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Machine-Learning the Skill of Mutual Fund Managers

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  • Kaniel, Ron
  • Lin, Zihan
  • Pelger, Markus
  • Van Nieuwerburgh, Stijn

Abstract

We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, before and after fees. The outperformance persists for more than three years. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum.

Suggested Citation

  • Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-Learning the Skill of Mutual Fund Managers," CEPR Discussion Papers 18129, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:18129
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    1. Kelly, Bryan T. & Pruitt, Seth & Su, Yinan, 2019. "Characteristics are covariances: A unified model of risk and return," Journal of Financial Economics, Elsevier, vol. 134(3), pages 501-524.
    2. Markus Ibert & Ron Kaniel & Stijn Van Nieuwerburgh & Roine Vestman, 2018. "Are Mutual Fund Managers Paid for Investment Skill?," Review of Financial Studies, Society for Financial Studies, vol. 31(2), pages 715-772.
    3. Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021. "Corrigendum: Bond Risk Premiums with Machine Learning [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1090-1103.
    4. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    5. Brad M. Barber & Xing Huang & Terrance Odean, 2016. "Which Factors Matter to Investors? Evidence from Mutual Fund Flows," The Review of Financial Studies, Society for Financial Studies, vol. 29(10), pages 2600-2642.
    6. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    7. Marcin Kacperczyk & Clemens Sialm & Lu Zheng, 2008. "Unobserved Actions of Mutual Funds," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2379-2416, November.
    8. Lu Zheng, 1999. "Is Money Smart? A Study of Mutual Fund Investors' Fund Selection Ability," Journal of Finance, American Finance Association, vol. 54(3), pages 901-933, June.
    9. Martin Lettau & Markus Pelger & Stijn Van Nieuwerburgh, 2020. "Factors That Fit the Time Series and Cross-Section of Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2274-2325.
    10. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    11. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    12. Travis Sapp & Ashish Tiwari, 2004. "Does Stock Return Momentum Explain the "Smart Money" Effect?," Journal of Finance, American Finance Association, vol. 59(6), pages 2605-2622, December.
    13. Ralph S. J. Koijen & Motohiro Yogo, 2019. "A Demand System Approach to Asset Pricing," Journal of Political Economy, University of Chicago Press, vol. 127(4), pages 1475-1515.
    14. Nikolai Roussanov & Hongxun Ruan & Yanhao Wei & Stijn Van Nieuwerburgh, 2021. "Marketing Mutual Funds," The Review of Financial Studies, Society for Financial Studies, vol. 34(6), pages 3045-3094.
    15. Robert Kosowski, 2011. "Do Mutual Funds Perform When It Matters Most to Investors? US Mutual Fund Performance and Risk in Recessions and Expansions," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 1(03), pages 607-664.
    16. K. J. Martijn Cremers & Antti Petajisto, 2009. "How Active Is Your Fund Manager? A New Measure That Predicts Performance," The Review of Financial Studies, Society for Financial Studies, vol. 22(9), pages 3329-3365, September.
    17. Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew KarolyiEditor, 2020. "Dissecting Characteristics Nonparametrically," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    18. Itzhak Ben-David & Jiacui Li & Andrea Rossi & Yang Song, 2022. "What Do Mutual Fund Investors Really Care About?," The Review of Financial Studies, Society for Financial Studies, vol. 35(4), pages 1723-1774.
    19. Yang Song, 2020. "The Mismatch Between Mutual Fund Scale and Skill," Journal of Finance, American Finance Association, vol. 75(5), pages 2555-2589, October.
    20. Stambaugh, Robert F. & Yu, Jianfeng & Yuan, Yu, 2012. "The short of it: Investor sentiment and anomalies," Journal of Financial Economics, Elsevier, vol. 104(2), pages 288-302.
    21. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    22. Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021. "Bond Risk Premiums with Machine Learning [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1046-1089.
    23. 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.
    24. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    25. Martijn Cremers & Antti Petajisto, 2006. "How Active is Your Fund Manager? A New Measure That Predicts Performance," Yale School of Management Working Papers amz2370, Yale School of Management, revised 01 May 2009.
    26. Marcin Kacperczyk & Clemens Sialm & Lu Zheng, 2005. "On the Industry Concentration of Actively Managed Equity Mutual Funds," Journal of Finance, American Finance Association, vol. 60(4), pages 1983-2011, August.
    27. Coval, Joshua & Stafford, Erik, 2007. "Asset fire sales (and purchases) in equity markets," Journal of Financial Economics, Elsevier, vol. 86(2), pages 479-512, November.
    28. G Andrew Karolyi & Stijn Van Nieuwerburgh, 2020. "New Methods for the Cross-Section of Returns," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1879-1890.
    29. Narasimhan Jegadeesh & Chandra Sekhar Mangipudi, 2021. "What Do Fund Flows Reveal about Asset Pricing Models and Investor Sophistication? [Alpha or beta in the eye of the beholder: What drives hedge fund flows?]," The Review of Financial Studies, Society for Financial Studies, vol. 34(1), pages 108-148.
    30. Jeremiah Green & John R. M. Hand & X. Frank Zhang, 2017. "The Characteristics that Provide Independent Information about Average U.S. Monthly Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4389-4436.
    31. Dong Lou, 2012. "A Flow-Based Explanation for Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 25(12), pages 3457-3489.
    32. David P. Brown & Youchang Wu, 2016. "Mutual Fund Flows and Cross-Fund Learning within Families," Journal of Finance, American Finance Association, vol. 71(1), pages 383-424, February.
    33. 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.
    34. Martijn Cremers & Antti Petajisto, 2006. "How Active is Your Fund Manager? A New Measure That Predicts Performance," Yale School of Management Working Papers amz2370, Yale School of Management, revised 01 May 2009.
    35. Martin Lettau & Markus Pelger, 2020. "Factors That Fit the Time Series and Cross-Section of Stock Returns," Review of Finance, European Finance Association, vol. 33(5), pages 2274-2325.
    36. Massa, Massimo & Yadav, Vijay, 2015. "Investor Sentiment and Mutual Fund Strategies," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 50(4), pages 699-727, August.
    37. Berk, Jonathan B. & van Binsbergen, Jules H., 2016. "Assessing asset pricing models using revealed preference," Journal of Financial Economics, Elsevier, vol. 119(1), pages 1-23.
    38. Stefano Giglio & Matteo Maggiori & Johannes Stroebel & Stephen Utkus, 2021. "Five Facts about Beliefs and Portfolios," American Economic Review, American Economic Association, vol. 111(5), pages 1481-1522, May.
    39. 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.
    40. 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).
    41. Hitesh Doshi & Redouane Elkamhi & Mikhail Simutin, 2015. "Managerial Activeness and Mutual Fund Performance," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 5(2), pages 156-184.
    42. Wenbo Wu & Jiaqi Chen & Zhibin (Ben) Yang & Michael L. Tindall, 2021. "A Cross-Sectional Machine Learning Approach for Hedge Fund Return Prediction and Selection," Management Science, INFORMS, vol. 67(7), pages 4577-4601, July.
    43. Narasimhan Jegadeesh & Chandra Sekhar Mangipudi & Stijn Van Nieuwerburgh, 2021. "What Do Fund Flows Reveal about Asset Pricing Models and Investor Sophistication?," Review of Economic Studies, Oxford University Press, vol. 34(1), pages 108-148.
    44. Fama, Eugene F & French, Kenneth R, 1996. "Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, American Finance Association, vol. 51(1), pages 55-84, March.
    45. Robert F. Stambaugh, 2014. "Presidential Address: Investment Noise and Trends," Journal of Finance, American Finance Association, vol. 69(4), pages 1415-1453, August.
    46. Samuel G. Hanson & Adi Sunderam, 2014. "The Growth and Limits of Arbitrage: Evidence from Short Interest," The Review of Financial Studies, Society for Financial Studies, vol. 27(4), pages 1238-1286.
    47. Eugene F. Fama & Kenneth R. French, 2010. "Luck versus Skill in the Cross‐Section of Mutual Fund Returns," Journal of Finance, American Finance Association, vol. 65(5), pages 1915-1947, October.
    48. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
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    Cited by:

    1. Damir Filipovi'c & Puneet Pasricha, 2022. "Empirical Asset Pricing via Ensemble Gaussian Process Regression," Papers 2212.01048, arXiv.org.
    2. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
    3. Li, Zhiyong & Rao, Xiao, 2023. "Exploring the zoo of predictors for mutual fund performance in China," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).

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    More about this item

    Keywords

    Mutual fund performance;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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