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Information flow networks of Chinese stock market sectors

Author

Listed:
  • Peng Yue

    (ECUST)

  • Qing Cai

    (Zhicang Tech)

  • Wanfeng Yan

    (Zhicang Tech)

  • Wei-Xing Zhou

    (ECUST)

Abstract

Transfer entropy measures the strength and direction of information flow between different time series. We study the information flow networks of the Chinese stock market and identify important sectors and information flow paths. This paper uses the daily closing price data of the 28 level-1 sectors from Shenyin \& Wanguo Securities ranging from 2000 to 2017 to study the information transmission between different sectors. We construct information flow networks with the sectors as the nodes and the transfer entropy between them as the corresponding edges. Then we adopt the maximum spanning arborescence (MSA) to extracting important information flows and the hierarchical structure of the networks. We find that, during the whole sample period, the \textit{composite} sector is an information source of the whole stock market, while the \textit{non-bank financial} sector is the information sink. We also find that the \textit{non-bank finance}, \textit{bank}, \textit{computer}, \textit{media}, \textit{real estate}, \textit{medical biology} and \textit{non-ferrous metals} sectors appear as high-degree root nodes in the outgoing and incoming information flow MSAs. Especially, the \textit{non-bank finance} and \textit{bank} sectors have significantly high degrees after 2008 in the outgoing information flow networks. We uncover how stock market turmoils affect the structure of the MSAs. Finally, we reveal the specificity of information source and sink sectors and make a conclusion that the root node sector as the information sink of the incoming information flow networks. Overall, our analyses show that the structure of information flow networks changes with time and the market exhibits a sector rotation phenomenon. Our work has important implications for market participants and policy makers in managing market risks and controlling the contagion of risks.

Suggested Citation

  • Peng Yue & Qing Cai & Wanfeng Yan & Wei-Xing Zhou, 2020. "Information flow networks of Chinese stock market sectors," Papers 2004.08759, arXiv.org.
    • Handle: RePEc:arx:papers:2004.08759
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    as
    1. G. Bonanno & F. Lillo & R. N. Mantegna, 2001. "High-frequency cross-correlation in a set of stocks," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 96-104.
    2. Didier SORNETTE & Guilherme DEMOS & Zhang QUN & Peter CAUWELS & Vladimir FILIMONOV & Qunzhi ZHANG, 2015. "Real-Time Prediction and Post-Mortem Analysis of the Shanghai 2015 Stock Market Bubble and Crash," Swiss Finance Institute Research Paper Series 15-32, Swiss Finance Institute.
    3. Yao, Can-Zhong & Lin, Qing-Wen & Lin, Ji-Nan, 2016. "A study of industrial electricity consumption based on partial Granger causality network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 629-646.
    4. Gang-Jin Wang & Chi Xie & Shou Chen, 2017. "Multiscale correlation networks analysis of the US stock market: a wavelet analysis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 561-594, October.
    5. 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.
    6. Sunil Kumar & Nivedita Deo, 2012. "Correlation, Network and Multifractal Analysis of Global Financial Indices," Papers 1202.0409, arXiv.org.
    7. Kwon, Okyu & Yang, Jae-Suk, 2008. "Information flow between composite stock index and individual stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2851-2856.
    8. Dias, João, 2012. "Sovereign debt crisis in the European Union: A minimum spanning tree approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 2046-2055.
    9. Miccichè, Salvatore & Bonanno, Giovanni & Lillo, Fabrizio & N. Mantegna, Rosario, 2003. "Degree stability of a minimum spanning tree of price return and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 66-73.
    10. Fenghua Wen & Xin Yang & Wei‐Xing Zhou, 2019. "Tail dependence networks of global stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 558-567, January.
    11. Heimo, Tapio & Kaski, Kimmo & Saramäki, Jari, 2009. "Maximal spanning trees, asset graphs and random matrix denoising in the analysis of dynamics of financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(2), pages 145-156.
    12. Jiang, Zhi-Qiang & Zhou, Wei-Xing & Sornette, Didier & Woodard, Ryan & Bastiaensen, Ken & Cauwels, Peter, 2010. "Bubble diagnosis and prediction of the 2005-2007 and 2008-2009 Chinese stock market bubbles," Journal of Economic Behavior & Organization, Elsevier, vol. 74(3), pages 149-162, June.
    13. G. Bonanno & G. Caldarelli & F. Lillo & S. Micciché & N. Vandewalle & R. Mantegna, 2004. "Networks of equities in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 38(2), pages 363-371, March.
    14. Zhou, Wei-Xing & Sornette, Didier, 2004. "Antibubble and prediction of China's stock market and real-estate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(1), pages 243-268.
    15. R. D'Hulst & G. J. Rodgers, 2000. "Exact Solution Of A Model For Crowding And Information Transmission In Financial Markets," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 609-616.
    16. 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.
    17. Fujio Toriumi & Kazuki Komura, 2018. "Investment Index Construction from Information Propagation Based on Transfer Entropy," Computational Economics, Springer;Society for Computational Economics, vol. 51(1), pages 159-172, January.
    18. 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.
    19. Cao, Guangxi & Zhang, Qi & Li, Qingchen, 2017. "Causal relationship between the global foreign exchange market based on complex networks and entropy theory," Chaos, Solitons & Fractals, Elsevier, vol. 99(C), pages 36-44.
    20. Frank Emmert-Streib & Aliyu Musa & Kestutis Baltakys & Juho Kanniainen & Shailesh Tripathi & Olli Yli-Harja & Herbert Jodlbauer & Matthias Dehmer, 2017. "Computational Analysis of the structural properties of Economic and Financial Networks," Papers 1710.04455, arXiv.org.
    21. Gilmore, Claire G. & Lucey, Brian M. & Boscia, Marian W., 2010. "Comovements in government bond markets: A minimum spanning tree analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4875-4886.
    22. Dionisio, Andreia & Menezes, Rui & Mendes, Diana A., 2004. "Mutual information: a measure of dependency for nonlinear time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 326-329.
    23. Eom, Cheoljun & Oh, Gabjin & Jung, Woo-Sung & Jeong, Hawoong & Kim, Seunghwan, 2009. "Topological properties of stock networks based on minimal spanning tree and random matrix theory in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(6), pages 900-906.
    24. Zuochao Zhang & Yongjie Zhang & Dehua Shen & Wei Zhang, 2018. "The Dynamic Cross-Correlations between Mass Media News, New Media News, and Stock Returns," Complexity, Hindawi, vol. 2018, pages 1-11, February.
    25. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2003. "A theory of power-law distributions in financial market fluctuations," Nature, Nature, vol. 423(6937), pages 267-270, May.
    26. Greg Leibon & Scott D. Pauls & Daniel N. Rockmore & Robert Savell, 2008. "Topological structures in the equities market network," Papers 0805.3470, arXiv.org.
    27. Yanzhu Hu & Huiyang Zhao & Xinbo Ai, 2016. "Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-25, November.
    28. Kazemilari, Mansooreh & Mardani, Abbas & Streimikiene, Dalia & Zavadskas, Edmundas Kazimieras, 2017. "An overview of renewable energy companies in stock exchange: Evidence from minimal spanning tree approach," Renewable Energy, Elsevier, vol. 102(PA), pages 107-117.
    29. Jang, Wooseok & Lee, Junghoon & Chang, Woojin, 2011. "Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 707-718.
    30. Heimo, Tapio & Saramäki, Jari & Onnela, Jukka-Pekka & Kaski, Kimmo, 2007. "Spectral and network methods in the analysis of correlation matrices of stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 147-151.
    31. Eryiğit, Mehmet & Eryiğit, Resul, 2009. "Network structure of cross-correlations among the world market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3551-3562.
    32. Coletti, Paolo, 2016. "Comparing minimum spanning trees of the Italian stock market using returns and volumes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 246-261.
    33. Parameswaran Gopikrishnan & Martin Meyer & Luis A Nunes Amaral & H Eugene Stanley, 1998. "Inverse Cubic Law for the Probability Distribution of Stock Price Variations," Papers cond-mat/9803374, arXiv.org, revised May 1998.
    34. Yook, Soon-Hyung & Chae, Huiseung & Kim, Jinho & Kim, Yup, 2016. "Finding modules and hierarchy in weighted financial network using transfer entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 493-501.
    35. Grauer, Robert R & Hakansson, Nils H, 2001. "Applying Portfolio Change and Conditional Performance Measures: The Case of Industry Rotation via the Dynamic Investment Model," Review of Quantitative Finance and Accounting, Springer, vol. 17(3), pages 237-265, November.
    36. Grauer, Robert R. & Hakansson, Nils H. & Shen, Frederick C., 1990. "Industry rotation in the U.S. stock market : 1934-1986 returns on passive, semi-passive, and active strategies," Journal of Banking & Finance, Elsevier, vol. 14(2-3), pages 513-538, August.
    37. Okyu Kwon & Jae-Suk Yang, 2008. "Information flow between stock indices," Papers 0802.1747, arXiv.org.
    38. P. Gopikrishnan & M. Meyer & L.A.N. Amaral & H.E. Stanley, 1998. "Inverse cubic law for the distribution of stock price variations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 3(2), pages 139-140, July.
    39. Gang-Jin Wang & Chi Xie & H. Eugene Stanley, 2018. "Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 607-635, March.
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    3. Nie, Chun-Xiao, 2023. "Time-varying characteristics of information flow networks in the Chinese market: An analysis based on sector indices," Finance Research Letters, Elsevier, vol. 54(C).
    4. Choi, Insu & Lee, Myounggu & Kim, Hyejin & Kim, Woo Chang, 2023. "Elucidating Directed Statistical Dependencies: Investigating Global Financial Market Indices' Influence on Korean Short Selling Activities," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    5. Xie, Wen-Jie & Yong, Yang & Wei, Na & Yue, Peng & Zhou, Wei-Xing, 2021. "Identifying states of global financial market based on information flow network motifs," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    6. Kuang, Peng-Cheng, 2021. "Measuring information flow among international stock markets: An approach of entropy-based networks on multi time-scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 577(C).

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