Abstract
Stock market is an important and active part of nowadays financial markets. Addressing the question as to how to model financial information from two sources, we focus on improving the accuracy of a computer aided prediction by combining information hidden in market news and stock prices in this study. Using the multi-kernel learning technique, a system is presented that makes predictions for the Hong Kong stock market by incorporating those two information sources. Experiments were conducted and the results have shown that in both cross validation and independent testing, our system has achieved better directional accuracy than those by the baseline system that is based on single one information source, as well as by the system that integrates information sources in a simple way.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fama, E.F.: The behavior of stock market prices. Journal of business 38(1) (1964)
Barberis, N., Thaler, R.: A survey of behavioral finance. Handbook of the Economics of Finance 1, 1053–1128 (2003)
Fung, G., Yu, J., Lam, W.: News sensitive stock trend prediction. Advances in Knowledge Discovery and Data Mining, 481–493 (2002)
Fung, G.P.C., Yu, J.X., Lu, H.: The predicting power of textual information on financial markets. IEEE Intelligent Informatics Bulletin 5(1), 1–10 (2005)
Wu, D., Fung, G., Yu, J., Liu, Z.: Integrating Multiple Data Sources for Stock Prediction. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 77–89. Springer, Heidelberg (2008)
Wu, D., Fung, G.P.C., Yu, J.X., Pan, Q.: Stock prediction: an event-driven approach based on bursty keywords. Frontiers of Computer Science in China 3(2), 145–157 (2009)
Ederington, L.H., Lee, J.H.: How markets process information: News releases and volatility. Journal of Finance 48(4), 1161–1191 (1993)
Engle, R.F., Ng, V.K.: Measuring and testing the impact of news on volatility. Journal of finance 48(5), 1749–1778 (1993)
Seo, Y.W., Giampapa, J., Sycara, K.: Financial news analysis for intelligent portfolio management. Robotics Institute, Carnegie Mellon University (2004)
Schumaker, R.P., Chen, H.: Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS) 27(2), 12 (2009)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 43(1), 59–69 (1982)
Kohonen, T., Somervuo, P.: Self-organizing maps of symbol strings. Neurocomputing 21(1-3), 19–30 (1998)
Ultsch, A.: Data mining and knowledge discovery with emergent self-organizing feature maps for multivariate time series. Kohonen Maps 46 (1999)
Fu, T., Chung, F.L., Ng, V., Luk, R.: Pattern discovery from stock time series using self-organizing maps. In: Workshop Notes of KDD2001 Workshop on Temporal Data Mining, pp. 26–29 (2001)
Smyth, P.J.: Hidden Markov models for fault detection in dynamic systems (November 7, 1995)
Pavlidis, T., Horowitz, S.L.: Segmentation of plane curves. IEEE Transactions on Computers 100(23), 860–870 (1974)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86 (2002)
Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of COLING, vol. 4, pp. 1367–1373 (2004)
Godbole, N., Srinivasaiah, M., Skiena, S.: Large-scale sentiment analysis for news and blogs. In: ICWSM 2007 (2007)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)
Schumaker, R.P., Chen, H.: A quantitative stock prediction system based on financial news. Information Processing & Management 45(5), 571–583 (2009)
Feldman, R., Sanger, J.: The text mining handbook (2007)
Dacorogna, M.M.: An introduction to high-frequency finance (2001)
Gidófalvi, G., Elkan, C.: Using news articles to predict stock price movements. In: Department of Computer Science and Engineering. University of California, San Diego (2001)
Tay, F.E.H., Cao, L.: Application of support vector machines in financial time series forecasting. Omega 29(4), 309–317 (2001)
Cao, L.J., Tay, F.E.H.: Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks 14(6), 1506–1518 (2004)
Sonnenburg, S., Rätsch, G., Henschel, S., Widmer, C., Behr, J., Zien, A., Bona, F., Binder, A., Gehl, C., Franc, V.: The SHOGUN machine learning toolbox. The Journal of Machine Learning Research 99, 1799–1802 (2010)
Ritter, J.R.: Behavioral finance. Pacific-Basin Finance Journal 11(4), 429–437 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, X., Wang, C., Dong, J., Wang, F., Deng, X., Zhu, S. (2011). Improving Stock Market Prediction by Integrating Both Market News and Stock Prices. In: Hameurlain, A., Liddle, S.W., Schewe, KD., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2011. Lecture Notes in Computer Science, vol 6861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23091-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-23091-2_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23090-5
Online ISBN: 978-3-642-23091-2
eBook Packages: Computer ScienceComputer Science (R0)