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Network analysis of the Chinese stock market during the turbulence of 2015–2016 using log-returns, volumes and mutual information

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  • khoojine, Arash Sioofy
  • Han, Dong

Abstract

We use mutual information and symbolization method of time series to construct minimum spanning tree of the financial network of log-returns and trading volumes of the top 110 companies on the Chinese stock market listed on the CSI 300 index from January 2014 to December 2017 to analyze the Chinese stock market’s turbulence during 2015 to 2016. We construct three minimum spanning trees of pre-turbulence, turbulence and post-turbulence. The findings show that minimum spanning tree of turbulence has the significant differences in topological characteristics and network’s measures with pre-turbulence and post-turbulence networks. Furthermore, the pre-turbulence network is robust against nodes attack while turbulence network is fragile against it.

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  • 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.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:1091-1109
    DOI: 10.1016/j.physa.2019.04.128
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    7. Charu Sharma & Amber Habib, 2019. "Mutual information based stock networks and portfolio selection for intraday traders using high frequency data: An Indian market case study," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-19, August.
    8. So, Mike K.P. & Chu, Amanda M.Y. & Chan, Thomas W.C., 2021. "Impacts of the COVID-19 pandemic on financial market connectedness," Finance Research Letters, Elsevier, vol. 38(C).
    9. Esmalifalak, Hamidreza, 2022. "Euclidean (dis)similarity in financial network analysis," Global Finance Journal, Elsevier, vol. 53(C).
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    14. Muzi Chen & Nan Li & Lifen Zheng & Difang Huang & Boyao Wu, 2024. "Dynamic Correlation of Market Connectivity, Risk Spillover and Abnormal Volatility in Stock Price," Papers 2403.19363, arXiv.org.

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