Skip to main content
SpringerLink
Log in

A Causality Analysis of Societal Risk Perception and Stock Market Volatility in China

  • Published:
Journal of Systems Science and Systems Engineering Aims and scope Submit manuscript

Abstract

Modern China is undergoing a variety of social conflicts as the arrival of new era with the transformation of the principal contradiction. Then monitoring the society stable is a huge workload. Online societal risk perception is acquired by mapping on-line public concerns respectively into societal risk events including national security, economy & finance, public morals, daily life, social stability, government management, and resources & environment, and then provides one kind of measurement toward the society state. Obviously, stable and harmonious social situations are the basic guarantee for the healthy development of the stock market. Thus we concern whether the variations of the societal risk are related to stock market volatility. We study their relationships by two steps, first the relationships between search trends and societal risk perception; next the relationships between societal risk perception and stock volatility. The weekend and holiday effects in China stock market are taken into consideration. Three different econometric methods are explored to observe the impacts of variations of societal risk on Shanghai Composite Index and Shenzhen Composite Index. 3 major findings are addressed. Firstly, there exist causal relations between Baidu Index and societal risk perception. Secondly, the perception of finance & economy, social stability, and government management has distinguishing effects on the volatility of both Shanghai Composite Index and Shenzhen Composite Index. Thirdly, the weekend and holiday effects of societal risk perception on the stock market are verified. The research demonstrates that capturing societal risk based on on-line public concerns is feasible and meaningful.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bai, M. (2017). Seize the new contradictions and solve the unbalanced and inadequate development problem. Price Theory & Practice, 11: 11–14 (in Chinese).

    Google Scholar 

  2. Ball, D.J. & Boehmer–Christiansen, S. (2007). Societal concerns and risk decisions. Journal of Hazardous Materials, 144(1): 556–563.

    Article  Google Scholar 

  3. Bank, M., Larch, M. & Peter, G. (2011). Google search volume and its influence on liquidity and returns of German stocks. Financial Markets and Portfolio Management, 25(3): 239–264.

    Article  Google Scholar 

  4. Chen, W.Z., Qiu, Y.Q. & Ni, Z. (2014). Is the stock market a barometer of the economy? Review of Investment Studies, 33(10): 31–40 (in Chinese).

    Google Scholar 

  5. Cheng, S.H. & Zhang, B. (2014). Limited attention of investors and IPO performances: a study based on Baidu Index. Journal of Finance and Economics, 29(06): 54–63 (in Chinese).

    Google Scholar 

  6. Choi, H. & Varian, H.R. (2010). Predicting initial claims for unemployment benefits. Google Inc., 1–5.

    Google Scholar 

  7. Choi, H. & Varian, H. (2012). Predicting the present with Google trends. Economic Record, 88(s1): 2–9.

    Google Scholar 

  8. Curme, C. et al. (2014). Quantifying the semantics of search behavior before stock market moves. Proceedings of the National Academy of Sciences, 111(32): 11600–11605

    Article  Google Scholar 

  9. Da, Z., Engelberg, J. & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5): 1461–1499.

    Article  Google Scholar 

  10. Dong, Y.H. et al. (2015). Collective emotional reaction to societal risks in China. IEEE International Conference on Systems, Man, and Cybernetics: 557–562, Hong Kong, October 9–12, 2015, IEEE

    Book  Google Scholar 

  11. Gao, T.M. (2006). Econometric Analysis And Modeling. Tsinghua University Press, Beijing (in Chinese).

    Google Scholar 

  12. Ginsberg, J. et al. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232): 1012–1014

    Article  Google Scholar 

  13. Groenewold, N., Tang, S.H.K. & Wu, Y. (2003). The efficiency of the Chinese stock market and the role of the banks. Journal of Asian Economics, 14(4): 593–609.

    Article  Google Scholar 

  14. Hu, Y. & Tang, X.J. (2013). Using support vector machine for classification of Baidu hot word. In: Wang, M.Z. (ed.), International Conference on Knowledge Science, Engineering and Management), LNAI 8041: 580–590, Springer.

    Google Scholar 

  15. Jiang, Z.Q. et al. (2010). Bubble diagnosis and prediction of the 2005–2007 and 2008–2009 Chinese stock market bubbles. Journal of Economic Behavior & Organization, 74(3): 149–162.

    Article  Google Scholar 

  16. Lleo, S. & Ziemba, W.T. (2017). A tale of two indexes: Predicting equity market downturns in China. SRC Discussion Paper, No 72. Systemic Risk Centre. The London School of Economics and Political Science, London, UK.

    Google Scholar 

  17. Qu, L., Yang G. & Pan D. (2017). An empirical analysis of the chronergy of the impact of Web search volume on the premiere box office. In: Chen, J. et al. (eds.), Knowledge and Systems Sciences. CCIS 780: 162–174, Springer.

    Google Scholar 

  18. Sudhof, M. et al. (2014). Sentiment expression conditioned by affective transitions and social forces. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 1136–1145.

    Book  Google Scholar 

  19. Takeda, F. & Wakao, T. (2014). Google search intensity and its relationship with returns and trading volume of Japanese stocks. Pacific–Basin Finance Journal, 27: 1–18.

    Google Scholar 

  20. Tang, X.J. (2013). Exploring on–line societal risk perception for harmonious society measurement. Journal of Systems Science and Systems Engineering, 22(4): 469–486.

    Article  Google Scholar 

  21. Thomas, D. & Jank, S. (2016). Can Internet search queries help to predict stock market volatility? European Financial Management, 22(2): 171–192.

    Article  Google Scholar 

  22. Wu, D. & Tang, X.J. (2011). Preliminary analysis of Baidu hot words. In: Proceedings of the 11th Youth Conference on Systems Science and Management Science: 478–483 (in Chinese).

    Google Scholar 

  23. Xie, X.F. & Xu, L.C. (2002). Survey on public’s risk perception. Psychological Science, 25(6): 723–724 (in Chinese).

    Google Scholar 

  24. Xu, N. & Tang, X.J. (2017). Exploring effective methods for on–line societal risk classification and feature mining. In: Cheng, X.Q., Ma, W.Y., et al. (eds.), Chinese National Conference on Social Media Processing. CCIS 774: 65–76, Springer.

    Google Scholar 

  25. Yu, Q.J. & Zhang, B. (2012). Limited attention and stock performance: An empirical study using Baidu Index as the proxy for investor attention. Journal of Financial Research, 8: 152–165 (in Chinese).

    Google Scholar 

  26. Zhang, C. et al. (2012). A study on correlation between Web search data and CPI. In: Gaol, L. F. (ed.), Recent Progress in Data Engineering and Internet Technology. LNEE 157: 269–274, Springer.

    Google Scholar 

  27. Zhang, W. et al. (2013). Open source information, investor attention, and asset pricing. Economic Modelling, 33: 613–619.

    Article  Google Scholar 

  28. Zhang, Y. et al. (2016). Market reaction to Internet news: Information diffusion and price pressure. Economic Modelling, 56: 43–49.

    Article  Google Scholar 

  29. Zhang, Y. J. et al. (2018). Media coverage and trading volume: Evidence from Baidu media index. Systems Engineering–Theory & Practice, 38(3): 576–584 (in Chinese).

    Google Scholar 

  30. Zhao, B. et al. (2016). Stock market prediction exploiting microblog sentiment analysis. IEEE International Joint Conference on Neural Networks: 4482–4488, Vancouver, July 24–29, 2016, IEEE.

    Book  Google Scholar 

  31. Zhao, Y., Ye, Q. & Li, Z. (2014). The relationship between online attention and share prices. In: Proceedings of WHICEB 2014, International Conference on E–Business–Human Behavior and Social Impacts on E–Business: 462–469.

    Google Scholar 

  32. Zheng, R., Shi, K. & Li, S. (2009). The influence factors and mechanism of societal risk perception. In: Zhou, J. (ed.), International Conference on Complex Sciences: Theory and Applications. LNICST 5: 2266–2275, Shanghai, February 23–25, 2009, Springer.

    Google Scholar 

  33. Zhu, H.Q. et al. (2008). Causal linkages among Shanghai, Shenzhen, and Hong Kong stock market. International Journal of Theoretical & Applied Finance, 7(2): 135–149.

    Article  MATH  Google Scholar 

Download references

Acknowledgments

We sincerely thank all the reviewers for their valuable and professional comments and suggestions that have helped to improve the quality of the paper from KSS2017 publication to extensive modification for JSSSE.

Author information

Authors and Affiliations

  1. Academy ofMathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Nuo Xu & Xijin Tang

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Nuo Xu & Xijin Tang

Authors
  1. Nuo Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xijin Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xijin Tang.

Additional information

This research is supported by National Key Research and Development Program of China (2016YFB1000902) and National Natural Science Foundation of China (61473284 & 71731002).

Nuo Xu is a doctoral student in Academy of Mathematics and Systems Science, Chinese Academy of Sciences. She received her BSc (2013) on information and computing science from Northeast Electric Power University. Her research interests include text mining, sentiment analysis and opinion mining.

Xijin Tang is a full professor in the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. She received her BEng (1989) on computer science and engineering from Zhejiang University, MEng (1992) on management science and engineering from University of Science and Technology of China and PhD (1995) from CAS Institute of Systems Science. During her early system research and practice, she developed several decision support systems for water resources management, weapon system evaluation, e-commerce evaluation, etc. Her research always focuses on exploring a variety of decision support to wicked problem solving. Her recent interests are meta-synthesis and advanced modeling, opinion mining and opinion dynamics, social computing, decision support systems, etc. She was one of "30 recognized leaders in their areas of study" selected from about 200 nominations under an extensive and rigorous process of speaker nominations and selection to give the invited talk at the inaugural Systems Analysis 2015 Conference organized by International Institute for Applied Systems Analysis (IIASA), in partnership with the Santa Fe Institute, the Complexity Institute at Nanyang Technological University, and INFORMS, at IIASA during November 11–13, 2015.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, N., Tang, X. A Causality Analysis of Societal Risk Perception and Stock Market Volatility in China. J. Syst. Sci. Syst. Eng. 27, 613–631 (2018). https://doi.org/10.1007/s11518-018-5386-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11518-018-5386-4

Keywords

Search

Navigation