Directional prediction of stock prices using breaking news on Twitter

Article type: Research Article

Authors: Alostad, Hana^{; *} | Davulcu, Hasan

Affiliations: School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona 85287–8809, USA. E-mails: Hana.Alostad@asu.edu, Hasan.Davulcu@asu.edu

Correspondence: [*] Corresponding author. E-mail: Hana.Alostad@asu.edu.

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 NASDAQ website matching 30 stock components in Dow Jones Index (DJI) to train a directional stock price prediction system based on news content. Next, we proceed to show that information in articles indicated by breaking Tweet volumes leads to a statistically significant boost in hourly directional prediction accuracies for the DJI stock prices mentioned in these articles. Secondly, we show that using document-level sentiment extraction does not yield a statistically significant boost in the directional predictive accuracies in the presence of other 1-gram keyword features. Thirdly we test the performance of the system on several time-frames and identify 4 hour time-frame for both the price charts and for Tweet breakout detection as the best time-frame. Finally, we develop a set of price momentum based trade exit rules to cut losing trades early and to allow winning trades run longer. We show that the Tweet volume breakout based trading system with price momentum based exit rules not only improve the winning accuracies and the return on investment, but they also lower the maximum drawdown and achieve highest overall return over maximum drawdown.

Keywords: Stock prediction, text mining, breaking news, Twitter analysis, Twitter volume spikes

DOI: 10.3233/WEB-170349

Journal: Web Intelligence, vol. 15, no. 1, pp. 1-17, 2017