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A network perspective of comovement and structural change: Evidence from the Chinese stock market

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  • Huang, Chuangxia
  • Deng, Yunke
  • Yang, Xiaoguang
  • Cao, Jinde
  • Yang, Xin

Abstract

How to appropriately characterize the comovement between any pair of individual stocks and describe the market comovement structure is a great challenge and plays a key role in understanding emerging markets. This paper applies the complex network approach to deal with this issue for the Chinese stock market. Firstly, in view of the topological properties, we investigate the time-varying comovement between individual stocks by constructing 14 directed weighted stock networks. Furthermore, the weighted LeaderRank algorithm is employed to describe the comovement structure of the entire market. Most importantly, from the perspective of fundamental factors and industry factors, we reveal the driving factors of the comovement and structural change of the entire market. The empirical results suggest that: (i) Stocks with higher weighted LeaderRank algorithm scores generally have more long-term investment value; and the so-called views, “too big to fail” and “too connected to fail”, are further confirmed. (ii) ROE, BMratio and Growth are significantly positively correlated with the comovement between individual stocks, and Mvalue is significantly negatively correlated during normal periods. However, during the crisis, the signs of regression coefficients of above four explanatory variables are reversed. (iii) In normal periods, we only find that the agriculture, forestry, animal husbandry & fishery and composite have significant influence on the comovement structure of the entire market. Besides, public utilities and medias also have a significant impact during the crisis. In addition, a very interesting fact in point is that network density, average clustering coefficient, and global efficiency can provide an “early warning” for possible upcoming crises.

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  • Huang, Chuangxia & Deng, Yunke & Yang, Xiaoguang & Cao, Jinde & Yang, Xin, 2021. "A network perspective of comovement and structural change: Evidence from the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 76(C).
    • Handle: RePEc:eee:finana:v:76:y:2021:i:c:s1057521921001125
    DOI: 10.1016/j.irfa.2021.101782
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    as
    1. Chowdhury, Biplob & Dungey, Mardi & Kangogo, Moses & Sayeed, Mohammad Abu & Volkov, Vladimir, 2019. "The changing network of financial market linkages: The Asian experience," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 71-92.
    2. Sun, Qingru & Gao, Xiangyun & Wen, Shaobo & Chen, Zhihua & Hao, Xiaoqing, 2018. "The transmission of fluctuation among price indices based on Granger causality network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 36-49.
    3. He, Zhifang, 2020. "Dynamic impacts of crude oil price on Chinese investor sentiment: Nonlinear causality and time-varying effect," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 131-153.
    4. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    5. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    6. Yu, Jia-Wei & Xie, Wen-Jie & Jiang, Zhi-Qiang, 2018. "Early warning model based on correlated networks in global crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1335-1343.
    7. Aloui, Riadh & Aïssa, Mohamed Safouane Ben & Nguyen, Duc Khuong, 2011. "Global financial crisis, extreme interdependences, and contagion effects: The role of economic structure?," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 130-141, January.
    8. Huang, Xiaohong & Huang, Shupei, 2020. "Identifying the comovement of price between China's and international crude oil futures: A time-frequency perspective," International Review of Financial Analysis, Elsevier, vol. 72(C).
    9. Mark Buchanan, 2009. "Economics: Meltdown modelling," Nature, Nature, vol. 460(7256), pages 680-682, August.
    10. Nguyen, Q. & Nguyen, N.K. K. & Nguyen, L.H. N., 2019. "Dynamic topology and allometric scaling behavior on the Vietnamese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 235-243.
    11. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang & Uryasev, Stan, 2016. "A financial network perspective of financial institutions’ systemic risk contributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 183-196.
    12. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    13. M. Tumminello & T. Di Matteo & T. Aste & R. N. Mantegna, 2007. "Correlation based networks of equity returns sampled at different time horizons," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 209-217, January.
    14. Zhifang He & Linjie He & Fenghua Wen, 2019. "Risk Compensation and Market Returns: The Role of Investor Sentiment in the Stock Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(3), pages 704-718, February.
    15. Song, Lingfeng & Zhang, Yinsainan, 2021. "Banking network structure and transnational systemic risk contagion—The case of the European Union," Finance Research Letters, Elsevier, vol. 39(C).
    16. Kallberg, Jarl & Pasquariello, Paolo, 2008. "Time-series and cross-sectional excess comovement in stock indexes," Journal of Empirical Finance, Elsevier, vol. 15(3), pages 481-502, June.
    17. 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.
    18. Chen, Peng, 2018. "Understanding international stock market comovements: A comparison of developed and emerging markets," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 451-464.
    19. Zhang, Weiping & Zhuang, Xintian & Lu, Yang & Wang, Jian, 2020. "Spatial linkage of volatility spillovers and its explanation across G20 stock markets: A network framework," International Review of Financial Analysis, Elsevier, vol. 71(C).
    20. Linyuan Lü & Yi-Cheng Zhang & Chi Ho Yeung & Tao Zhou, 2011. "Leaders in Social Networks, the Delicious Case," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-9, June.
    21. Anna D. Broido & Aaron Clauset, 2019. "Scale-free networks are rare," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    22. Kukacka, Jiri & Kristoufek, Ladislav, 2020. "Do ‘complex’ financial models really lead to complex dynamics? Agent-based models and multifractality," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    23. Wang, Dan & Huang, Wei-Qiang, 2021. "Centrality-based measures of financial institutions’ systemic importance: A tail dependence network view," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    24. Zhang, Hongwei & Wang, Ying & Zhu, Xuehong & Guo, Yaoqi, 2020. "The impact of energy trade patterns on CO2 emissions: An emergy and network analysis," Energy Economics, Elsevier, vol. 92(C).
    25. 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.
    26. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    27. Dirk Helbing, 2013. "Globally networked risks and how to respond," Nature, Nature, vol. 497(7447), pages 51-59, May.
    28. Fujii, Eiji, 2005. "Intra and inter-regional causal linkages of emerging stock markets: evidence from Asia and Latin America in and out of crises," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(4), pages 315-342, October.
    29. Malceniece, Laura & Malcenieks, Kārlis & Putniņš, Tālis J., 2019. "High frequency trading and comovement in financial markets," Journal of Financial Economics, Elsevier, vol. 134(2), pages 381-399.
    30. repec:taf:emfitr:v:55:y:2019:i:3:p:704-718 is not listed on IDEAS
    31. Robert Engle & Clive Granger, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    32. Green, T. Clifton & Hwang, Byoung-Hyoun, 2009. "Price-based return comovement," Journal of Financial Economics, Elsevier, vol. 93(1), pages 37-50, July.
    33. Cimini, Riccardo, 2015. "Eurozone network “Connectedness” after fiscal year 2008," Finance Research Letters, Elsevier, vol. 14(C), pages 160-166.
    34. Jean-Philippe Bouchaud, 2008. "Economics need a scientific revolution," Papers 0810.5306, arXiv.org.
    35. Majapa, Mohamed & Gossel, Sean Joss, 2016. "Topology of the South African stock market network across the 2008 financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 35-47.
    36. Zhang, Hongwei & Wang, Ying & Yang, Cai & Guo, Yaoqi, 2021. "The impact of country risk on energy trade patterns based on complex network and panel regression analyses," Energy, Elsevier, vol. 222(C).
    37. Jean-Philippe Bouchaud, 2008. "Economics needs a scientific revolution," Nature, Nature, vol. 455(7217), pages 1181-1181, October.
    38. J.-P. Onnela & K. Kaski & J. Kertész, 2004. "Clustering and information in correlation based financial networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 38(2), pages 353-362, March.
    39. 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.
    40. Ftiti, Zied & Guesmi, Khaled & Abid, Ilyes, 2016. "Oil price and stock market co-movement: What can we learn from time-scale approaches?," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 266-280.
    41. Li, Qian & Zhou, Tao & Lü, Linyuan & Chen, Duanbing, 2014. "Identifying influential spreaders by weighted LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 47-55.
    42. 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.
    43. Tsani, Stela Z., 2010. "Energy consumption and economic growth: A causality analysis for Greece," Energy Economics, Elsevier, vol. 32(3), pages 582-590, May.
    44. Wang, Ze & Gao, Xiangyun & An, Haizhong & Tang, Renwu & Sun, Qingru, 2020. "Identifying influential energy stocks based on spillover network," International Review of Financial Analysis, Elsevier, vol. 68(C).
    45. 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.
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