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Predicting Asset Value through Twitter Buzz

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Advances in Collective Intelligence 2011

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 113))

Abstract

This paper describes early work trying to predict financial market movement such as gold price, crude oil price, currency exchange rates and stock market indicators by analyzing Twitter posts. We collected Twitter feeds for 5 months obtaining a large set of emotional retweets originating from within the US, from which six public opinion time series containing the keywords “dollar% t ”, “$% t ”, “gold% t ”, “oil% t , “job% t ” and “economy% t ” were extracted. Our results show that these variables are correlated to and even predictive of the financial market movement. Except “$% t ”, all other five public opinion time series are identified by a Granger-causal relationship with certain market movements. It is demonstrated that daily changes in the volume of economic topic retweeting seem to match the value shift occurring in the corresponding market next day.

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Author information

Authors and Affiliations

  1. Department of Mathematic and Systems Science, National University of Defense Technology, Changsha, Hunan, P.R.China

    Xue Zhang

  2. MIT Center for Collective Intelligence, Cambridge, MA, USA

    Xue Zhang, Hauke Fuehres & Peter A. Gloor

Authors
  1. Xue Zhang
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  2. Hauke Fuehres
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  3. Peter A. Gloor
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Editor information

Editors and Affiliations

  1. , College of Engineering, Seoul National University, Gwanak-Ro, Gwanak-Gu 599, Seoul, 151-742, Korea, Republic of (South Korea)

    Jörn Altmann

  2. , Fakultät für Wirtschaftwissenschaft, FernUniversität in Hagen, Universitätsstrasse 41, Hagen, 58097, Germany

    Ulrike Baumöl

  3. , Fakultät für Mathematik und Informatik, FernUniversität Hagen, Universitätsstrasse 27, Hagen, 58097, Germany

    Bernd J. Krämer

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© 2012 Springer-Verlag Berlin Heidelberg

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Zhang, X., Fuehres, H., Gloor, P.A. (2012). Predicting Asset Value through Twitter Buzz. In: Altmann, J., Baumöl, U., Krämer, B. (eds) Advances in Collective Intelligence 2011. Advances in Intelligent and Soft Computing, vol 113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25321-8_3

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  • DOI: https://doi.org/10.1007/978-3-642-25321-8_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25320-1

  • Online ISBN: 978-3-642-25321-8

  • eBook Packages: EngineeringEngineering (R0)

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