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Hierarchical Organization And Disassortative Mixing Of Correlation-Based Weighted Financial Networks

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  • SHI-MIN CAI

    (Department of Electronic Science and Technology, University of Science and Technology of China, Hefei Anhui 230026, P. R. China;
    Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland)

  • YAN-BO ZHOU

    (Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland)

  • TAO ZHOU

    (Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland;
    Department of Modern Physics, University of Science and Technology of China, Hefei Anhui 230026, P. R. China)

  • PEI-LING ZHOU

    (Department of Electronic Science and Technology, University of Science and Technology of China, Hefei Anhui 230026, P. R. China)

Abstract

Correlation-based weighted financial networks are analyzed to present cumulative distribution of strength with a power-law tail, which suggests that a small number of hub-like stocks have greater influence on the whole fluctuation of financial market than others. The relationship between clustering and connectivity of vertices emphasizes hierarchical organization, which has been depicted by minimal span tree in previous work. These results urge us to further study the mixing patter of financial network to understand the tendency for vertices to be connected to vertices that are like (or unlike) them in some way. The measurement of average nearest-neighbor degree running over classes of vertices with degreekshows a descending trend whenkincreases. This interesting result is first uncovered in our work, and suggests the disassortative mixing of financial network which refers to a bias in favor of connections between dissimilar vertices. All the results in weighted complex network aspect may provide some insights to deeper understand the underlying mechanism of financial market and model the evolution of financial market.

Suggested Citation

  • Shi-Min Cai & Yan-Bo Zhou & Tao Zhou & Pei-Ling Zhou, 2010. "Hierarchical Organization And Disassortative Mixing Of Correlation-Based Weighted Financial Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(03), pages 433-441.
    • Handle: RePEc:wsi:ijmpcx:v:21:y:2010:i:03:n:s0129183110015208
    DOI: 10.1142/S0129183110015208
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    References listed on IDEAS

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    1. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871, October.
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    1. 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.
    2. Sun, Xiao-Qian & Cheng, Xue-Qi & Shen, Hua-Wei & Wang, Zhao-Yang, 2011. "Distinguishing manipulated stocks via trading network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3427-3434.
    3. Longfeng Zhao & Chao Wang & Gang-Jin Wang & H. Eugene Stanley & Lin Chen, 2021. "Community detection and portfolio optimization," Papers 2112.13383, arXiv.org.
    4. Fei Ren & Wei-Xing Zhou, 2014. "Dynamic Evolution of Cross-Correlations in the Chinese Stock Market," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-15, May.
    5. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    6. Zhao, Longfeng & Wang, Gang-Jin & Wang, Mingang & Bao, Weiqi & Li, Wei & Stanley, H. Eugene, 2018. "Stock market as temporal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1104-1112.
    7. Zhang, Shuaishuai & Wu, Libo & Zhou, Yang, 2020. "The impact of negative list policy on sectoral structure: Based on complex network and DID analysis," Applied Energy, Elsevier, vol. 278(C).
    8. Xiao-Qian Sun & Xue-Qi Cheng & Hua-Wei Shen & Zhao-Yang Wang, 2011. "Distinguishing manipulated stocks via trading network analysis," Papers 1110.2260, arXiv.org.

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