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Comparison between global financial crisis and local stock disaster on top of Chinese stock network

Author

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  • Xia, Lisi
  • You, Daming
  • Jiang, Xin
  • Guo, Quantong

Abstract

The science of complex network theory can be usefully applied in many important fields, one of which is the finance. In these practical cases, a massive dataset can be represented as a very large network with certain attributes associated with its nodes and edges. As one of the most important components of financial market, stock market has been attracting more and more attention. In this paper, we propose a threshold model to build Chinese stock market networks and study the topological properties of these networks. To be specific, we compare the effects of different crises, namely the 2008 global crisis and the stock market disaster in 2015, on the threshold networks. Prices of the stocks belonging to the Shanghai and Shenzhen 300 index are considered for three periods: the global crisis, common period and the stock market disaster. We find the probability distribution of the cross-correlations of the stocks during the stock market disaster is fatter than that of others. Besides, the thresholds of cross-correlations are assigned to obtain the threshold networks and the power-law of degree distribution in these networks are observed in a certain range of threshold values. The networks during the stock market disaster also appear to have larger mean degree and modularity, which reveals the strong correlations among these stock prices. Our findings to some extent crosscheck the liquidity shortage reason which is believed to result in the outbreak of the stock market disaster. Moreover, we hope that this paper could give us a deeper understanding of the market’s behavior and also lead to interesting future research about the problems of modern finance theory.

Suggested Citation

  • Xia, Lisi & You, Daming & Jiang, Xin & Guo, Quantong, 2018. "Comparison between global financial crisis and local stock disaster on top of Chinese stock network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 222-230.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:222-230
    DOI: 10.1016/j.physa.2017.08.005
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    Citations

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    Cited by:

    1. Peng Liu, 2024. "Antinetwork among China A-shares," Papers 2404.00028, arXiv.org.
    2. Hongxing Yao & Yanyu Lu & Bilal Ahmed Memon, 2019. "Impact of US-China Trade War on the Network Topology Structure of Chinese Stock Market," Journal of Asian Business Strategy, Asian Economic and Social Society, vol. 9(2), pages 235-250, December.
    3. Yang, Xin & Wen, Shigang & Zhao, Xian & Huang, Chuangxia, 2020. "Systemic importance of financial institutions: A complex network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    4. Hou, Jianlei & Zhao, Shangmei & Yang, Haijun, 2018. "Security analysts’ earnings forecasting performance based on information transmission network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 611-619.
    5. He, Chengying & Wen, Zhang & Huang, Ke & Ji, Xiaoqin, 2022. "Sudden shock and stock market network structure characteristics: A comparison of past crisis events," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    6. Guo, Hongfeng & Xia, Shengxiang & An, Qiguang & Zhang, Xin & Sun, Weihua & Zhao, Xinyao, 2020. "Empirical study of financial crises based on topological data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    7. Mbatha, Vusisizwe Moses & Alovokpinhou, Sedjro Aaron, 2022. "The structure of the South African stock market network during COVID-19 hard lockdown," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    8. Bilal Ahmed Memon & Hongxing Yao & Rabia Tahir, 2020. "General election effect on the network topology of Pakistan’s stock market: network-based study of a political event," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-14, December.
    9. Chen, Wei & Qu, Shuai & Jiang, Manrui & Jiang, Cheng, 2021. "The construction of multilayer stock network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    10. Su, Zhi & Shu, Tengjia & Yin, Libo, 2018. "The pricing effect of the common pattern in firm-level idiosyncratic volatility: Evidence from A-Share stocks of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 218-235.
    11. de Pontes, Lucca Siebra & Rêgo, Leandro Chaves, 2022. "Impact of macroeconomic variables on the topological structure of the Brazilian stock market: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    12. Fang, Libing & Qian, Yichuo & Chen, Ying & Yu, Honghai, 2018. "How does stock market volatility react to NVIX? Evidence from developed countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 490-499.

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