Abstract
This study focuses on the use of machine learning algorithms to construct a model that can predict the movements of Bursa Malaysia stock prices. In this research, we concentrate on linguistics terms from financial news that can contribute movements of the prices. Our aim is to develop a prototype that can classify sentiments towards financial news for investment decision. We experimented with five blue-chip companies from different industries of the top market constituents in Bursa Malaysia KLCI. A total of 14,992 finance articles were crawled and used as the dataset. Support Vector Machine algorithm was employed and the accuracy recorded was at 56%. The findings of this research can be used to assist investors in investment decision making.
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References
Bucur, L., Florea, A.: Techniques for prediction in chaos–a comparative study on financial data. UPB Sci. Bull. Ser. C 73(3), 17–32 (2011)
Chan, S.W., Franklin, J.: A text-based decision support system for financial sequence prediction. Decis. Support Syst. 52(1), 189–198 (2011)
Dietterich, T.: Machine learning for sequential data: a review. Struct. Syntact. Stat. Pattern Recogn. 227–246 (2002)
Heidari, M., Felden, C.: Financial footnote analysis: developing a text mining approach. In: Proceedings of International Conference on Data Mining (DMIN), p. 10. The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), January 2015
Hagenau, M., Liebmann, M., Neumann, D.: Automated news reading: stock price prediction based on financial news using context-capturing features. Decis. Support Syst. 55(3), 685–697 (2013)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM computing surveys (CSUR) 31(3), 264–323 (1999)
Jiang, Z., Chen, P., Pan, X.: Announcement based stock prediction. In: 2016 International Symposium on Computer, Consumer and Control (IS3C), pp. 428–431. IEEE, July 2016
Koppel, M., Shtrimberg, I.: Good news or bad news? Let the market decide. In: Shanahan, J.G., Qu, Y., Wiebe, J. (eds.) Computing Attitude and Affect in Text: Theory and Applications, pp. 297–301. Springer, Dordrecht (2006). https://doi.org/10.1007/1-4020-4102-0_22
Kumar, B.S., Ravi, V.: A survey of the applications of text mining in financial domain. Knowl.-Based Syst. 114, 128–147 (2016). https://doi.org/10.1016/j.knosys.2016.10.003
Luss, R., d’Aspremont, A.: Predicting abnormal returns from news using text classification. Quant. Financ. 15(6), 999–1012 (2015)
Murphy, J.J.: Technical analysis of the financial markets: a comprehensive guide to trading methods and applications. Penguin (1999)
Schumaker, R., Chen, H.: Textual analysis of stock market prediction using financial news articles. In: AMCIS 2006 Proceedings, vol. 185 (2006)
Schumaker, R.P., Chen, H.: A quantitative stock prediction system based on financial news. Inf. Process. Manag. 45(5), 571–583 (2009)
Schumaker, R.P., Zhang, Y., Huang, C.N., Chen, H.: Evaluating sentiment in financial news articles. Decis. Support Syst. 53(3), 458–464 (2012)
Shynkevich, Y., McGinnity, T.M., Coleman, S.A., Belatreche, A.: Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning. Decis. Support Syst. 85, 74–83 (2016)
Acknowledgement
The authors would like to thank Faculty of Computer and Mathematical Sciences, and Research Management Centre of Universiti Teknologi MARA for supporting this research with LESTARI 111/2017 grant.
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Ab. Rahman, A.S., Abdul-Rahman, S., Mutalib, S. (2017). Mining Textual Terms for Stock Market Prediction Analysis Using Financial News. In: Mohamed, A., Berry, M., Yap, B. (eds) Soft Computing in Data Science. SCDS 2017. Communications in Computer and Information Science, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-10-7242-0_25
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DOI: https://doi.org/10.1007/978-981-10-7242-0_25
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