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Modelling Movement of Stock Market Indexes with Data from Emoticons of Twitter Users

  • Alexander PorshnevEmail author
  • Ilya Redkin
  • Nikolay Karpov
Chapter
Part of the Communications in Computer and Information Science book series (CCIS, volume 505)

Abstract

The issue of using Twitter data to increase the prediction rate of stock price movements draws attention of many researchers. In this paper we examine the possibility of analyzing Twitter users’ emoticons to improve accuracy of predictions for DJIA and S&P500 stock market indices. We analyzed 1.6 billion tweets downloaded from February 13, 2013 to May 19, 2014. As a forecasting technique, we tested the Support Vector Machine (SVM), Neural Networks and Random Forest, which are commonly used for prediction tasks in finance analytics. The results of applying machine learning techniques to stock market price prediction are discussed.

Keywords

Prediction Emoticons DJIA S&P500 Twitter Mood Support vectors machine Neural networks Random forest Behavioral finance 

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Alexander Porshnev
    • 1
    Email author
  • Ilya Redkin
    • 1
  • Nikolay Karpov
    • 1
  1. 1.National Research University Higher School of EconomicsNizhny NovgorodRussia

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