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

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 6861)

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.

Keywords

  • Stock market prediction
  • Information integration
  • Multi-kernel learning

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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

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  • 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

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