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


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Alexander Porshnev , Ilya Redkin , Nikolay Karpov

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. More... »

PAGES

297-306

Book

TITLE

Information Retrieval

ISBN

978-3-319-25484-5
978-3-319-25485-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-25485-2_10

DOI

http://dx.doi.org/10.1007/978-3-319-25485-2_10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017627847


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