DocumentCode
3740161
Title
Directional Prediction of Stock Prices Using Breaking News on Twitter
Author
Hana Alostad;Hasan Davulcu
Author_Institution
Sch. of Comput., Inf. &
Volume
1
fYear
2015
Firstpage
523
Lastpage
530
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
"Feature extraction","Twitter","Companies","Systems architecture","Indexes","Stock markets","Prediction algorithms"
Publisher
ieee
Conference_Titel
Web Intelligence and Intelligent Agent Technology (WI-IAT), 2015 IEEE / WIC / ACM International Conference on
Type
conf
DOI
10.1109/WI-IAT.2015.82
Filename
7396858
Link To Document