IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v536y2019ics0378437119305369.html
   My bibliography  Save this article

Analyzing the dynamic sectoral influence in Chinese and American stock markets

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119305369
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.04.158?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Raddant, Matthias & Kenett, Dror Y., 2021. "Interconnectedness in the global financial market," Journal of International Money and Finance, Elsevier, vol. 110(C).
    2. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    3. Zhang, Xingwei & Zheng, Xiaolong & Zeng, Daniel Dajun, 2017. "The dynamic interdependence of international financial markets: An empirical study on twenty-seven stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 32-42.
    4. A. Garas & P. Argyrakis & S. Havlin, 2008. "The structural role of weak and strong links in a financial market network," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(2), pages 265-271, May.
    5. Chunxia, Yang & Bingying, Xia & Sen, Hu & Rui, Wang, 2012. "A study of the interplay between the structure variation and fluctuations of the Shanghai stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3198-3205.
    6. Li, Muyi & Huang, Yongxiang, 2014. "Hilbert–Huang Transform based multifractal analysis of China stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 222-229.
    7. Yu, Lean & Li, Jingjing & Tang, Ling & Wang, Shuai, 2015. "Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach," Energy Economics, Elsevier, vol. 51(C), pages 300-311.
    8. Antonios Garas & Panos Argyrakis & Shlomo Havlin, 2008. "The structural role of weak and strong links in a financial market network," Papers 0805.2477, arXiv.org.
    9. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    10. Sensoy, Ahmet & Ozturk, Kevser & Hacihasanoglu, Erk & Tabak, Benjamin M., 2017. "Not all emerging markets are the same: A classification approach with correlation based networks," Journal of Financial Stability, Elsevier, vol. 33(C), pages 163-186.
    11. Bangzhu Zhu & Ping Wang & Julien Chevallier & Yiming Wei, 2015. "Carbon Price Analysis Using Empirical Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 195-206, February.
    12. Bradley Ewing, 2002. "The transmission of shocks among S&P indexes," Applied Financial Economics, Taylor & Francis Journals, vol. 12(4), pages 285-290.
    13. Yang, Chunxia & Chen, Yanhua & Niu, Lei & Li, Qian, 2014. "Cointegration analysis and influence rank—A network approach to global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 168-185.
    14. Mai, Yong & Chen, Huan & Meng, Lei, 2014. "An analysis of the sectorial influence of CSI300 stocks within the directed network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 235-241.
    15. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    16. Hassan, Syed Aun & Malik, Farooq, 2007. "Multivariate GARCH modeling of sector volatility transmission," The Quarterly Review of Economics and Finance, Elsevier, vol. 47(3), pages 470-480, July.
    17. Lean Yu & Jingjing Li & Ling Tang, 2015. "Dynamic volatility spillover effect analysis between carbon market and crude oil market: a DCC-ICSS approach," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 38(4/5/6), pages 242-256.
    18. Zhang, Yiting & Lee, Gladys Hui Ting & Wong, Jian Cheng & Kok, Jun Liang & Prusty, Manamohan & Cheong, Siew Ann, 2011. "Will the US economy recover in 2010? A minimal spanning tree study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2020-2050.
    19. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    20. Xu, Mengjia & Shang, Pengjian & Lin, Aijing, 2016. "Cross-correlation analysis of stock markets using EMD and EEMD," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 82-90.
    21. Wang, Jing & Shang, Pengjian & Xia, Jianan & Shi, Wenbin, 2015. "EMD based refined composite multiscale entropy analysis of complex signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 583-593.
    22. Tu, Chengyi, 2014. "Cointegration-based financial networks study in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 245-254.
    23. Hu, Sen & Yang, Hualei & Cai, Boliang & Yang, Chunxia, 2013. "Research on spatial economic structure for different economic sectors from a perspective of a complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3682-3697.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Hu Tian & Xiaolong Zheng & Kang Zhao & Maggie Wenjing Liu & Daniel Dajun Zeng, 2022. "Inductive Representation Learning on Dynamic Stock Co-Movement Graphs for Stock Predictions," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 1940-1957, July.
    3. 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).
    4. 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).
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    2. Bu, Hui & Tang, Wenjin & Wu, Junjie, 2019. "Time-varying comovement and changes of comovement structure in the Chinese stock market: A causal network method," Economic Modelling, Elsevier, vol. 81(C), pages 181-204.
    3. Yang, Chunxia & Chen, Yanhua & Niu, Lei & Li, Qian, 2014. "Cointegration analysis and influence rank—A network approach to global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 168-185.
    4. Chen, Yanhua & Li, Youwei & Pantelous, Athanasios A. & Stanley, H. Eugene, 2022. "Short-run disequilibrium adjustment and long-run equilibrium in the international stock markets: A network-based approach," International Review of Financial Analysis, Elsevier, vol. 79(C).
    5. Lahmiri, Salim, 2017. "Cointegration and causal linkages in fertilizer markets across different regimes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 181-189.
    6. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2019. "Return spillovers around the globe: A network approach," Economic Modelling, Elsevier, vol. 77(C), pages 133-146.
    7. Tu, Chengyi, 2014. "Cointegration-based financial networks study in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 245-254.
    8. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    9. Yong Tang & Jason Jie Xiong & Zi-Yang Jia & Yi-Cheng Zhang, 2018. "Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets," Complexity, Hindawi, vol. 2018, pages 1-31, November.
    10. Huang, Chuangxia & Wen, Shigang & Li, Mengge & Wen, Fenghua & Yang, Xin, 2021. "An empirical evaluation of the influential nodes for stock market network: Chinese A-shares case," Finance Research Letters, Elsevier, vol. 38(C).
    11. Ftiti, Zied & Hadhri, Sinda, 2019. "Can economic policy uncertainty, oil prices, and investor sentiment predict Islamic stock returns? A multi-scale perspective," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 40-55.
    12. Výrost, Tomas & Lyócsa, Štefan & Baumöhl, Eduard, 2019. "Network-based asset allocation strategies," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 516-536.
    13. Vidal-Tomás, David, 2021. "Transitions in the cryptocurrency market during the COVID-19 pandemic: A network analysis," Finance Research Letters, Elsevier, vol. 43(C).
    14. Zhong, Tao & Peng, Qinke & Wang, Xiao & Zhang, Jing, 2016. "Novel indexes based on network structure to indicate financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 583-594.
    15. Wen, Danyan & Ma, Chaoqun & Wang, Gang-Jin & Wang, Senzhang, 2018. "Investigating the features of pairs trading strategy: A network perspective on the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 903-918.
    16. Kyu-Min Lee & Jae-Suk Yang & Gunn Kim & Jaesung Lee & Kwang-Il Goh & In-mook Kim, 2010. "Impact of the topology of global macroeconomic network on the spreading of economic crises," Papers 1011.4336, arXiv.org, revised Apr 2011.
    17. D’Amico, Guglielmo & De Blasis, Riccardo & Petroni, Filippo, 2023. "The Mixture Transition Distribution approach to networks: Evidence from stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    18. Papana, Angeliki & Kyrtsou, Catherine & Kugiumtzis, Dimitris & Diks, Cees, 2017. "Financial networks based on Granger causality: A case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 65-73.
    19. Zhang, Yaozhong & Wu, Junfeng & Zhang, Chao, 2021. "Risk transfer between stock and open-ended equity fund markets in China based on a multi-layer network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    20. Dror Kenett & Shlomo Havlin, 2015. "Network science: a useful tool in economics and finance," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 14(2), pages 155-167, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:536:y:2019:i:c:s0378437119305369. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.