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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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    8. Xiurong Chen & Aimin Hao & Yali Li, 2020. "The impact of financial contagion on real economy-An empirical research based on combination of complex network technology and spatial econometrics model," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    9. Arash Sioofy Khoojine & Mahboubeh Shadabfar & Yousef Edrisi Tabriz, 2022. "A Mutual Information-Based Network Autoregressive Model for Crude Oil Price Forecasting Using Open-High-Low-Close Prices," Mathematics, MDPI, vol. 10(17), pages 1-20, September.
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