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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]

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Author Info

  • 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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 16939.

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Date of creation: 25 Aug 2009
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Handle: RePEc:pra:mprapa:16939

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Related research

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

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  1. 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, 06.
  2. Kim, Moon & Shukla, Ravi & Tomas, Michael, 2000. "Mutual fund objective misclassification," Journal of Economics and Business, Elsevier, vol. 52(4), pages 309-323.
  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. Mark Mitchell, 2001. "Characteristics of Risk and Return in Risk Arbitrage," Journal of Finance, American Finance Association, vol. 56(6), pages 2135-2175, December.
  5. Brown, Stephen J. & Goetzmann, William N., 1997. "Mutual fund styles," Journal of Financial Economics, Elsevier, vol. 43(3), pages 373-399, March.
  6. 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 313-41.
  7. Stephen J. Brown & William N. Goetzmann, 2001. "Hedge Funds With Style," NBER Working Papers 8173, National Bureau of Economic Research, Inc.
  8. 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).
  9. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer, vol. 50(2), pages 159-179, June.
  10. 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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