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Predicting Asset Value through Twitter Buzz

  • Xue Zhang
  • Hauke Fuehres
  • Peter A. Gloor
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (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.

Keywords

Stock Market Granger Causality Earthquake Early Warning Currency Exchange Rate Twitter Data 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xue Zhang
    • 1
    • 2
  • Hauke Fuehres
    • 2
  • Peter A. Gloor
    • 2
  1. 1.Department of Mathematic and Systems ScienceNational University of Defense TechnologyChangshaP.R.China
  2. 2.MIT Center for Collective IntelligenceCambridgeUSA

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