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Analyzing the co-movement and its spatial–temporal patterns in Chinese stock market

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  • Chen, Hanxiao
  • Zheng, Xiaolong
  • Zeng, Daniel Dajun

Abstract

The regional financial turbulence might result in the collapse of the whole financial system and could even subsequently burden the international financial markets. In this study, we mainly focus on investigating the interdependence of stocks and understanding the co-movement and its spatial–temporal patterns in Chinese stock market. Our empirical results demonstrate that the extent of co-movement between two provinces is positively influenced by their GDP on average, and negatively affected by dissimilarity of the economic structure. By constructing the Triangulated Maximally Filtered Graph and hierarchical tree, we find that western region is not as covariant as eastern region while the influence strength of a province is highly associated with its economic strength. Further, we obtain that the stock indices are stably closely connected over time, and provincial indices grow greater during the bull and bear market and move towards the direction opposite to Shanghai Stock Exchange Composite Index during the regular market oscillation period. These findings can provide for policymakers and investors significant insights into understanding the underlying interdependency of stocks and the corresponding economic factors in Chinese financial markets from the spatial–temporal perspective.

Suggested Citation

  • Chen, Hanxiao & Zheng, Xiaolong & Zeng, Daniel Dajun, 2020. "Analyzing the co-movement and its spatial–temporal patterns in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    • Handle: RePEc:eee:phsmap:v:555:y:2020:i:c:s0378437120303198
    DOI: 10.1016/j.physa.2020.124655
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    1. Xiaodong Zhang & Suhui Liu & Xin Zheng, 2021. "Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism," Mathematics, MDPI, vol. 9(8), pages 1-21, April.

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