Directional prediction of stock prices using breaking news on Twitter | Zendy

Hana Alostad | Zendy; Hasan Davulcu | Zendy

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Directional prediction of stock prices using breaking news on Twitter

Author(s) -

Hana Alostad,

Hasan Davulcu

Publication year - 2017

Publication title -

web intelligence

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.163

H-Index - 23

eISSN - 2405-6464

pISSN - 2405-6456

DOI - 10.3233/web-170349

Subject(s) - breakout , stock (firearms) , drawdown (hydrology) , computer science , econometrics , stock price , technical analysis , momentum (technical analysis) , economics , financial economics , geology , history , paleontology , geotechnical engineering , series (stratigraphy) , aquifer , groundwater , archaeology

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—stock prediction; text mining; breaking news; twitter analysis; twitter volume spike; stock trading

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