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Anticipating Stock Market Movements with Google and Wikipedia

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Nonlinear Phenomena in Complex Systems: From Nano to Macro Scale

Abstract

Many of the trading decisions that have led to financial crises are captured by vast, detailed stock market datasets. Here, we summarize two of our recent studies which investigate whether Internet usage data contain traces of attempts to gather information before such trading decisions were taken. By analyzing changes in how often Internet users searched for financially related information on Google (Preis et al., Sci Rep 3:1684, 2013) and Wikipedia (Moat et al., Sci Rep 3:1801, 2013), patterns are found that may be interpreted as “early warning signs” of stock market moves. Our results suggest that online data may allow us to gain new insight into early information gathering stages of economic decision making.

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Acknowledgements

The results summarized here were first reported in Preis, T., Moat, H.S., Stanley. H.E.: Quantifying trading behavior in financial markets using Google Trends. Sci. Rep. 3, 1684 (2013), http://www.nature.com/srep/2013/130425/srep01684/full/srep01684.html, and Moat, H.S. et al.: Quantifying Wikipedia usage patterns before stock market moves. Sci. Rep. 3, 1801 (2013), http://www.nature.com/srep/2013/130508/srep01801/full/srep01801.html. We thank Adam Avakian and Dror Y. Kenett for comments. H.S.M. and T.P. acknowledge the support of the Research Councils UK Grant EP/K039830/1. 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. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the U.S. Government.

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Authors and Affiliations

  1. Behavioural Science, Warwick Business School, University of Warwick, Scarman Road, Coventry, CV4 7AL, UK

    Helen Susannah Moat & Tobias Preis

  2. Center for Polymer Studies, Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, MA, 02215, USA

    Chester Curme

  3. Department of Physics, Boston University, Boston, MA, USA

    H. Eugene Stanley

Authors
  1. Helen Susannah Moat
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  2. Chester Curme
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  3. H. Eugene Stanley
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  4. Tobias Preis
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Correspondence to Helen Susannah Moat .

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Editors and Affiliations

  1. Turin Polytechnic University in Tashkent, Tashkent, Uzbekistan

    Davron Matrasulov

  2. Department of Physics, Boston University, Boston, Massachusetts, USA

    H. Eugene Stanley

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Moat, H.S., Curme, C., Stanley, H.E., Preis, T. (2014). Anticipating Stock Market Movements with Google and Wikipedia . In: Matrasulov, D., Stanley, H. (eds) Nonlinear Phenomena in Complex Systems: From Nano to Macro Scale. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8704-8_4

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  • DOI: https://doi.org/10.1007/978-94-017-8704-8_4

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  • Print ISBN: 978-94-017-8703-1

  • Online ISBN: 978-94-017-8704-8

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