IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v47y2022ipas1544612321005547.html
   My bibliography  Save this article

Do AI-powered mutual funds perform better?

Author

Listed:
  • Chen, Rui
  • Ren, Jinjuan

Abstract

We evaluate the performance of artificial intelligence (AI)-powered mutual funds. We find that these funds do not outperform the market per se. However, a comparison shows that AI-powered funds significantly outperform their human-managed peer funds. We further show that the outperformance of AI funds is attributable to their lower transaction cost, superior stock-picking capability, and reduced behavioral biases.

Suggested Citation

  • Chen, Rui & Ren, Jinjuan, 2022. "Do AI-powered mutual funds perform better?," Finance Research Letters, Elsevier, vol. 47(PA).
    • Handle: RePEc:eee:finlet:v:47:y:2022:i:pa:s1544612321005547
    DOI: 10.1016/j.frl.2021.102616
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612321005547
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2021.102616?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. 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.
    3. 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.
    4. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.
    5. 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.
    6. Neely, Christopher & Weller, Paul & Dittmar, Rob, 1997. "Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 32(4), pages 405-426, December.
    7. Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, vol. 4(1), pages 17-46, January.
    8. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    9. repec:bla:jfinan:v:55:y:2000:i:4:p:1655-1703 is not listed on IDEAS
    10. 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.
    11. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    12. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    13. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    14. 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.
    15. 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.
    16. Ratanabanchuen, Roongkiat & Saengchote, Kanis, 2020. "Institutional capital allocation and equity returns: Evidence from Thai mutual funds’ holdings," Finance Research Letters, Elsevier, vol. 32(C).
    17. Francesco D’Acunto & Nagpurnanand Prabhala & Alberto G Rossi, 2019. "The Promises and Pitfalls of Robo-Advising," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1983-2020.
    18. Gradojevic, Nikola & Gençay, Ramazan, 2013. "Fuzzy logic, trading uncertainty and technical trading," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 578-586.
    19. Garcia, Rene & Gencay, Ramazan, 2000. "Pricing and hedging derivative securities with neural networks and a homogeneity hint," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 93-115.
    20. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    21. Daniel, Kent, et al, 1997. "Measuring Mutual Fund Performance with Characteristic-Based Benchmarks," Journal of Finance, American Finance Association, vol. 52(3), pages 1035-1058, July.
    22. Samuel M. Hartzmark, 2015. "The Worst, the Best, Ignoring All the Rest: The Rank Effect and Trading Behavior," The Review of Financial Studies, Society for Financial Studies, vol. 28(4), pages 1024-1059.
    23. Gerard Hoberg & Nitin Kumar & Nagpurnanand Prabhala, 2018. "Mutual Fund Competition, Managerial Skill, and Alpha Persistence," The Review of Financial Studies, Society for Financial Studies, vol. 31(5), pages 1896-1929.
    24. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    25. Juhani T. Linnainmaa & Brian T. Melzer & Alessandro Previtero, 2021. "The Misguided Beliefs of Financial Advisors," Journal of Finance, American Finance Association, vol. 76(2), pages 587-621, April.
    26. repec:bla:jfinan:v:53:y:1998:i:5:p:1775-1798 is not listed on IDEAS
    27. Bailey, Warren & Kumar, Alok & Ng, David, 2011. "Behavioral biases of mutual fund investors," Journal of Financial Economics, Elsevier, vol. 102(1), pages 1-27, October.
    28. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    29. Adcock, Robert & Gradojevic, Nikola, 2019. "Non-fundamental, non-parametric Bitcoin forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    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. Zhang, Junsheng & Peng, Zezhi & Zeng, Yamin & Yang, Haisheng, 2023. "Do big data mutual funds outperform?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    2. Chen, Xun-Qi & Ma, Chao-Qun & Ren, Yi-Shuai & Lei, Yu-Tian & Huynh, Ngoc Quang Anh & Narayan, Seema, 2023. "Explainable artificial intelligence in finance: A bibliometric review," Finance Research Letters, Elsevier, vol. 56(C).
    3. Ko, Hyungjin & Byun, Junyoung & Lee, Jaewook, 2023. "A privacy-preserving robo-advisory system with the Black-Litterman portfolio model: A new framework and insights into investor behavior," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    4. Byun, Junyoung & Ko, Hyungjin & Lee, Jaewook, 2023. "A Privacy-preserving mean–variance optimal portfolio," Finance Research Letters, Elsevier, vol. 54(C).

    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. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    2. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
    3. 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).
    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. 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.
    6. Paul Handro & Bogdan Dima, 2024. "Analyzing Financial Markets Efficiency: Insights from a Bibliometric and Content Review," Journal of Financial Studies, Institute of Financial Studies, vol. 16(9), pages 119-175, May.
    7. 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.
    8. Suresh Nallareddy & Maria Ogneva, 2017. "Accrual quality, skill, and the cross-section of mutual fund returns," Review of Accounting Studies, Springer, vol. 22(2), pages 503-542, June.
    9. William J. Hippler & M. Kabir Hassan & Luca Pezzo, 2021. "Partial adjustment towards performance‐based mutual fund returns: Evidence from U.S.‐based equity funds," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5864-5883, October.
    10. Mason, Andrew & Agyei-Ampomah, Sam & Skinner, Frank, 2016. "Realism, skill, and incentives: Current and future trends in investment management and investment performance," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 31-40.
    11. Han, Chulwoo & He, Zhaodong & Toh, Alenson Jun Wei, 2023. "Pairs trading via unsupervised learning," European Journal of Operational Research, Elsevier, vol. 307(2), pages 929-947.
    12. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    13. 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.
    14. 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.
    15. Hao Jiang & Michela Verardo, "undated". "Does herding behavior reveal skill? An analysis of mutual fund performance," FMG Discussion Papers dp720, Financial Markets Group.
    16. Ľuboš Pástor & M Blair Vorsatz & Jeffrey Pontiff, 0. "Mutual Fund Performance and Flows during the COVID-19 Crisis," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(4), pages 791-833.
    17. Chen, Li-Wen & Adams, Andrew & Taffler, Richard, 2013. "What style-timing skills do mutual fund “stars” possess?," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 156-173.
    18. 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.
    19. 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.
    20. Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Post-Print hal-04144588, HAL.

    More about this item

    Keywords

    Artificial intelligence; Mutual fund performance; Behavioral biases;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    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:eee:finlet:v:47:y:2022:i:pa:s1544612321005547. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.