World Scientific
  • Search
    Quick Search in Journals
    Access type:
    Quick Search anywhere
    Access type:
    Advanced Search
  • My Cart
  •   
Skip main navigation
Close Drawer MenuOpen Drawer Menu

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

Our site uses Javascript to enchance its usability.You can disable your ad blocker or whitelist our website www.worldscientific.com to view the full content.

Select your blocker:

Adblock Plus Instructions

  1. Click the AdBlock Plus icon in the extension bar
  2. Click the blue power button
  3. Click refresh
Our website is made possible by displaying certain online content using javascript.
In order to view the full content, please disable your ad blocker or whitelist our website www.worldscientific.com.

System Upgrade on Tue, Oct 25th, 2022 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.
No Access

Relationship between Herd Behavior and Chinese Stock Market Fluctuations during a Bullish Period Based on Complex Networks

    https://doi.org/10.1142/S0219622021400010Cited by:2 (Source: Crossref)
    Previous
    Next

    Abstract

    Herding has a great impact on stock market fluctuations, and it is possible for researchers to analyze the herding effect due to the recent popularity of mobile Internet and the development of big data analysis technology. In this paper, we propose both investor-based and stock-based sentiment propagation networks of Chinese stock markets based on the simple pairwise correlation of posts’ sentiment indexes. And the relationship between the herding effect and Chinese stock market fluctuations is studied by comparing the network indicators with the Shanghai Securities Composite Index (SSCI) and the Causeway International Value Index (CIVIX). Through the experimental results, we find that the indicators are indeed ahead of the Chinese stock market. This study is the first attempt to model stock market sentiment by using a complex network, and it proves that investor behavior has a great effect on the stock market.

    References

    • 1. C. Liu and N. Arunkumar, Risk prediction and evaluation of transnational transmission of financial crisis based on complex network, Cluster Computing 22 (2019) 4307–4313. Crossref, Web of Science, Google Scholar
    • 2. F. Stefan and A. Atman, Is there any connection between the network morphology and the fluctuations of the stock market index? Physica A: Statistical Mechanics and its Applications 419 (2015) 630–641. Crossref, Web of Science, Google Scholar
    • 3. S. H. Strogatz, Exploring complex networks, Nature, 410(6825) (2001) 268. Crossref, Web of Science, Google Scholar
    • 4. P. Li and B.-H. Wang, Extracting hidden fluctuation patterns of Hang Seng stock index from network topologies, Physica A: Statistical Mechanics and its Applications 378(2) (2007) 519–526. Crossref, Web of Science, Google Scholar
    • 5. W. Zhang and X. Zhuang, The stability of Chinese stock network and its mechanism, Physica A: Statistical Mechanics and its Applications 515 (2019) 748–761. Crossref, Web of Science, Google Scholar
    • 6. D. S. Scharfstein et al., Herd behavior and investment, American Economic Review 80(3) (1990) 465–479. Web of Science, Google Scholar
    • 7. S. Bikhchandani and S. Sharma, Herd behavior in financial markets, IMF Staff Papers 47(3) (2000) 279–310. Google Scholar
    • 8. G. D. Raimundo, R. B. Palazzi, M. C. Klotzle and A. C. F. Pinto, Analyzing herding behavior in commodities markets - an empirical approach, Finance Research Letters 35 (2020) Paper ID 101285. Web of Science, Google Scholar
    • 9. E. C. Chang, J. W. Cheng and A. Khorana, An examination of herd behavior in equity markets: An international perspective, Journal of Banking & Finance 24(10) (2000) 1651–1679. Crossref, Web of Science, Google Scholar
    • 10. E. C. Galariotis, S.-I. Krokida and S. I. Spyrou, Herd behavior and equity market liquidity: Evidence from major markets, International Review of Financial Analysis 48 (2016) 140–149. Crossref, Web of Science, Google Scholar
    • 11. H. Litimi, A. BenSaïda and O. Bouraoui, Herding and excessive risk in the American stock market: A sectoral analysis, Research in International Business and Finance 38 (2016) 6–21. Crossref, Web of Science, Google Scholar
    • 12. A. Clements, S. Hurn and S. Shi, An empirical investigation of herding in the US stock market, Economic Modelling 67 (2017) 184–192. Crossref, Web of Science, Google Scholar
    • 13. E. C. Galariotis, S.-I. Krokida and S. I. Spyrou, Bond market investor herding: Evidence from the European financial crisis, International Review of Financial Analysis 48 (2016) 367–375. Crossref, Web of Science, Google Scholar
    • 14. T. C. Chiang, J. Li and L. Tan, Empirical investigation of herding behavior in Chinese stock markets: Evidence from quantile regression analysis, Global Finance Journal 21(1) (2010) 111–124. Crossref, Google Scholar
    • 15. J. Yao, C. Ma, and W. P. He, Investor herding behaviour of Chinese stock market, International Review of Economics & Finance 29 (2014) 12–29. Crossref, Web of Science, Google Scholar
    • 16. Z. Chen and T. Lux, Identification of high-frequency herding behavior in the Chinese stock market: An agent-based approach, in Innovative Approaches in Agent-Based Modelling and Business Intelligence (Springer, 2018), pp. 157–171. Crossref, Google Scholar
    • 17. D. Zheng, H. Li and T. C. Chiang, Herding within industries: Evidence from Asian stock markets, International Review of Economics & Finance 51 (2017) 487–509. Crossref, Web of Science, Google Scholar
    • 18. T. C. Chiang and D. Zheng, An empirical analysis of herd behavior in global stock markets, Journal of Banking & Finance 34(8) (2010) 1911–1921. Crossref, Web of Science, Google Scholar
    • 19. A. Devenow and I. Welch, Rational herding in financial economics, European Economic Review 40(3) (1996) 603–615. Crossref, Web of Science, Google Scholar
    • 20. D. Zheng, H. Li and T. C. Chiang, Herding within industries: Evidence from Asian stock markets, International Review of Economics & Finance 51 (2017) 487–509. Crossref, Web of Science, Google Scholar
    • 21. W. Q. Huang, X. T. Zhuang and S. Yao, A network analysis of the Chinese stock market, Physica A: Statistical Mechanics and its Applications 388(14) (2009) 2956–2964. Crossref, Web of Science, Google Scholar
    • 22. Z. Jiang and W. Zhou, Complex stock trading network among investors, Physica A: Statistical Mechanics and its Applications 389 (2010) 4929–4941. Crossref, Web of Science, Google Scholar
    • 23. M. X. Li, Z. Q. Jiang, W. J. Xie, X. Xiong, W. Zhang and W. X. Zhou, Unveiling correlations between financial variables and topological metrics of trading networks: Evidence from a stock and its warrant, Physica A: Statistical Mechanics and its Applications 419 (2015) 575–584. Crossref, Web of Science, Google Scholar
    • 24. L. Zhao, G. J. Wang, M. Wang, W. Bao and H. E. Stanley, Stock market as temporal network, Physica A: Statistical Mechanics and its Applications 506(15) (2018) 1104–1112. Crossref, Web of Science, Google Scholar
    • 25. T. Lee, J. Cho, D. Kwon and S. Sohn, Global stock market investment strategies based on financial network indicators using machine learning techniques, Expert Systems with Applications 117 (2019) 228–242. Crossref, Web of Science, Google Scholar
    • 26. X. Zhang, J. Zhu, Q. Wang and H. Zhao, Identifying influential nodes in complex networks with community structure, Knowledge-Based Systems 42 (2013) 74–84. Crossref, Web of Science, Google Scholar
    • 27. S. Hu, Z. Gu, Y. Wang and X. Zhang, An analysis of the clustering effect of a jump risk complex network in the Chinese stock market, Physica A: Statistical Mechanics and its Applications 523 (2019) 622–630. Crossref, Web of Science, Google Scholar
    • 28. W. Long, L. Guan, J. Shen, L. Song and L. Cui, A complex network for studying the transmission mechanisms in stock market, Physica A: Statistical Mechanics and its Applications 484 (2017) 345–357. Crossref, Web of Science, Google Scholar
    • 29. R. Ren and D. Wu, An innovative sentiment analysis to measure herd behavior, IEEE Transactions on Systems, Man, and Cybernetics: Systems 50(10) (2020) 3841–3851. Web of Science, Google Scholar
    • 30. Y. Hudson, M. Yan and D. Zhang, Herd behaviour & investor sentiment: Evidence from UK mutual funds, International Review of Financial Analysis 71 (2020) 101494. Crossref, Web of Science, Google Scholar
    • 31. C. Z. Yao and H. Y. Li, Time-varying lead-lag structure between investor sentiment and stock market, The North American Journal of Economics and Finance 52 (2020) 101148. Crossref, Web of Science, Google Scholar
    • 32. M. Schmeling, Investor sentiment and stock returns: Some international evidence, Journal of Empirical Finance 16(3) (2009) 394–408. Crossref, Web of Science, Google Scholar
    • 33. R. N. Mantegna, Hierarchical structure in financial markets, Computer Physics Communications 121–122 (1999) 153–156. Crossref, Web of Science, Google Scholar
    • 34. Y. Shi, Y. Zheng, K. Guo and X. Ren, Stock movement prediction with sentiment analysis based on deep learning networks, Concurrency and Computation: Practice and Experience 33(6) (2021) e6076. Crossref, Web of Science, Google Scholar