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Construction and robustness of directed-weighted financial stock networks via meso-scales

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  • Su, Qingqing
  • Tu, Lilan
  • Wang, Xianjia
  • Rong, Hang

Abstract

In order to investigate the evolution of stock market in any period of time, in this paper, combined with sliding window method, Granger causality test and cointegration test, a novel algorithm of constructing directed-weighted financial stock networks via meso-scale. We use the proposed algorithm on the financial stock data of the Shanghai Stock Exchange from January 1st, 2016 to December 31st, 2020 divided at the meso-scale level to construct some directed-weighted financial stock networks with fixed meso-scale and different meso-scale. Analyzing the topology and robustness of these constructed networks indicates that the robustness of financial stock networks with fixed meso-scale (assuming one year) is generally poor. By comparison, in 2018, the financial stocks were closely in contact with each other while the stock market was relatively stable. We also noted that the financial stock networks with different meso-scale show a high level of robustness when being attacked according to descending arrangement of in-degree or out-degree. They are however less robust against attacks according to descending arrangement of degree. Besides, the networks perform best for a meso-scale of 190. Hence, long-term investors are advised to consider a 190-day holding.

Suggested Citation

  • Su, Qingqing & Tu, Lilan & Wang, Xianjia & Rong, Hang, 2022. "Construction and robustness of directed-weighted financial stock networks via meso-scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    • Handle: RePEc:eee:phsmap:v:605:y:2022:i:c:s0378437122006045
    DOI: 10.1016/j.physa.2022.127955
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    as
    1. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    2. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2012. "Stock market networks: The dynamic conditional correlation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4147-4158.
    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. Majapa, Mohamed & Gossel, Sean Joss, 2016. "Topology of the South African stock market network across the 2008 financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 35-47.
    5. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    6. Kazemilari, Mansooreh & Djauhari, Maman Abdurachman, 2015. "Correlation network analysis for multi-dimensional data in stocks market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 62-75.
    7. Papana, Angeliki & Kyrtsou, Catherine & Kugiumtzis, Dimitris & Diks, Cees, 2017. "Financial networks based on Granger causality: A case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 65-73.
    8. Nobi, Ashadun & Maeng, Seong Eun & Ha, Gyeong Gyun & Lee, Jae Woo, 2014. "Effects of global financial crisis on network structure in a local stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 135-143.
    9. 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.
    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. Xiaole Wan & Zhen Zhang & Chi Zhang & Qingchun Meng, 2020. "Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index," Complexity, Hindawi, vol. 2020, pages 1-19, July.
    12. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    13. Xin-Jian Xu & Kuo Wang & Liucun Zhu & Li-Jie Zhang, 2018. "Efficient construction of threshold networks of stock markets," Papers 1803.06223, arXiv.org, revised Aug 2018.
    14. Xu, Xin-Jian & Wang, Kuo & Zhu, Liucun & Zhang, Li-Jie, 2018. "Efficient construction of threshold networks of stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1080-1086.
    15. Fenghua Wen & Xu Gong & Youcong Chao & Xiaohong Chen, 2014. "The Effects of Prior Outcomes on Risky Choice: Evidence from the Stock Market," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, March.
    16. Teh, Boon Kin & Goo, Yik Wen & Lian, Tong Wei & Ong, Wei Guang & Choi, Wen Ting & Damodaran, Mridula & Cheong, Siew Ann, 2015. "The Chinese Correction of February 2007: How financial hierarchies change in a market crash," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 225-241.
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