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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6861 LNCS, pp. 279–293). https://doi.org/10.1007/978-3-642-23091-2_24
Mendeley helps you to discover research relevant for your work.