Codification of Dendrograms Portfolio Based on Euclidean Distance Measure (A Comparison Between Different Methods of Hierarchical Clustering)

Subject Areas : Financial Knowledge of Securities Analysis

Hojatollah Sadeghi ¹ , Sharifeh Forooghi Dehnavi ²

1 - استادیار دانشکده اقتصاد، مدیریت و حسابداری، دانشگاه یزد، ایران
2 - کارشناس ارشد مدیریت بازرگانی (مالی)، دانشکده اقتصاد، مدیریت و حسابداری، دانشگاه یزد، ایران

Received: 2016-04-07 Accepted : 2016-07-05 Published : 2017-06-22

Keywords: Dendrogram, Euclidean Distance Measure, Hierarchical Clustering Analys, portfolio,

Abstract :

Today analysis of financial markets as a part of the capital market and its impact on development and portfolio design and investment strategy of each country has become an important and most critical issue. The aim of this study was to investigate how the connection and distribution of stocks related to 30 large companies index of Tehran Stock Exchange and the effects of relationship between clusters of related stocks to every industry. In this study, using a variety of methods of hierarchical clustering, structure, classification and hierarchy of the stocks in the year 1393 reviewed. The results showed that With a focus on each of the hierarchical clustering methods and their implementation on the target stocks, were identified different clusters of stocks due to the similarity and economic relationships and also the key clusters and the vital stocks in the desired set were obtained. The results indicate that the choice best hierarchical clustering algorithm for clustering stocks depends on the desired purpose of cluster analysis and consideration of the advantages and disadvantages of each method.

References:

*      Anderberg, M. R. (1973). Cluster analysis for applications. Monographs and textbooks on probability and mathematical statistics: Academic Press, Inc., New York.

*      Arabie, P., Baier, N. D., Critchley, C. F., & Keynes, M. (2006). Studies in Classification, Data Analysis, and Knowledge Organization: springer.

*      Bonanno, G., Lillo, F., & Mantegna, R. N. (2001). High-frequency cross-correlation in a set of stocks,Quantitative Finance, 1(1), 96-104.

*      Bouguettaya, A., Yu, Q., Liu, X., Zhou, X., & Song, A. (2015).Efficient agglomerative hierarchical clustering. Expert Systems with Applications, 42(5), 2785-2797.

*      Brida, J. G., Gómez, D. M., & Risso, W. A. (2009). Symbolic hierarchical analysis in currency markets: An application to contagion in currency crises. Expert Systems with Applications, 36(4), 7721-7728.

*      Brida, J. G., & Risso, W. A. (2010). Dynamics and structure of the 30 largest North American companies. Computational Economics, 35(1), 85-99.

*      Brida, J. G., & Risso, W. A. (2007). Dynamics and structure of the main Italian companies. International Journal of Modern Physics C, 18(11), 1783-1793.

*      Cha, S.-H. (2008). Taxonomy of nominal type histogram distance measures. City, 1(2), 1.

*      Gan, G., Ma, C., & Wu, J. (2007). Data clustering: theory, algorithms, and applications (Vol. 20): Siam.

*      Garas, A., & Argyrakis, P. (2007). Correlation study of the Athens stock exchange. Physica A: Statistical Mechanics and its Applications, 380, 399-410.

*      Han, J., & Kamber, M. (2001). Data mining: concepts and techniques: Morgan Kaufmann San Francisco, Calif, USA.

*      Hastie, T. J., Tibshirani, R. J., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction: Springer.

*      Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review.ACM computing surveys (CSUR), 31(3), 264-323.

*      Kocakaplan, Y., Deviren, B., & Keskin, M. (2012). Hierarchical structures of correlations networks among Turkey’s exports and imports by currencies. Physica A: Statistical Mechanics and its Applications, 391(24), 6509-6518.

*      Kocakaplan, Y., Doğan, Ş., Deviren, B., & Keskin, M. (2013). Correlations, hierarchies and networks of the world’s automotive companies. Physica A: Statistical Mechanics and its Applications, 392(12), 2736-2774.

*      Kogan, J., Nicholas, C., & Teboulle, M. (2006). Grouping multidimensional data: Springer.

*      Lai, K. K., Yu, L., Wang, S., & Zhou, C. (2006). A double-stage genetic optimization algorithm for portfolio selection. Paper presented at the Neural Information Processing.

*      Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11(1), 193-197.

*      Markowitz, H. (1952). Portfolio selection. The journal of finance, 7(1), 77-91.

*      Mizuno, T., Takayasu, H., & Takayasu, M. (2006). Correlation networks among currencies. Physica A: Statistical Mechanics and its Applications, 364, 336-342.

*      Paulus, M., & Kristoufek, L. (2015). Worldwide clustering of the corruption perception. Physica A: Statistical Mechanics and its Applications, 428, 351-358.

*      Rea, A., & Rea, W. (2014). Visualization of a stock market correlation matrix. Physica A: Statistical Mechanics and its Applications, 400, 109-123.

*      Simon, H. A. (1991). The architecture of complexity: Springer.

*      Tabak, B. M., Serra, T. R., & Cajueiro, D. O. (2010). Topological properties of stock market networks: The case of Brazil. Physica A: Statistical Mechanics and its Applications, 389(16), 3240-3249.

*      Teh, B. K., Goo, Y. W., Lian, T. W., Ong, W. G., Choi, W. T., Damodaran, M., & Cheong, S. A. (2015). The Chinese Correction of February 2007: How financial hierarchies change in a market crash. Physica A: Statistical Mechanics and its Applications, 424, 225-241.

*      Ulusoy, T., Keskin, M., Shirvani, A., Deviren, B., Kantar, E., & Dönmez, C. Ç. (2012). Complexity of major UK companies between 2006 and 2010: Hierarchical structure method approach. Physica A: Statistical Mechanics and its Applications, 391(21), 5121-5131.

*      Wang, G.-J., & Xie, C. (2015). Correlation structure and dynamics of international real estate securities markets: A network perspective. Physica A: Statistical Mechanics and its Applications, 424, 176-193.

*      Yang, R., Li, X., & Zhang, T. (2014). Analysis of linkage effects among industry sectors in China’s stock market before and after the financial crisis. Physica A: Statistical Mechanics and its Applications, 411, 12-20.

*      Zhang, D., & Zhou, L. (2004). Discovering golden nuggets: data mining in financial application. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 34(4), 513-522