaillet, 2022. "Skill, Scale, and Value Creation in the Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 77(1), pages 601-638, February.
    2. DeMiguel, Victor & Gil-Bazo, Javier & Nogales, Francisco J. & Santos, André A.P., 2023. "Machine learning and fund characteristics help to select mutual funds with positive alpha," Journal of Financial Economics, Elsevier, vol. 150(3).
    3. Ľuboš Pástor & Robert F. Stambaugh & Lucian A. Taylor, 2017. "Do Funds Make More When They Trade More?," Journal of Finance, American Finance Association, vol. 72(4), pages 1483-1528, August.
    4. 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.
    5. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, vol. 65(1), pages 179-216, February.
    6. Ardia, David & Barras, Laurent & Gagliardini, Patrick & Scaillet, Olivier, 2024. "Is it alpha or beta? Decomposing hedge fund returns when models are misspecified," Journal of Financial Economics, Elsevier, vol. 154(C).
    7. Cai, Biqing & Cheng, Tingting & Yan, Cheng, 2018. "Time-varying skills (versus luck) in U.S. active mutual funds and hedge funds," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 81-106.
    8. Lan, Chunhua & Moneta, Fabio & Wermers, Russ, 2018. "Holding Horizon: A New Measure of Active Investment Management," CFR Working Papers 15-06, University of Cologne, Centre for Financial Research (CFR), revised 2018.
    9. Livingston, Miles & Yao, Ping & Zhou, Lei, 2019. "The volatility of mutual fund performance," Journal of Economics and Business, Elsevier, vol. 104(C), pages 1-1.
    10. Ferson, Wayne & Mo, Haitao, 2016. "Performance measurement with selectivity, market and volatility timing," Journal of Financial Economics, Elsevier, vol. 121(1), pages 93-110.
    11. Bianchi, Daniele & Babiak, Mykola, 2022. "On the performance of cryptocurrency funds," Journal of Banking & Finance, Elsevier, vol. 138(C).
    12. Fisher, Mark & Jensen, Mark J., 2022. "Bayesian nonparametric learning of how skill is distributed across the mutual fund industry," Journal of Econometrics, Elsevier, vol. 230(1), pages 131-153.
    13. Hung, Pi-Hsia & Lien, Donald & Kuo, Ming-Sin, 2020. "Window dressing in equity mutual funds," The Quarterly Review of Economics and Finance, Elsevier, vol. 78(C), pages 338-354.
    14. Huang, Rong & Pilbeam, Keith & Pouliot, William, 2021. "Do actively managed US mutual funds produce positive alpha?," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 472-492.
    15. Lee, Jaeram & Jeon, Hyunglae & Kang, Jangkoo & Lee, Changjun, 2020. "Do actively managed mutual funds exploit stock market mispricing?," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    16. Dahm, Laura K. & Sorhage, Christoph, 2015. "Milk or wine: Mutual funds' (dis)economies of life," CFR Working Papers 15-05, University of Cologne, Centre for Financial Research (CFR).
    17. Jiang, Hao & Verardo, Michela, 2013. "Does herding behavior reveal skill? An analysis of mutual fund performance," LSE Research Online Documents on Economics 119034, London School of Economics and Political Science, LSE Library.
    18. Patton, Andrew J. & Weller, Brian M., 2020. "What you see is not what you get: The costs of trading market anomalies," Journal of Financial Economics, Elsevier, vol. 137(2), pages 515-549.
    19. Huazhu Zhang & Cheng Yan, 2018. "A skeptical appraisal of the bootstrap approach in fund performance evaluation," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 27(2), pages 49-86, May.
    20. Ferson, Wayne E., 2013. "Investment Performance: A Review and Synthesis," 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 969-1010, Elsevier.

    More about this item

    Keywords

    Mutual fund skill; Non-parametric density estimation; Large panel;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gnv:wpgsem:unige:110006. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jean-Blaise Claivaz (email available below). General contact details of provider: https://edirc.repec.org/data/depgech.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.