Improving Stock Market Prediction by Integrating Both Market News and Stock Prices

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6861)


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.


Stock market prediction Information integration Multi-kernel learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of Computer ScienceCity University of Hong KongHong Kong
  2. 2.Shanghai Key Lab of Intelligent Information Processing and School of Computer ScienceFudan UniversityShanghaiChina
  3. 3.School of Computer and State Key Lab of Software EngineeringWuhan UniversityWuhanChina
  4. 4.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK

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