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Dynamics of Cluster Structures in Stock Market Networks

Author

Listed:
  • Kocheturov, A.

    (Center for Applied Optimization, University of Florida, USA)

  • Batsyn, M.

    (NRU HSE, Nizhny Novgorod, Russia)

  • Pardalos, P.

    (NRU HSE, Nizhny Novgorod, Russia
    Center for Applied Optimization, University of Florida, USA)

Abstract

In recent 15 years network analysis has been actively applied for studying financial markets. In this paper we present a network-based analysis of stock markets of USA and Sweden. We extract and study special cluster structures of networks built from correlation matrices of stock returns for these stock markets. A cluster structure of a network is extracted by solving the p-median problem which chooses p central stocks (medians) and partitions all stocks into p clusters around these medians - centers. The objective function maximizes the sum of correlations between each stock and the median of its cluster. The obtained cluster structure is represented by an undirected disconnected weighted graph, which components are star-graphs with one central vertex (median) and several leaf vertices connected only with the median by weighted edges. Our main observation is that in non-crisis periods cluster structures of stock market networks change more chaotically, while during crises they demonstrate more stable behavior and smaller changes. Thus an increase in stability of the cluster structure for a stock market network obtained by means of the p-median problem solution can serve as an indicator of a coming crisis.

Suggested Citation

  • Kocheturov, A. & Batsyn, M. & Pardalos, P., 2015. "Dynamics of Cluster Structures in Stock Market Networks," Journal of the New Economic Association, New Economic Association, vol. 28(4), pages 12-30.
  • Handle: RePEc:nea:journl:y:2015:i:28:p:12-30
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    1. Onnela, J.-P. & Chakraborti, A. & Kaski, K. & Kertész, J., 2003. "Dynamic asset trees and Black Monday," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 247-252.
    2. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    3. Papadimitriou, Theophilos & Gogas, Periklis & Tabak, Benjamin M., 2013. "Complex networks and banking systems supervision," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4429-4434.
    4. Tabak, Benjamin M. & Takami, Marcelo & Rocha, Jadson M.C. & Cajueiro, Daniel O. & Souza, Sergio R.S., 2014. "Directed clustering coefficient as a measure of systemic risk in complex banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 211-216.
    5. Jung, Woo-Sung & Chae, Seungbyung & Yang, Jae-Suk & Moon, Hie-Tae, 2006. "Characteristics of the Korean stock market correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 361(1), pages 263-271.
    6. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang, 2009. "A network analysis of the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2956-2964.
    7. Dror Y. Kenett & Yoash Shapira & Asaf Madi & Sharron Bransburg-Zabary & Gitit Gur-Gershgoren & Eshel Ben-Jacob, 2010. "Dynamics of Stock Market Correlations," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 4(3), pages 330-340, November.
    8. Çukur, Sadik & Eryiğit, Mehmet & Eryiğit, Resul, 2007. "Cross correlations in an emerging market financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 555-564.
    9. Fuad Aleskerov & Boris Goldengorin & Panos M. Pardalos (ed.), 2014. "Clusters, Orders, and Trees: Methods and Applications," Springer Optimization and Its Applications, Springer, edition 127, number 978-1-4939-0742-7, September.
    10. Boris Goldengorin & Anton Kocheturov & Panos M. Pardalos, 2014. "A Pseudo-Boolean Approach to the Market Graph Analysis by Means of the p-Median Model," Springer Optimization and Its Applications, in: Fuad Aleskerov & Boris Goldengorin & Panos M. Pardalos (ed.), Clusters, Orders, and Trees: Methods and Applications, edition 127, pages 77-89, Springer.
    11. Giuseppe Buccheri & Stefano Marmi & Rosario N. Mantegna, 2013. "Evolution of correlation structure of industrial indices of US equity markets," Papers 1306.4769, arXiv.org.
    12. Cajueiro, Daniel O. & Tabak, Benjamin M., 2008. "The role of banks in the Brazilian interbank market: Does bank type matter?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6825-6836.
    13. Tabak, Benjamin M. & Serra, Thiago R. & Cajueiro, Daniel O., 2010. "Topological properties of stock market networks: The case of Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3240-3249.
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    More about this item

    Keywords

    dynamics; cluster structure; stock markets; p-median problem; clustering; crisis; network analysis;
    All these keywords.

    JEL classification:

    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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