Pembentukan Portofolio Saham Menggunakan Klastering Time Series K-Medoid dengan Ukuran Jarak Dynamic Time Warping
DOI:
https://doi.org/10.34123/jurnalasks.v13i2.295Keywords:
klastering, k-medoids, portofolio saham, estimasi robust, kinerja portofolioAbstract
Pada penelitian ini akan disajikan pembentukan portofolio saham dengan preprocessing data menggunakan klastering time series dengan ukuran jarak Dynamic Time Warping (DTW). Pertama-tama saham-saham dikelompokkan ke dalam beberapa klaster menggunakan klastering time series Partitioning Around Medoids (PAM) berdasarkan ukuran jarak DTW. Setelah proses klastering, saham dipilih untuk mewakili masing-masing klaster untuk membangun portofolio optimum. Saham yang dipilih dari masing-masing klaster merupakan saham yang memiliki Sharpe ratio tertinggi. Portofolio optimal ditentukan dengan menggunakan tiga model portofolio, yaitu: model portofolio MV klasik, model portofolio MV robust FMCD dan model portofolio robust S. Dengan menggunakan prosedur ini, dapat diperoleh portofolio optimum secara efisien bila ada banyak saham yang terlibat dalam proses pembentukan portofolio. Untuk mengukur kinerja portofolio yang terbentuk digunakan Sharpe ratio. Hasil kajian empiris menunjukkan bahwa kinerja portofolio yang dihasilkan dengan menggunakan klastering time series PAM dengan ukuran disimilaritas jarak DWT yang dikombinasikan dengan model portofolio MV klasik mengungguli kinerja portofolio yang dihasilkan kombinasi dengan model yang lain.
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References
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Berndt, Donald J. dan Clifford, James 1994. Using Dynamic Time Warping to Find Patterns in Time Series. In Workshop on Knowledge Discovery in Databases, 359–370.
Ceria, Sabastian dan Stubbs, Robert A. 2006. Incorporating estimation errors into portfolio selection: Robust portfolio construction. Journal of Asset Management, Vol. 7(2), 109–127.
Chen, Liang H. dan Huang, Lindsay. 2009. Portfolio optimization of equity mutual funds with fuzzy return rates and risks. Expert Systems with Applications, Vol. 36, 3720–3727.
Chopra, Vijay K. dan Ziemba, William T. 1993. The Effect of Errors in Means, Variances, and Covariances on Optimal Portfolio Choice. Journal of Portfolio Management, Vol. 19(2), 6–11.
Davies, P. L. 1987. Asymptotic Behaviour of S-Estimates of Multivariate Location Parameters and Dispersion Matrices. The Annals of Statistics, Vol. 15(3), 1269–1292.
DeMiguel, Victor dan Nogales, Francisco J. 2009. Portfolio Selection With Robust Estimation. Operations Research, Vol. 57(3), 560–577.
Guan, He S. dan Jiang, Qing S. 2007. Cluster financial time series for portfolio. Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2007, Vol. 2, 851–856.
Gubu, L. Rosadi, D., dan Abdurakhman 2019. Classical portfolio selection with cluster analysis: Comparison between hierarchical complete linkage and Ward algorithm, in International Conference on Mathematics and its Applications 2019, AIP Conference Proceedings 2192, 090004-1–0960004-7.
Hardin, Johana S. 2000. Multivariate Outlier Detection and Robust Clustering with Minimum Covariance Determinant Estimation and S-estimation. Disertasi, University of California, California.
Kaufman, Leonard dan Rousseeuw, Peter J. 1990. Finding Groups in Data: An Introduction, John Wiley & Sons, Hoboken, New Jersey.
Lauprete, Geoffrey J. 2001. Portfolio Risk Minimization Under Departures from Normality. Disertasi, Massachusetts Institue of Technology.
Long, N. C., Wisitpongphan, N., Meesad, P., dan Unger, H. 2014. Clustering stock data for multi-objective portfolio optimization. International Journal of Computational Intelligence and Applications, Vol. 13(2), 1–13.
Lopuhaa, Hendrik P. 1989. On the Relation between S-Estimators and M-Estimators of Multivariate Location and Covariance. The Annals of Statistics, Vol. 17(4), 1662–1683.
Markowitz, Harry. 1952. Portfolio Selection. The Journal of Finance, Vol. 7(1), 77–91.
Maronna, R. A., Martin, R. D. dan Yohai, V. J. 2006. Robust Statistics: Theory and Methods. John Wiley & Sons, Ltd.
Montero, Pablo dan Vilar, Jose A. 2014. TSclust: an R Package for Time Series Clustering. Journal of Statistical Software, Vol. 62(1), 1–43.
Nanda, S. R., Mahanty, B., dan Tiwari, M. K. 2010. Clustering Indian Stock Market Data for Portfolio Management. Expert Systems with Applications, Vol. 37(12), 8793–8798.
Rousseeuw, Peter J. dan Van Driessen, Katrien. 1999. Fast Algorithm For Minimum Covariance Determinant Estimator. Technometrics, Vol. 41(3), 212–223.
Rousseeuw, Peter J. dan Yohai, Victor. 1984. Robust Regression By Means of S estimators, Lecture Notes in Statistics: Robust and Nonlinear Time Series Analysis, Vol. 26, 256–272.
Sankoff, David dan Kruskal, Joseph 1983. Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison, Addison Wesley.
Sharpe, William F. 1994. The Sharpe Ratio, The Jornal of Portfolio Management, Vol. 21, 49–58.
Supandi, Epha D. 2017. Pengembangan Model Portofolio Mean-Variance Melalui Metode Estimasi Robust dan Optimasi Robust. Disertasi, Departemen Matematika FMIPA Universitas Gadjah Mada, Yogyakarta
Tola, V., Lillo, F., Gallegati, M., dan Mantegna, R. N. 2008. Cluster analysis for portfolio optimization. Journal of Economic Dynamics and Control, Vol. 32(1), 235–258.
Vaz-de Melo, Beatriz dan Camara, Ricardo P. 2005. Robust Multivariate Modeling in Finance, International Journal of Managerial Finance, Vol. 1(2), 95–107.
Welsch, Roy E. dan Zhou, Xinfeng. 2007. Application of robust statistics to asset allocation models. REVSTAT–Statistical Journal, Vol. 5(1), 97–114.
Winston, Wayne L. dan Goldberg, Jeffrey B. 2004. Operations Research: Applications and Algorithms, Fourth Edition, Thomson Learning Inc.
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Published
2021-12-31
How to Cite
Gubu, L., Rosadi, D., & Abdurakhman, A. (2021). Pembentukan Portofolio Saham Menggunakan Klastering Time Series K-Medoid dengan Ukuran Jarak Dynamic Time Warping. Jurnal Aplikasi Statistika & Komputasi Statistik, 13(2), 35–46. https://doi.org/10.34123/jurnalasks.v13i2.295
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Statistika Ekonomi
