The Behaviour of Non-surviving Spanish Funds According to Their Investment Objectives

  • Antonio Terceño
  • M. Gloria Barberà-Mariné
  • Laura Fabregat-Aibar
  • Maraia Teresa Sorrosal-Forradellas
Chapter
First Online:
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 125)

Abstract

The main purpose of this paper is to determine if the characteristics which define the non-surviving funds are different according to their investment objectives. In Spanish market, the Spanish National Securities Market Commission (CNMV) classifies the mutual funds according to the type of assets in which each fund invests to form a portfolio. Thus, the investor could choose which fund is suitable based on the performance and the risk that he wants to assume. In this paper, Self-Organizing Maps (SOM) are used to cluster mutual funds that disappeared in 2013, 2014 and 2015, based on the variables that define its survival capacity and, as a result, to analyse if these variables, take similar values for all of them or, different depending on the funds’ investment objectives.

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References

  1. 1.
    Andreu, L., Sarto, J.L.: Financial consequences of mutual fund mergers. Eur. J. Finance 22(7), 529–550 (2016)CrossRefGoogle Scholar
  2. 2.
    Asebedo, G., Grable, J.: Predicting mutual fund over-performance over a nine-year period. J. Financial Couns. Plan. 15(1), 1–11 (2004)Google Scholar
  3. 3.
    Blake, D., Blake, D., Timmermann, A., Timmermann, A.: Mutual fund performance: evidence from the UK. Eur. Finance Rev. 2(1), 57–77 (1998)CrossRefMATHGoogle Scholar
  4. 4.
    Boubakri, N., Karoui, A., Kooli, M.: Performance and Survival of Mutual Fund Mergers: Evidence from Frequent and Infrequent Acquirers. Working Paper (2012)Google Scholar
  5. 5.
    Brown, S., Goetzmann, W.: Performance persistence. J. Finance 50(2), 679–698 (1995)CrossRefGoogle Scholar
  6. 6.
    Cameron, C., Hall, A.D.: A survival analysis of Australian equity mutual funds. Aust. J. Manag. 28(2), 209–226 (2003)CrossRefGoogle Scholar
  7. 7.
    Carhart, M.M.: On persistence in mutual fund performance. J. Finance 52, 57–82 (1997)CrossRefGoogle Scholar
  8. 8.
    Carhart, M.M., Carpenter, J.N., Lynch, A.W., Musto, D.K.: Mutual fund survivorship. Rev. Financial Stud. 15(5), 1439–1463 (2002)CrossRefGoogle Scholar
  9. 9.
    Carpenter, J.N., Lynch, A.W.: Survivorship bias and attrition effects in measures of performance persistence. J. Financial Econ. 54(3), 337–374 (1999)CrossRefGoogle Scholar
  10. 10.
    Chen, N., Ribeiro, B., Vieira, A., Chen, A.: Clustering and visualization of bankruptcy trajectory using self-organizing map. Expert Syst. Appl. 40, 385–393 (2013)CrossRefGoogle Scholar
  11. 11.
    Cogneau, P., Hübner, G.: The prediction of fund failure through performance diagnostics. J. Bank. Finance 50, 224–241 (2015)CrossRefGoogle Scholar
  12. 12.
    Deboeck, G., Kohonen, T. (eds.): Visual explorations in finance: with self-organizing maps. Springer Science & Business Media (2013)Google Scholar
  13. 13.
    Du Jardin, P., Séverin, E.: Predicting corporate bankruptcy using a self-organizing map: an empirical study to improve the forecasting horizon of a financial failure model. Decis. Support Syst. 51, 701–711 (2011)CrossRefGoogle Scholar
  14. 14.
    Elton, E.J., Gruber, Martin J.: Survivorship bias and mutual fund performance. Rev. Financial Stud. 9(4), 1097–1120 (1996)CrossRefGoogle Scholar
  15. 15.
    Filip, D.: Survivorship bias and performance of mutual funds in Hungary. Period. Polytech. Soc. Manag. Sci. 22(1), 47–56 (2014)CrossRefGoogle Scholar
  16. 16.
    Fioramanti, M.: Predicting debt crises using artificial neural networks: a comparative approach. J. Financial Stab. 4, 149–164 (2008)CrossRefGoogle Scholar
  17. 17.
    Jayaraman, N., Khorana, A., Nelling, E.: An analysis of the determinants and shareholder wealth effects of mutual fund mergers. J. Finance 57(3), 1521–1551 (2002)CrossRefGoogle Scholar
  18. 18.
    Kohonen, T.: Self-organization and Associative Memory. Springer, Berlin (1989)CrossRefMATHGoogle Scholar
  19. 19.
    Lunde, A., Timmermann, A., Blake, D.: The hazards of mutual fund underperformance: a Cox regression analysis. J. Empir. Finance 6(2), 121–152 (1999)CrossRefGoogle Scholar
  20. 20.
    Moreno, D., Marco, P., Olmeda, I.: Self-organizing maps could improve the classification of Spanish mutual funds. Eur. J. Oper. Res. 174(2), 1039–1054 (2006)CrossRefMATHGoogle Scholar
  21. 21.
    Namvar, E., Phillips, B.: Commonalities in investment strategy and the determinants of performance in mutual fund mergers. J. Bank. Finance 37(2), 625–635 (2013)CrossRefGoogle Scholar
  22. 22.
    Rohleder, M., Scholz, H., Wilkens, M.: Survivorship bias and mutual fund performance: relevance, significance, and methodical differences. Rev. Finance 15(2), 441–474 (2011)CrossRefGoogle Scholar
  23. 23.
    Sarlin, P., Marghescu, D.: Visual predictions of currency crisis using self-organizing maps. Intell. Syst. Account. Finance Manag. 18, 15–38 (2011)CrossRefGoogle Scholar
  24. 24.
    Ter Horst, J.R., Nijman, T.E., Verbeek, M.: Eliminating look-ahead bias in evaluating persistence in mutual fund performance. J. Empir. Finance 8(4), 345–373 (2001)CrossRefGoogle Scholar
  25. 25.
    Terceño, A., Fabregat-Aibar, L., Sorrosal-Forradellas, M.T., Barberà-Mariné, M.G.: Mutual funds survival in Spain using self-organizing maps. In: Modeling and Simulation in Engineering, Economics and Management, pp. 61–68. Springer (2016)Google Scholar
  26. 26.
    Zhao, X.: Exit decisions in the U.S. mutual fund industry. J. Bus. 78(4), 1365–1402 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Antonio Terceño
    • 1
  • M. Gloria Barberà-Mariné
    • 1
  • Laura Fabregat-Aibar
    • 1
  • Maraia Teresa Sorrosal-Forradellas
    • 1
  1. 1.Department of Business ManagementUniversity Rovira i Virgili, Campus BellissensReusSpain

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