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Dynamics of cluster structures in a financial market network

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  • Kocheturov, Anton
  • Batsyn, Mikhail
  • Pardalos, Panos M.

Abstract

In the course of recent fifteen years the network analysis has become a powerful tool for studying financial markets. In this work we analyze stock markets of the USA and Sweden. We study cluster structures of a market network constructed from a correlation matrix of returns of the stocks traded in each of these markets. Such cluster structures are obtained by means of the P-Median Problem (PMP) whose objective is to maximize the total correlation between a set of stocks called medians of size p and other stocks. Every cluster structure is an undirected disconnected weighted graph in which every connected component (cluster) is a star, or a tree with one central node (called a median) and several leaf nodes connected with the median by weighted edges. Our main observation is that in non-crisis periods of time cluster structures change more chaotically, while during crises they show more stable behavior and fewer changes. Thus an increasing stability of a market graph cluster structure obtained via the PMP could be used as an indicator of a coming crisis.

Suggested Citation

  • Kocheturov, Anton & Batsyn, Mikhail & Pardalos, Panos M., 2014. "Dynamics of cluster structures in a financial market network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 523-533.
    • Handle: RePEc:eee:phsmap:v:413:y:2014:i:c:p:523-533
    DOI: 10.1016/j.physa.2014.06.077
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    Cited by:

    1. Wu, Jianshe & Zhang, Long & Li, Yong & Jiao, Yang, 2016. "Partition signed social networks via clustering dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 568-582.
    2. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    3. Huang, Wei-Qiang & Yao, Shuang & Zhuang, Xin-Tian & Yuan, Ying, 2017. "Dynamic asset trees in the US stock market: Structure variation and market phenomena," Chaos, Solitons & Fractals, Elsevier, vol. 94(C), pages 44-53.
    4. Bentian Li & Dechang Pi, 2018. "Analysis of global stock index data during crisis period via complex network approach," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-16, July.
    5. khoojine, Arash Sioofy & Han, Dong, 2019. "Network analysis of the Chinese stock market during the turbulence of 2015–2016 using log-returns, volumes and mutual information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1091-1109.
    6. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang & Uryasev, Stan, 2016. "A financial network perspective of financial institutions’ systemic risk contributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 183-196.
    7. Kong, Xiaolin & Ma, Chaoqun & Ren, Yi-Shuai & Narayan, Seema & Nguyen, Thong Trung & Baltas, Konstantinos, 2023. "Changes in the market structure and risk management of Bitcoin and its forked coins," Research in International Business and Finance, Elsevier, vol. 65(C).
    8. Andrea Di Iura, 2022. "Comparison of empirical and shrinkage correlation algorithm for clustering methods in the futures market," SN Business & Economics, Springer, vol. 2(8), pages 1-17, August.
    9. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    10. Haiming Long & Ji Zhang & Nengyu Tang, 2017. "Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
    11. Zhu, Xiaoyu & Ma, Yinghong & Liu, Zhiyuan, 2018. "A novel evolutionary algorithm on communities detection in signed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 938-946.
    12. Tristan Millington & Mahesan Niranjan, 2020. "Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation," Papers 2005.03963, arXiv.org, revised Nov 2020.
    13. Millington, Tristan & Niranjan, Mahesan, 2021. "Stability and similarity in financial networks—How do they change in times of turbulence?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    14. Lu, Ya-Nan & Li, Sai-Ping & Zhong, Li-Xin & Jiang, Xiong-Fei & Ren, Fei, 2018. "A clustering-based portfolio strategy incorporating momentum effect and market trend prediction," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 1-15.
    15. Fazlollah Soleymani & Mahdi Vasighi, 2022. "Efficient portfolio construction by means of CVaR and k‐means++ clustering analysis: Evidence from the NYSE," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3679-3693, July.
    16. Millington, Tristan & Niranjan, Mahesan, 2021. "Construction of minimum spanning trees from financial returns using rank correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).

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