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The structural role of weak and strong links in a financial market network

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  • A. Garas
  • P. Argyrakis
  • S. Havlin

Abstract

We investigate the properties of correlation based networks originating from economic complex systems, such as the network of stocks traded at the New York Stock Exchange (NYSE). The weaker links (low correlation) of the system are found to contribute to the overall connectivity of the network significantly more than the strong links (high correlation). We find that nodes connected through strong links form well defined communities. These communities are clustered together in more complex ways compared to the widely used classification according to the economic activity. We find that some companies, such as General Electric (GE), Coca Cola (KO), and others, can be involved in different communities. The communities are found to be quite stable over time. Similar results were obtained by investigating markets completely different in size and properties, such as the Athens Stock Exchange (ASE). The present method may be also useful for other networks generated through correlations. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2008

Suggested Citation

  • A. Garas & P. Argyrakis & S. Havlin, 2008. "The structural role of weak and strong links in a financial market network," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(2), pages 265-271, May.
    • Handle: RePEc:spr:eurphb:v:63:y:2008:i:2:p:265-271
    DOI: 10.1140/epjb/e2008-00237-3
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    References listed on IDEAS

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    1. Dorogovtsev, S.N. & Mendes, J.F.F., 2003. "Evolution of Networks: From Biological Nets to the Internet and WWW," OUP Catalogue, Oxford University Press, number 9780198515906.
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    Cited by:

    1. F. Y. Ouyang & B. Zheng & X. F. Jiang, 2014. "Spatial and temporal structures of four financial markets in Greater China," Papers 1402.1046, arXiv.org.
    2. Tian, Hu & Zheng, Xiaolong & Zeng, Daniel Danjun, 2019. "Analyzing the dynamic sectoral influence in Chinese and American stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    3. Dror Kenett & Shlomo Havlin, 2015. "Network science: a useful tool in economics and finance," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 14(2), pages 155-167, November.
    4. 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.
    5. Rahul Kaushik & Stefano Battiston, "undated". "Credit Default Swaps Drawup Networks: Too Tied To Be Stable?," Working Papers ETH-RC-12-013, ETH Zurich, Chair of Systems Design.
    6. Dion Harmon & Marco Lagi & Marcus A M de Aguiar & David D Chinellato & Dan Braha & Irving R Epstein & Yaneer Bar-Yam, 2015. "Anticipating Economic Market Crises Using Measures of Collective Panic," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-27, July.
    7. Kyu-Min Lee & Jae-Suk Yang & Gunn Kim & Jaesung Lee & Kwang-Il Goh & In-mook Kim, 2011. "Impact of the Topology of Global Macroeconomic Network on the Spreading of Economic Crises," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    8. Kyu-Min Lee & Kwang-Il Goh, 2016. "Strength of weak layers in cascading failures on multiplex networks: case of the international trade network," Papers 1603.05181, arXiv.org, revised May 2016.
    9. Ouyang, F.Y. & Zheng, B. & Jiang, X.F., 2014. "Spatial and temporal structures of four financial markets in Greater China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 236-244.
    10. Yang, Chunxia & Chen, Yanhua & Niu, Lei & Li, Qian, 2014. "Cointegration analysis and influence rank—A network approach to global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 168-185.
    11. Tu, Chengyi, 2014. "Cointegration-based financial networks study in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 245-254.
    12. Rahul Kaushik & Stefano Battiston, 2013. "Credit Default Swaps Drawup Networks: Too Interconnected to Be Stable?," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-8, July.
    13. Jisha Mariyam John & Michele Bellingeri & Divya Sindhu Lekha & Davide Cassi & Roberto Alfieri, 2023. "Effect of Weight Thresholding on the Robustness of Real-World Complex Networks to Central Node Attacks," Mathematics, MDPI, vol. 11(16), pages 1-12, August.
    14. Zhang, Yaozhong & Wu, Junfeng & Zhang, Chao, 2021. "Risk transfer between stock and open-ended equity fund markets in China based on a multi-layer network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    15. Cai, Wenxue & Liang, Fenfen & Wan, Yanchun & Zhong, Huiling & Gu, Yimiao, 2021. "An innovative approach for constructing a shipping index based on dynamic weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    16. Kyu-Min Lee & Jae-Suk Yang & Gunn Kim & Jaesung Lee & Kwang-Il Goh & In-mook Kim, 2010. "Impact of the topology of global macroeconomic network on the spreading of economic crises," Papers 1011.4336, arXiv.org, revised Apr 2011.
    17. Jisha Mariyam John & Michele Bellingeri & Divya Sindhu Lekha & Davide Cassi & Roberto Alfieri, 2024. "Robustness of Real-World Networks after Weight Thresholding with Strong Link Removal," Mathematics, MDPI, vol. 12(10), pages 1-17, May.
    18. Ravi Kashyap, 2024. "The Blockchain Risk Parity Line: Moving From The Efficient Frontier To The Final Frontier Of Investments," Papers 2407.09536, arXiv.org.
    19. Das, Sai Saranga & Raman, Karthik, 2022. "Effect of dormant spare capacity on the attack tolerance of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).

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