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Using historic data from the period between January 2004 and February 2011, we detect increases in Google search volumes for keywords relating to financial markets before stock market falls

Quantifying trading behavior in financial markets using Google Trends.

Scientific reports, no. 1 (2013): 1684-1684

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摘要

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of...更多

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简介
  • Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises.
  • The authors investigate the intriguing possibility of analyzing search query data from Google Trends to provide new insights into the information gathering process that precedes the trading decisions recorded in the stock market data.
  • Further studies exploiting the temporal dimension of Google Trends data have demonstrated that changes in query volumes for selected search terms mirror changes in current numbers of influenza cases[32] and current volumes of stock market transactions[33].
  • The authors' results suggest that, following this logic, during the period 2004 to 2011 Google Trends search query volumes for certain terms could have been used in the construction of profitable trading strategies
重点内容
  • Crises in financial markets affect humans worldwide
  • By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as ‘‘early warning signs’’ of stock market moves
  • We investigate the intriguing possibility of analyzing search query data from Google Trends to provide new insights into the information gathering process that precedes the trading decisions recorded in the stock market data
  • We included terms related to the concept of stock markets, with some terms suggested by the Google Sets service, a tool which identifies semantically related keywords
  • Using historic data from the period between January 2004 and February 2011, we detect increases in Google search volumes for keywords relating to financial markets before stock market falls
  • Our results suggest that these warning signs in search volume data could have been exploited in the construction of profitable trading strategies
方法
  • The authors quantify financial relevance by calculating the frequency of each search term in the online edition of the Financial Times from August 2004 to June 2011, normalized by the number of Google hits for each search term.
  • The authors retrieved search volume data by accessing the Google Trends website on 10 April 2011, 17 April 2011, and 24 April 2011.
  • The data on the number of hits for search terms in the online edition of the Financial Times was retrieved on 7 June 2011.
  • The numbers of Google hits for these terms were obtained on 8 June 2011
结果
  • The authors analyze the performance of a set of 98 search terms.
  • Keyword with an obvious semantic connection to the most recent financial crisis, and overall the term which performed best in the analyses.
  • To uncover the relationship between the volume of search queries for a specific term and the overall direction of trader decisions, the authors analyze closing prices p(t) of the Dow Jones Industrial Average (DJIA) on the first trading day of week t.
  • The variability of Google Trends data across different dates of access is irrelevant for the results, and it can be shown that the data are consistent with reported real world events
结论
  • The authors' results are consistent with the suggestion that during the period the authors investigate, Google Trends data did reflect aspects of the current state of the economy, but may have provided some insight into future trends in the behavior of economic actors.
  • Using historic data from the period between January 2004 and February 2011, the authors detect increases in Google search volumes for keywords relating to financial markets before stock market falls.
  • The authors suggest that Google Trends data and stock market data may reflect two subsequent stages in the decision making process of investors.
  • Trends to sell on the financial market at lower prices may be preceded by periods of concern.
  • It is conceivable that such behavior may have historically been reflected by increased Google Trends search volumes for terms of higher financial relevance
基金
  • This work was partially supported by the German Research Foundation Grant PR 1305/1-1 (to T.P.)
  • This work was also supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC00285 and by the National Science Foundation (NSF), the Office of Naval Research (ONR), and the Defense Threat Reduction Agency (DTRA)
引用论文
  • Axtell, R. L. Zipf distribution of US firm sizes. Science 293, 1818–1820 (2001).
    Google ScholarLocate open access versionFindings
  • King, G. Ensuring the Data-Rich Future of the Social Sciences. Science 331, 719–721 (2011).
    Google ScholarLocate open access versionFindings
  • Vespignani, A. Predicting the Behavior of Techno-Social Systems. Science 325, 425–428 (2009).
    Google ScholarLocate open access versionFindings
  • Lazer, D. et al. Computational Social Science. Science 323, 721–723. (2009).
    Google ScholarLocate open access versionFindings
  • Perc, M. Evolution of the most common English words and phrases over the centuries. J. R. Soc. Interface 9, 3323–3328 (2012).
    Google ScholarLocate open access versionFindings
  • Petersen, A. M., Tenenbaum, J. N., Havlin, S., Stanley, H. E. & Perc, M. Languages cool as they expand: Allometric scaling and the decreasing need for new words. Scientific Reports 2, 943 (2012).
    Google ScholarLocate open access versionFindings
  • Christakis, N. A. & Fowler, J. H. Connected: The surprising power of our social networks and how they shape our lives (Little, Brown and Company, 2009).
    Google ScholarFindings
  • Fehr, E. Behavioural science - The economics of impatience. Nature 415, 269–272 (2002).
    Google ScholarLocate open access versionFindings
  • Shleifer, A. Inefficient Markets: An Introduction to Behavioral Finance (Oxford University Press, Oxford, 2000).
    Google ScholarFindings
  • Lillo, F., Farmer, J. D. & Mantegna, R. N. Econophysics - Master curve for priceimpact function. Nature 421, 129–130 (2003).
    Google ScholarLocate open access versionFindings
  • Gabaix, X., Gopikrishnan, P., Plerou, V. & Stanley, H. E. A theory of power-law distributions in financial market fluctuations. Nature 423, 267–270 (2003).
    Google ScholarLocate open access versionFindings
  • Preis, T., Kenett, D. Y., Stanley, H. E., Helbing, D. & Ben-Jacob, E. Quantifying the Behavior of Stock Correlations Under Market Stress. Scientific Reports 2, 752 (2012).
    Google ScholarLocate open access versionFindings
  • Preis, T., Schneider, J. J. & Stanley, H. E. Switching processes in financial markets. PNAS 108, 7674–7678 (2011).
    Google ScholarLocate open access versionFindings
  • Preis, T. Econophysics - complex correlations and trend switchings in financial time series. European Physical Journal Special Topics 194, 5–86 (2011).
    Google ScholarLocate open access versionFindings
  • Bunde, A., Schellnhuber, H. J. & Kropp, J., eds. The Science of Disasters: Climate Disruptions, Heart Attacks, and Market Crashes (Springer, Berlin, 2002).
    Google ScholarFindings
  • Vandewalle, N. & Ausloos, M. Coherent and random sequences in financial fluctuations. Physica A 246, 454–459 (1997).
    Google ScholarLocate open access versionFindings
  • Podobnik, B., Horvatic, D., Petersen, A. M. & Stanley, H. E. Cross-correlations between volume change and price change. PNAS 106, 22079–22084 (2009).
    Google ScholarLocate open access versionFindings
  • Sornette, D., Woodard, R. & Zhou, W. X. The 2006-2008 oil bubble: Evidence of speculation, and prediction. Physica A 388, 1571–1576. (2009).
    Google ScholarLocate open access versionFindings
  • Watanabe, K., Takayasu, H. & Takayasu, M. A mathematical definition of the financial bubbles and crashes. Physica A 383, 120–124 (2007).
    Google ScholarLocate open access versionFindings
  • Bouchaud, J. P., Matacz, A. & Potters, M. Leverage effect in financial markets: the retarded volatility model. Physical Review Letters 87, 228701 (2001).
    Google ScholarLocate open access versionFindings
  • Hommes, C. H. Modeling the stylized facts in finance through simple nonlinear adaptive systems. PNAS 99, 7221–7228 (2002).
    Google ScholarLocate open access versionFindings
  • Haldane, A. G. & May, R. M. Systemic risk in banking ecosystems. Nature 469, 351–355 (2011).
    Google ScholarLocate open access versionFindings
  • Lux, T. & Marchesi, M. Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397, 498–500 (1999).
    Google ScholarLocate open access versionFindings
  • Krugman, P. The Self-Organizing Economy (Blackwell, Cambridge, Massachusetts, 1996).
    Google ScholarFindings
  • Sornette, D. & von der Becke, S. Complexity clouds finance-risk models. Nature 471, 166 (2011).
    Google ScholarLocate open access versionFindings
  • Schweitzer, F. et al. Economic Networks: The New Challenges. Science 325, 422–425 (2009).
    Google ScholarLocate open access versionFindings
  • Garlaschelli, D., Caldarelli, G. & Pietronero, L. Universal scaling relations in food webs. Nature 423, 165–168 (2003).
    Google ScholarLocate open access versionFindings
  • Onnela, J. P., Arbesman, S., Gonzalez, M. C., Barabasi, A. L. & Christakis, N. A. Geographic Constraints on Social Network Groups. PLoS One 6, e16939 (2011).
    Google ScholarLocate open access versionFindings
  • Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E. & Havlin, S. Catastrophic cascade of failures in interdependent networks. Nature 464, 1025–1028 (2010).
    Google ScholarLocate open access versionFindings
  • Simon, H. A. A behavioral model of rational choice. Quarterly Journal of Economics 69, 99–118 (1955).
    Google ScholarLocate open access versionFindings
  • Mondria, J., Wu, T. & Zhang, Y. The determinants of international investment and attention allocation: Using internet search query data. Journal of International Economics 82, 85–95 (2010).
    Google ScholarLocate open access versionFindings
  • Ginsberg, J. et al. Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014 (2009).
    Google ScholarLocate open access versionFindings
  • Preis, T., Reith, D. & Stanley, H. E. Complex dynamics of our economic life on different scales: insights from search engine query data. Phil. Trans. R. Soc. A 368, 5707–5719 (2010).
    Google ScholarLocate open access versionFindings
  • Bordino, I. et al. Web Search Queries Can Predict Stock Market Volumes. PLoS One 7, e40014 (2012).
    Google ScholarLocate open access versionFindings
  • Choi, H. & Varian, H. Predicting the Present with Google Trends. The Economic Record 88, 2–9 (2012).
    Google ScholarLocate open access versionFindings
  • Preis, T., Moat, H. S., Stanley, H. E. & Bishop, S. R. Quantifying the Advantage of Looking Forward. Scientific Reports 2, 350 (2012).
    Google ScholarLocate open access versionFindings
  • Kendall, M. A New Measure of Rank Correlation. Biometrika 30, 81–89 (1938).
    Google ScholarLocate open access versionFindings
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