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Klassifizierung von Hedge-Fonds durch das k-means Clustering von Self-Organizing Maps: eine renditebasierte Analyse zur Selbsteinstufungsgüte und Stiländerungsproblematik
[Classifying Hedge Funds using k-means Clustering of Self-Organizing Maps: a return-based analysis of misclassification and the problem of style creep]

Author

Listed:
  • Deetz, Marcus
  • Poddig, Thorsten
  • Varmaz, Armin

Abstract

Through an implementation of the 2-level-approach due to Vesanto & Alhoniemi (2000), this paper addresses a number of problems typically seen when visualized interpretation of Self Organizing Maps (SOM) are applied to derive a systematic classification system in the hedge fund literature. Normally, a trained SOM does not result in an exact depiction of the detected structures of the input data, and is therefore challenging for visual interpretations. The 2-level-approach overcomes this problem and assures a consistent clustering of neighboring output units, and therefore an objective classification scheme. Through an empirical application, such an objective classification is derived. Building on this, further analyses concerning the misclassification and style creep problems are conducted. Within the ten-year sample period (31.01.1999 to 31.12.2008), which comprises 2789 hedge funds, organized in eleven strategies, six classes can be identified. This six-class taxonomy is fairly robust to different sub-sample periods, topologies and data-samples. According to the classification system applied here, it is shown that most of the analyzed hedge funds are inconsistent in their self-declared strategies. Furthermore, evidence of undisclosed trading style changes over time is identified – specifically, it is shown that misclassified hedge funds are more likely to change their trading style.

Suggested Citation

  • Deetz, Marcus & Poddig, Thorsten & Varmaz, Armin, 2009. "Klassifizierung von Hedge-Fonds durch das k-means Clustering von Self-Organizing Maps: eine renditebasierte Analyse zur Selbsteinstufungsgüte und Stiländerungsproblematik
    [Classifying Hedge Funds u
    ," MPRA Paper 16939, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:16939
    as

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    File URL: https://mpra.ub.uni-muenchen.de/16939/1/MPRA_paper_16939.pdf
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    References listed on IDEAS

    as
    1. Mark Mitchell, 2001. "Characteristics of Risk and Return in Risk Arbitrage," Journal of Finance, American Finance Association, vol. 56(6), pages 2135-2175, December.
    2. Stephen Brown & William Goetzmann, 2001. "Hedge Funds With Style," Yale School of Management Working Papers ysm21, Yale School of Management, revised 01 Apr 2008.
    3. Mangiameli, Paul & Chen, Shaw K. & West, David, 1996. "A comparison of SOM neural network and hierarchical clustering methods," European Journal of Operational Research, Elsevier, vol. 93(2), pages 402-417, September.
    4. Brown, Stephen J. & Goetzmann, William N., 1997. "Mutual fund styles," Journal of Financial Economics, Elsevier, vol. 43(3), pages 373-399, March.
    5. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    6. Bertrand Maillet & Patrick Rousset, 2003. "Classifying Hedge Funds using Kohonen Map," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00308996, HAL.
    7. Fung, William & Hsieh, David A, 2001. "The Risk in Hedge Fund Strategies: Theory and Evidence from Trend Followers," Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 313-341.
    8. Carl Ackermann & Richard McEnally & David Ravenscraft, 1999. "The Performance of Hedge Funds: Risk, Return, and Incentives," Journal of Finance, American Finance Association, vol. 54(3), pages 833-874, June.
    9. Ohlms, Christian, 2006. "Aktives Investmentportfolio-Management : Opitmierung von Portfolios aus derivatebasierten dynamischen Investmentstrategien," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 25428, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    10. Kim, Moon & Shukla, Ravi & Tomas, Michael, 2000. "Mutual fund objective misclassification," Journal of Economics and Business, Elsevier, vol. 52(4), pages 309-323.
    11. Fung, William & Hsieh, David A, 1997. "Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds," Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 275-302.
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    More about this item

    Keywords

    Self-Organizing Maps; Clustering; Klassifzierung; Hedge-Fonds; Style Creep;

    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
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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