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Analyzing the dynamic sectoral influence in Chinese and American stock markets

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  • Tian, Hu
  • Zheng, Xiaolong
  • Zeng, Daniel Danjun

Abstract

In this paper, we mainly focus on examining the sectoral influence on fine time scales in the Chinese and American stock markets. Based on the dataset regarding the 10 sector indices, we construct the sectoral-level causal networks by incorporating the empirical mode decomposition into Granger causal test and find that the most influential sectors on different time scales are almost different except that industrial sector has prominent influence on all time scales in the Chinese stock markets. We further confirm that the influence of dominant sectors on different time scale is stable both in the Chinese and American stock markets. Especially, we investigate the periods of some extreme market events such as the 2008 financial crisis, and obtain that the stock market collapse and soar events can improve the statistical causality of sectors and enhances the linkages among sectors on the long time scale. These findings can provide significant insights for policymakers and investors to understand the underlying differences regarding the dynamic sectoral influence in stock markets of developing and developed countries.

Suggested Citation

  • Tian, Hu & Zheng, Xiaolong & Zeng, Daniel Danjun, 2019. "Analyzing the dynamic sectoral influence in Chinese and American stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    • Handle: RePEc:eee:phsmap:v:536:y:2019:i:c:s0378437119305369
    DOI: 10.1016/j.physa.2019.04.158
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    Cited by:

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    2. Chuangxia Huang & Xian Zhao & Renli Su & Xiaoguang Yang & Xin Yang, 2022. "Dynamic network topology and market performance: A case of the Chinese stock market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1962-1978, April.
    3. Niu, Hongli & Hu, Ziang, 2021. "Information transmission and entropy-based network between Chinese stock market and commodity futures market," Resources Policy, Elsevier, vol. 74(C).
    4. Hu, Yunchao & Lu, Guibin & Gao, Wenyu, 2022. "A study on China’s systemically important financial institutions based on multi-time scale causality networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    5. 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).

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