Relationship between Internet Search Data and Stock Return: Empirical Evidence from Chinese Stock Market

  • Ying Liu
  • Benfu Lv
  • Geng Peng
  • Chong Zhang
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 157)


Internet search data can be used for the study of market transaction behaviors. We firstly establish a concept framework to reveal the lead-lag relationship between search data and stock market based on micro-perspective of investors’ behaviors. Then we develop three types of composite search indices: investor action index, market condition index, and macroeconomic index. The empirical test indicates the cointegration relationship between search indices and the annual return rate of Shanghai composite index. In the long-term trend, each percentage point increase in the three types of search indices separately, the annual return rate will increase 0.22, 0.56, 0.83 percentage points in the next month. Furthermore, Granger causality test shows that the search indices have significant predictive power for the annual return rate of Shanghai composite index.


Stock Market Stock Return Granger Causality Granger Causality Test Percentage Point Increase 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kahneman, D.: Attention and Effort. Prentice-Hall, Englewood Cliffs (1973)Google Scholar
  2. 2.
    Barber, B.M., Odean, T.: All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors. Review of Financial Studies 21(2), 785–818 (2008)CrossRefGoogle Scholar
  3. 3.
    Hou, K., Lin, P., Wei, X.: A tale of two anomalies: The implications of investor attention for price and earnings momentum. Working Paper. Ohio State University and Princeton University (2008)Google Scholar
  4. 4.
    Ginsberg, Mohebbi, Patel, Brammer, Smolinski, Brilliant: Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014 (2009)CrossRefGoogle Scholar
  5. 5.
    Doornik, J.A.: Improving the Timeliness of Data on Influenza-like Illnesses using Google Search Data. Working Paper (2009)Google Scholar
  6. 6.
    Tierney, H.L.R., Pan, B.: A Poisson Regression Examination of the Relationship between Website Traffic and Search Engine Queries. Working Paper (2010)Google Scholar
  7. 7.
    Choi, H., Varian, H.: Predicting the Present with Google Trends, Working Paper, Technical Report. Google Inc. (2009)Google Scholar
  8. 8.
    Lynn, W., Erik, B.: The Future of Prediction—how google searches foreshadow housing prices and sales. Working Paper (2009)Google Scholar
  9. 9.
    Suhoy, T.: Query Indices and a 2008 Downturn: Israeli Data. Bank of Israel (2009)Google Scholar
  10. 10.
    Askitas, N., Zimmermann, K.F.: Google Econometrics and Unemployment Forecasting. Applied Economics Quarterly (2009)Google Scholar
  11. 11.
    Schmidt, T., Vosen, S.: Forecasting Private Consumption: Survey-based Indicators vs. Google Trends. Technische Universität Dortmund. Department of Economic and Social Sciences (2009)Google Scholar
  12. 12.
    Marta.: Consumption and Information: An Exploration of Theories of Consumer Behavior using Daily Data. Working paper (2009)Google Scholar
  13. 13.
    Moore, G.H., Shiskin, J.: Indicators of Business Expansions and Contractions. NBER Occasional Paper No. 103 (1967)Google Scholar
  14. 14.
    Boehm, E.A.: The Contribution of Economic Indicator Analysis to Understanding and Forecasting Business Cycles. Indian Economic Review 36, 1–36 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Management ScienceGraduate University of Chinese Academy of SciencesBeijingChina

Personalised recommendations