Modelling Movement of Stock Market Indexes with Data from Emoticons of Twitter Users
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.
KeywordsPrediction Emoticons DJIA S&P500 Twitter Mood Support vectors machine Neural networks Random forest Behavioral finance
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