Klassifizierung von Hedge-Fonds durch das k-means Clustering von Self-Organizing Maps: eine renditebasierte Analyse zur Selbsteinstufungsgüte und Stiländerungsproblematik

Deetz, Marcus and Poddig, Thorsten and Varmaz, Armin (2009): Klassifizierung von Hedge-Fonds durch das k-means Clustering von Self-Organizing Maps: eine renditebasierte Analyse zur Selbsteinstufungsgüte und Stiländerungsproblematik.

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.

• Item Type: MPRA Paper
• English Title: Classifying Hedge Funds using k-means Clustering of Self-Organizing Maps: a return-based analysis of misclassification and the problem of style creep
• Language: German
• Keywords: Self-Organizing Maps; Clustering; Klassifzierung; Hedge-Fonds; Style Creep
• Subjects: G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions; G - Financial Economics > G2 - Financial Institutions and Services > G23 - Non-bank Financial Institutions ; Financial Instruments ; Institutional Investors; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C81 - Methodology for Collecting, Estimating, and Organizing Microeconomic Data ; Data Access

References

Ackermann, C., McEnally, R. & Ravenscraft, D. (1999), `The performance of hedge funds: Risk, return, and incentives', Journal of Finance 54, 833-874.

Agarwal, V. & Naik, N. (2000), `On taking the 'alternative' route: risks, rewards style and performance persistence of hedge funds', Journal of Alternative Investments 2, 6-23.

Amin, G. & Kat, H. (2003), `Welcome to the dark side: Hedge fund attrition and survivorship bias 1994-2001', Journal of Alternative Investments 6(2), 57-73.

Baghai-Wadji, R., El-Berry, R., Klocker, S. & Schwaiger, M. (2006), `Changing investment styles: Style creep and style gaming in the hedge fund industry', Journal of Intelligent Systems in Accounting, Finance and Management 14, 157-177.

Bares, P., R., G. & Gyger, S. (2001), Style consistence and survival probability in the hedge fonds industry, Technical report, Swiss Institute of Banking.

Bezdek, J. (1998), `Some new indexes of cluster validity', IEEE Transactions on Systems, Man abd Cybernetics B 28, 301-315.

Blackmore, J. & Miikkulainen, R. (1993), Incremental grid growing: Encoding high-dimensional structure into a two-dimensional feature map, in `Proceedings of ICNN'93; IEEE International Conference on Neural Networks', Vol. I, Piscataway, NJ, 450-455.

Bookstaber, R. (2003), `Hedge fund existential', Financial Analysts Journal 59(5), 19-23.

Bretzler, M. & Rudolph, D. (2004), Hedge Fonds und Alternative Investments, 1 edn, Frankfurt School Verlag, Frankfurt am Main.

Brown, S. & Goetzmann, W. (1997), `Mutual fund styles', Journal of Financial Economics 43(3), 373-399.

Brown, S. & Goetzmann, W. (2003), `Hedge funds with style', Journal of Portfolio Management 29, 101-112.

Das, N. & Das, R. (2005), Hedge fund classifcation technique using self organizing feature map neural network, Technical report, Financial Management Association International.

Davies, D. & Bouldin, D. (1979), `A cluster seperation measure', IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-1, 224-227.

Deboeck, G. (1998), Picking mutual funds with self-organizing maps, in G. Deboeck & T. Kohonen, eds, `Visiual Explorations in Finance with Self-Organizing Maps', Springer, London, 39-58.

Eling, M. (2006), `Hedgefonds-Strategien - Systematisierung und Überblick', BankArchiv (ÖBA)54(8), 543-555.

Fritzke, B. (1994), `Growing cell structures - a self organizing network for unsupervised and supervised learning', Neural Network 7, 1441-1460.

Fung, W. & Hsieh, D. (1997), `Empirical characteristics of dynamic trading strategies: The case of hedge funds', Review of Financial Studies 10(2), 275-302.

Fung, W. & Hsieh, D. (2001), `The risk in hedge fund strategies: Theory and evidence from trend followers', Review of Financial Studies 14(2), 313-341.

Fung, W. & Hsieh, D. (2002), `Risk in Fixed-income hedge fund styles', Journal of Fixed Income pp. 1-22.

Fung, W. & Hsieh, D. (2004), `Extracting portable alphas from equity long/short hedge funds',Journal of Investment Management 2(4), 1-19.

Indijc, D. & Heen, A. (2003), `Aima survey of hedge fund classifcation practice', AIMA Journal.

Indro, C., Jiang, C., Hu, M. & Wayne, L. (1998), `Mutual fund performance: A question of style', Journal of Investing 7(2), 46-53.

Ineichen, A. (2003), Absolute Returns: The Risk and Opportunities of Hedge Fund Investing, Wiley, New York.

Kaiser, D. (2004), Hedgefonds: Entmystifzierung einer Anlageklasse; Strukturen - Chancen - Risiken, 1 edn, Gabler, Wiesbaden.

Kaski, S. & Lagus, K. (1996), Comparing self-organizing maps, in von der Malsburg, C. & von Seelen, W. & Vorbrügge, J. & Sendho, B., ed., `Proceedings of ICANN'96, International Conference on Artificial Neural Networks', 809-814.

Kerling, M. (1997), Moderne Konzepte in der Finanzanalyse. Markthypothesen Renditegenerierungsprozesse und Modellierungswerkzeuge, Uhlenbruch.

Kim, M., Shukla, R. & Tomas, M. (2000), `Mutual fund objective misclassifcation', Journal of Economics and Business 52, 309-323.

Kohonen, T. (1981), Automatic formations of topological maps in a self-organizing system, in E. Oja & O. Simula, eds, `Proceedings of the 2nd Scandinavian Conference on Image Analysis', 214-220.

Kohonen, T. (1982a), Clustering, taxonomy and topological maps of patterns, in `Proceedings of the sixth International Conference on Pattern Recognition', Silver Spring, MD (IEEE Computer Society), 114-118.

Kohonen, T. (1982b), `Self-organized formation of toplogically correct feature maps', Biological Cybernetics 43, 59-69.

Kohonen, T. (1982c), A simple paradigmen for the self-organisation of structured feature maps, competition and cooperation in neural nets, in S. Amari & M. Arbib, eds, `Lecture Notes in Biomathematics', Vol. 45, Berlin, 248-266.

Kohonen, T. (1997), Self-Organizing Maps, Springer, Berlin.

Kohonen, T., Hyninnen, J., Kangas, J. & Laaksonen, J. (1995), Som pak the self-origanizing map program package, Technical report, Helsinki Univeristy of Technology.

MacQueen, J. (1967), Some methods for classifcation and analysis of multivariate observations, in `Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability', Vol. 1, 281{297.

Maillet, B. & Rousset, P. (2001), Classifying hedge funds with kohonen maps: A first attempt, Technical report, Social Science Research Network (SSRN).

Mangiameli, P., Chen, S. & West, D. (1996), `A comparison of som neural network and hierarchical clustering methods', European Journal of Operational Research 93, 402-417.

Merkl, D. & Rauber, A. (1997), Alternative ways for cluster visualization in self-organizing maps. in: Proceedings of wsom'97, workshop on self-organizing maps, Technical report, Helsinki University of Technology.

Milligan, G. & Cooper, M. (1985), `An examination of procedures for determining the number of clusters on a data set', Psychometrika 50(2), 159-179.

Mitchell, M. & Pulvino, T. (2001), `Characterisitics of risk and return in risk arbitrage', Journal of Finance 56(6), 2135-2175.

Moutarde, F. & Ultsch, A. (2005), U*f clustering: A new performant "cluster-mining"method based on segmentation of self-organizing maps, in `5th workshop on self-organizing maps', pp. 25-32.

Ohlms, C. (2006), Aktives Investmentportfolio-Management: Optimierung von Portfolios aus derivatebasierten dynamischen Investmentstrategien, PhD thesis, Technische Universität Darmstadt, Wiesbaden.

Poddig, T. & Sidorovitch, I. (2001), Künstlich Neuronale Netze: �Überblick, Einsatzmöglichkeiten und Anwendungsprobleme, in H. Hippner, U. K�usters, M. Meyer & K. Wilde, eds, `Handbuch Data Mining im Marketing', Vieweg, 363-402.

Poddig, T. & Viebig, J. (2006), `Hedgefonds-Strategien und Asset-based Style-Faktoren', Kredit und Kapital 2, 281-316.

Schäfer, D. (2004), Hedge-Fonds - eine gute Anlageform?, Wochenbericht 32, Deutsches Institut für Wirtschaftsforschung.

Seiler, K. (2008), Phasenmodelle und Investmentstilanalyse von Hedge- und Investmentfonds, PhDthesis, Universität Bremen.

Sharpe, W. (1992), `Asset allocation: Management style and performance measurement', Journal of Portfolio Management 18(2), 7-19.

Signer, A. (2005), Generieren Hedge Funds einen Mehrwert?, in H. Dichtl, J. Kleeberg & C. Schlenger, eds, `Handbuch Hedge Funds; Chancen, Risiken und Einsatz in der Asset Allocation', Uhlenbruch, Bad Soden/Ts., 211-232.

Ultsch, A. (1993), Self-organizing neural networks for visualiziation and classification, in O. Opitz, B. Lausen & R. Klar, eds, `Information and Classification: Concepts, Methods and Applications', Springer, Berlin, 307-313.

Ultsch, A. (2003), U*-matrix: A tool to visualize clusters in high dimensional data, Technical Report 36, University of Marburg.

Ultsch, A. & Vetter, C. (1994), Self-organizing-maps versus statistical clustering methods: A benchmark, Report 0994, FG Neuroinformatik und K�unstliche Intelligenz, Universität Marburg.

Vesanto, J. & Alhoniemi, E. (2000), `Clustering of self-organizing map', IEEE Transactions on Neural Networks 11(3), 586-600.

Vesanto, J., Himberg, J., Alhoniemi, E. & Parhankangas, J. (2000), Som toolbox for matlab 5, Technical Report Report A57, Helsinki Univeristy of Technology.

Zimmermann, H. (1994), Neuronale Netze als Entscheidungskalkül, in H. Rehkugler & H. Zimmermann, eds, `Neuronale Netze in der Ökonomie', Frankfurt School Verlag, 1-87.