IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i6p695-d522910.html
   My bibliography  Save this article

Can Artificial Neural Networks Predict the Survival Capacity of Mutual Funds? Evidence from Spain

Author

Listed:
  • Laura Fabregat-Aibar

    (Department of Business Management, Faculty of Business and Economics, Universitat Rovira i Virgili, 43204 Reus, Spain)

  • Maria-Teresa Sorrosal-Forradellas

    (Department of Business Management, Faculty of Business and Economics, Universitat Rovira i Virgili, 43204 Reus, Spain)

  • Glòria Barberà-Mariné

    (Department of Business Management, Faculty of Business and Economics, Universitat Rovira i Virgili, 43204 Reus, Spain)

  • Antonio Terceño

    (Department of Business Management, Faculty of Business and Economics, Universitat Rovira i Virgili, 43204 Reus, Spain)

Abstract

Recently, the total net assets of mutual funds have increased considerably and turned them into one of the main investment instruments. Despite this increment, every year a considerable number of funds disappear. The main purpose of this paper is to determine if the neural networks can be a valid instrument to detect the survival capacity of a fund, using the traditional variables linked to the literature of disappearance funds: age, size, performance and volatility. This paper also incorporates annualized variation in return and the Sharpe ratio as variables. The data used is a sample of Spanish mutual funds during 2018 and 2019. The results show that the network correctly classifies funds into surviving and non-surviving with a total error of 13%. Moreover, it shows that not all variables are significant to determine the survival capacity of a fund. The results indicate that surviving and non-surviving funds differ in variables related to performance and its variation, volatility and the Sharpe ratio. However, age and size are not significant variables. As a conclusion, the neural network correctly predicts the 87% of survival capacity of mutual funds. Therefore, this methodology can be used to classify this financial instrument according to its survival or disappearance.

Suggested Citation

  • Laura Fabregat-Aibar & Maria-Teresa Sorrosal-Forradellas & Glòria Barberà-Mariné & Antonio Terceño, 2021. "Can Artificial Neural Networks Predict the Survival Capacity of Mutual Funds? Evidence from Spain," Mathematics, MDPI, vol. 9(6), pages 1-10, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:6:p:695-:d:522910
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/6/695/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/6/695/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sherrill, D. Eli & Stark, Jeffrey R., 2018. "ETF liquidation determinants," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 357-373.
    2. Laura Andreu & José Luis Sarto, 2016. "Financial consequences of mutual fund mergers," The European Journal of Finance, Taylor & Francis Journals, vol. 22(7), pages 529-550, May.
    3. Narayanan Jayaraman & Ajay Khorana & Edward Nelling, 2002. "An Analysis of the Determinants and Shareholder Wealth Effects of Mutual Fund Mergers," Journal of Finance, American Finance Association, vol. 57(3), pages 1521-1551, June.
    4. Moreno, David & Marco, Paulina & Olmeda, Ignacio, 2006. "Self-organizing maps could improve the classification of Spanish mutual funds," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1039-1054, October.
    5. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
    6. Laura Fabregat-Aibar & Antonio Terceño & M. Glòria Barberà-Mariné, 2017. "Analysis of the survival capacity of mutual funds: a systematic review of the literature," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 13(4), pages 440-474, August.
    7. Chiang, W. -C. & Urban, T. L. & Baldridge, G. W., 1996. "A neural network approach to mutual fund net asset value forecasting," Omega, Elsevier, vol. 24(2), pages 205-215, April.
    8. Cogneau, Philippe & Hübner, Georges, 2015. "The prediction of fund failure through performance diagnostics," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 224-241.
    9. Martin Rohleder & Hendrik Scholz & Marco Wilkens, 2010. "Survivorship Bias and Mutual Fund Performance: Relevance, Significance, and Methodical Differences," Review of Finance, European Finance Association, vol. 15(2), pages 441-474.
    10. McLemore, Ping, 2019. "Do Mutual Funds Have Decreasing Returns to Scale? Evidence from Fund Mergers," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(4), pages 1683-1711, August.
    11. Xinge Zhao, 2005. "Exit Decisions in the U.S. Mutual Fund Industry," The Journal of Business, University of Chicago Press, vol. 78(4), pages 1365-1402, July.
    12. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    13. Indro, D. C. & Jiang, C. X. & Patuwo, B. E. & Zhang, G. P., 1999. "Predicting mutual fund performance using artificial neural networks," Omega, Elsevier, vol. 27(3), pages 373-380, June.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Zalewska, Anna (Ania) & Zhang, Yue, 2020. "Mutual funds' exits, financial crisis and Darwin," Journal of Corporate Finance, Elsevier, vol. 65(C).
    3. Chen, Hong-Yi & Chen, Hsuan-Chi & Lai, Christine W., 2021. "Internet search, fund flows, and fund performance," Journal of Banking & Finance, Elsevier, vol. 129(C).
    4. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
    5. Khorana, Ajay & Tufano, Peter & Wedge, Lei, 2007. "Board structure, mergers, and shareholder wealth: A study of the mutual fund industry," Journal of Financial Economics, Elsevier, vol. 85(2), pages 571-598, August.
    6. Namvar, Ethan & Phillips, Blake, 2013. "Commonalities in investment strategy and the determinants of performance in mutual fund mergers," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 625-635.
    7. Greg N. Gregoriou & Maher Kooli, 2017. "The profiles of merged hedge funds, funds of hedge funds, and CTA," Journal of Asset Management, Palgrave Macmillan, vol. 18(1), pages 49-63, January.
    8. Hung-Cheng Lai & Kuan-Min Wang, 2016. "Does Survivorship Bias of Mutual Funds Differ Between Liquidations and Mergers?," Eastern European Business and Economics Journal, Eastern European Business and Economics Studies Centre, vol. 2(4), pages 299-314.
    9. Renjie, Rex Wang & Verwijmeren, Patrick & Xia, Shuo, 2022. "Corporate governance benefits of mutual fund cooperation," IWH Discussion Papers 21/2022, Halle Institute for Economic Research (IWH).
    10. Bernhard Breloer & Hannah Lea Hühn & Hendrik Scholz, 2016. "Jensen alpha and market climate," Journal of Asset Management, Palgrave Macmillan, vol. 17(3), pages 195-214, May.
    11. Martin Rohleder & Hendrik Scholz & Marco Wilkens, 2018. "Success and failure on the corporate bond fund market," Journal of Asset Management, Palgrave Macmillan, vol. 19(6), pages 429-443, October.
    12. Ying-Fen Fu, 2014. "Individual Fund Manager Sentiment, Fund Performance and Performance Persistence," International Journal of Economics and Financial Issues, Econjournals, vol. 4(4), pages 870-885.
    13. Omneya Abdelsalam & Meryem Duygun & Juan Carlos Matallín-Sáez & Emili Tortosa-Ausina, 2014. "Is Ethical Money Sensitive to Past Returns? The Case of Portfolio Constraints and Persistence of Islamic and Socially Responsible Funds," Working Papers 2014/19, Economics Department, Universitat Jaume I, Castellón (Spain).
    14. Martin Rohleder & Dominik Schulte & Marco Wilkens, 2017. "Management of flow risk in mutual funds," Review of Quantitative Finance and Accounting, Springer, vol. 48(1), pages 31-56, January.
    15. Francesco Lisi, 2011. "Dicing with the market: randomized procedures for evaluation of mutual funds," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 163-172.
    16. Azadeh, A. & Ghaderi, S.F. & Anvari, M. & Saberi, M., 2007. "Performance assessment of electric power generations using an adaptive neural network algorithm," Energy Policy, Elsevier, vol. 35(6), pages 3155-3166, June.
    17. Juan Carlos Matallín-Sáez & Amparo Soler-Domínguez & Emili Tortosa-Ausina, 2016. "Ethical strategy focus and mutual fund management: performance and persistence," Working Papers 2016/01, Economics Department, Universitat Jaume I, Castellón (Spain).
    18. Anna (Ania) Zalewska, 2022. "Saving with Group or Individual Personal Pension Schemes: How Much Difference Does It Make?," Management Science, INFORMS, vol. 68(7), pages 5384-5402, July.
    19. Bryant, Lonnie L. & Liu, Hao-Chen, 2011. "Mutual fund industry management structure, risk and the impacts to shareholders," Global Finance Journal, Elsevier, vol. 22(2), pages 101-115.
    20. Agarwal, Vikas & Ma, Linlin, 2013. "Managerial multitasking in the mutual fund industry," CFR Working Papers 13-10, University of Cologne, Centre for Financial Research (CFR).

    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:gam:jmathe:v:9:y:2021:i:6:p:695-:d:522910. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.