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 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

Get full access to this article

View all access options for this article.

References

Achrekar,

Gandhe,

Lazarus,

Yu and

Liu, Twitter improves seasonal influenza prediction, in: HEALTHINF 2012 – Proceedings of the International Conference on Health Informatics, 2012, pp. 61–70.

Asur and

Huberman, Predicting the future with social media, in: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Vol. 1, IEEE Computer Society, 2010, pp. 492–499. doi:10.1109/WI-IAT.2010.63.

Average true range, https://en.wikipedia.org/wiki/Average_true_range, Accessed: 2016-05-02.

Bollen,

Mao and

Zeng, Twitter mood predicts the stock market, Journal of Computational Science, 2(1) (2011), 1–8.

Bollinger bands, https://en.wikipedia.org/wiki/Bollinger_Bands, Accessed: 2016-04-29.

J.F.

Carter, Mastering the Trade: Proven Techniques for Profiting from Intraday and Swing Trading Setups, American Media International, 2007.

Compton,

Lee,

Xu,

Artieda-Moncada,

T.-C.

Lu,

Silva and

Macy, Using publicly visible social media to build detailed forecasts of civil unrest, Security Informatics 3 (2014), 4.

Drawdown (economics), https://en.wikipedia.org/wiki/Drawdown_(economics), Accessed: 2016-05-01.

Gong and

Sun, A new approach of stock price prediction based on logistic regression model, in: 2009 International Conference on New Trends in Information and Service Science NISS ’09, 2009, pp. 1366–1371. doi:10.1109/NISS.2009.267.

10.

Hagenau,

Michael and

Neumann, Automated news reading: Stock price prediction based on financial news using context-capturing features, Decision Support Systems and Electronic Commerce 55(3) (2013), 685–697.

11.

M.Y.

Kaya and

M.E.

Karsligil, Stock price prediction using financial news articles, in: 2010 Second IEEE International Conference on Information and Financial Engineering (ICIFE), IEEE, 2010, pp. 478–482. doi:10.1109/ICIFE.2010.5609404.

12.

Keltner channels, http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:keltner_channels, Accessed: 2016-04-29.

13.

Lauren and

S.D.

Harlili, Stock trend prediction using simple moving average supported by news classification, in: 2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA), IEEE, 2014, pp. 135–139.

14.

E.L.

Lehmann, Nonparametrics: Statistical Methods Based on Ranks, Holden-Day, San Francisco, 2006.

15.

Liu and

Setiono, Feature selection via discretization, IEEE Transactions on Knowledge and Data Engineering 9(4) (1997), 642–645.

16.

Liu,

Ji and

Ye, Slep: Sparse learning with efficient projections, Arizona State University, 2009.

17.

Loughran and

McDonald, When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks, The Journal of Finance 66(1) (2011), 35–65.

18.

Mao,

Wei and

Wang, Twitter volume spikes: Analysis and application in stock trading, in: Proceedings of the Seventh Workshop on Social Network Mining and Analysis, ACM, 2013, p. 4.

19.

Mao,

Wei,

Wang and

Liu, Correlating s&p 500 stocks with Twitter data, in: Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research, ACM, 2012, pp. 69–72. doi:10.1145/2392622.2392634.

20.

A.K.

Nassirtoussi,

Aghabozorgi,

T.Y.

Wah and

D.C.L.

Ngo, Text mining of news-headlines for FOREX market prediction: A multi-layer dimension reduction algorithm with semantics and sentiment, Expert Systems with Applications 42(1) (2015), 306–324.

21.

Patel, Predicting stock market index using fusion of machine learning techniques, Expert Systems with Applications 42(4) (2015), 2162–2172.

22.

Pukelsheim, The three sigma rule, The American Statistician 48(2) (1994), 88–91.

23.

Return on investment – roi, http://www.investopedia.com/terms/r/returnoninvestment.asp, Accessed: 2016-05-01.

24.

Return over maximum drawdown (romad), http://www. investopedia.com/terms/r/return-over-maximum-drawdown- romad.asp, Accessed: 2016-05-01.

25.

S.S.

Roy,

Mittal,

Basu and

Abraham, Stock market forecasting using lasso linear regression model, in: Afro-European Conference for Industrial Advancement, Springer, 2015, pp. 371–381.

26.

Schumaker and

Chen, Textual analysis of stock market prediction using financial news articles, in: AMCIS 2006 Proceedings, 2006, p. 185.

27.

Squeeze momentum indicator, https://www.tradingview.com/ script/nqQ1DT5a-Squeeze-Momentum-Indicator-LazyBear, Accessed: 2016-04-29.

28.

Thelwall,

Buckley and

Paltoglou, Sentiment strength detection for the social web, Journal of the American Society for Information Science and Technology 63(1) (2012), 163–173.

29.

T.-T.

Vu,

Chang,

Q.T.

Ha and

Collier, An experiment in integrating sentiment features for tech stock, prediction in Twitter, 2012.

30.

Wang,

Brown and

Gerber, Spatio-temporal modeling of criminal incidents using geographic, demographic, and Twitter-derived information, in: International Conference on Intelligence and Security Informatics, Lecture Notes in Computer Science, IEEE Press, 2012. doi:10.1007/978-3-642-30428-6.