@inproceedings{7e2040917a3f4f90a22d23d66e18f6f8,
title = "Directional prediction of stock prices using breaking news on twitter",
abstract = "Stock market news and investing tips are popular topics in Twitter. In this paper, first we utilize a 5-year financial news corpus comprising over 50,000 articles collected from the NASDAQ website for the 30 stock symbols in Dow Jones Index (DJI) to train a directional stock price prediction system based on news content. Then we proceed to prove that information in articles indicated by breaking Tweet volumes leads to a statistically significant boost in the hourly directional prediction accuracies for the prices of DJI stocks mentioned in these articles. Secondly, we show that using document-level sentiment extraction does not yield to a statistically significant boost in the directional predictive accuracies in the presence of other 1-gram keyword features.",
keywords = "Breaking news, Stock prediction, Stock trading, Text mining, Twitter analysis, Twitter volume spike",
author = "Hana Alostad and Hasan Davulcu",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT Workshops 2015 ; Conference date: 06-12-2015 Through 09-12-2015",
year = "2016",
month = feb,
day = "2",
doi = "10.1109/WI-IAT.2015.82",
language = "English (US)",
series = "Proceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "523--530",
booktitle = "Proceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015",
}