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Multi-scale Cluster Hierarchy for Non-stationary Functional Signals of Mutual Fund Returns  

Kim, Dae-Lyong (동국대학교 경영학과)
Jung, Uk (동국대학교 경영학과)
Publication Information
Korean Management Science Review / v.24, no.2, 2007 , pp. 57-72 More about this Journal
Abstract
Many Applications of scientific research have coupled with functional data signal clustering techniques to discover novel characteristics that can be used for the diagnoses of several issues. In this article we present an interpretable multi-scale cluster hierarchy framework for clustering functional data using its multi-aspect frequency information. The suggested method focuses on how to effectively select transformed features/variables in unsupervised manner so that finally reduce the data dimension and achieve the multi-purposed clustering. Specially, we apply our suggested method to mutual fund returns and make superior-performing funds group based on different aspects such as global patterns, seasonal variations, levels of noise, and their combinations. To promise our method producing a quality cluster hierarchy, we give some empirical results under the simulation study and a set of real life data. This research will contribute to financial market analysis and flexibly fit to other research fields with clustering purposes.
Keywords
Mutual Fund Returns; Unsupervised Clustering; Non-stationary Functional Data; Wavelet; Multi-resolution Analysis;
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  • Reference
1 Ramsay, J., and B. Silverman, Functional Data Analysis, Springer Series in Statistics. Springer Verlag, 1997
2 Sharpe, W.F., 'Morning star's risk-adjusted ratings,' Financial Analysis Journal, (1998), pp.21-33
3 Vidakovic, B., Statistical Modeling by Wavelets, John Wiley & Sons, 1999
4 Hartigan, J.A. and Wong, M.A. 'A K-means clustering algorithm,' Applied Statistics, Vol.28, No.1(1978), pp.100-108
5 Donoho, D.L. and I.M. Johnstone, 'Adapting to unknown smoothness via wavelet shrinkage,' Journal of American Statistics Association, Vol.90(1995), pp.1200-1224   DOI
6 Jensen, M.C., 'The performance of mutual funds in the period 1945-1964,' Journal of Finance Vol.23, No.2(1968), pp.389-416   DOI   ScienceOn
7 Jeong, M.K., J.C. Lu, X. Huo, B. Vidakovic, and D. Chen, 'Wavelet-based data reduction techniques for fault detection and classification,' Technometrics, Vol.48, No.1(2006), pp.26-40   DOI   ScienceOn
8 Mallat, S.G., A Wavelet Tour of Signal Processing, Academic Press, San Diago,1989
9 Calinski, T. and J. Harabasz, 'A dendrite method for cluster analysis,' Communications in Statistics, Vol.3, No.1(1974), pp.1-27
10 Kaufman, L. and P. Rousseeuw, Finding Groups in Data：An Introduction to Cluster Analysis, New York：Wiley, 1990
11 Guyon, I. and A. Elissee, 'An introduction to variable and feature selection,' Journal of Machine Learning Research, Vol.3(2003), pp.1157-1182   DOI
12 Carreira-Perpinan, M., 'A review of dimension reduction techniques,' Technical report CS-96-09(1997), Department of Computer Science, University of Sheffield, UK]
13 Jung, U., M.K. Jeong, and J.C. Lu, 'Data reduction for multiple functional data with class information,' International Journal of Production Research, Vol.44, No14, 15(2006b), pp.2695-2710   DOI   ScienceOn
14 Benz, C., P.D. Teresa, and R. Kinnel, Mutual Funds-Morningstar, John Wiley & Sons, 2003
15 Duda, R.O., P.E. Hart, and D.G. Stork, Pattern Classification, 2nd edn., Wiley, New York, NY, 2001
16 Grogan, R. 'High speed stamping processimprovement thru force and displacement monitoring,' Technical report(2002), Helm Instrument Company, Maumee, OH
17 Krzanowski, W.J. and Y.T. Lai, 'A criterion for determining the number of groups in a data set using sum of squares clustering,' Biometrics, Vol.44, No.1(1988), pp.23-34   DOI   ScienceOn
18 Hartigan, J.A. Clustering Algorithms, New York, USA：John Wiley and Sons, 1975
19 Jin, J. and J. Shi, 'Automatic feature extraction of waveform signals for in-process diagnostic performance improvement,' Journal of Intelligent Manufacturing, Vol.12 (2001), pp.257-268   DOI
20 Sharpe, W.F., 'Mutual fund performance,' Journal of Business Vol.39, No.1(1966), pp. 119-138   DOI   ScienceOn
21 Kim, D.L., 'An empirical study of performance rating and distribution of mutual funds under the finite mixtures of normal distribution hypothesis,' Ph.D. dissertation, University of Nebraska, USA, 2003
22 Jung, U., M.K. Jeong, and J.C. Lu, 'A vertical-energy-thresholding procedure for data reduction with multiple complex curves,' IEEE Transactions on Systems, Man, and Cybernetics-Part B, Vol.36, No.5(2006a), pp.1128-1138   DOI   ScienceOn
23 Rousseeuw, P.J., 'Silhouettes：a graphical aid to the interpretations and validation of cluster analysis,' Journal of Computational and Applied Mathematics, Vol.20(1987), pp. 53-65   DOI   ScienceOn
24 Treynor, J.L., 'How to rate management of investment funds,' Harvard Business Review, Vol.43, No.1(1965), pp.63-75
25 Banfield, J.D. and A.E. Raftery, 'Modelbased Gaussian and non-gaussian clustering,' Biometrics, Vol.49(1993), pp.803-821   DOI   ScienceOn