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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2004: Intelligent Data Engineering and Automated Learning – IDEAL 2004 pp 794–799Cite as

Stock Trading by Modelling Price Trend with Dynamic Bayesian Networks

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3177))

Abstract

We study a stock trading method based on dynamic bayesian networks to model the dynamics of the trend of stock prices. We design a three level hierarchical hidden Markov model (HHMM). There are five states describing the trend in first level. Second and third levels are abstract and concrete hidden Markov models to produce the observed patterns. To train the HHMM, we adapt a semi-supervised learning so that the trend states of first layer is manually labelled. The inferred probability distribution of first level are used as an indicator for the trading signal, which is more natural and reasonable than technical indicators. Experimental results on representative 20 companies of Korean stock market show that the proposed HHMM outperforms a technical indicator in trading performances.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Seoul National University, San 56-1, Shillim-dong, Kwanak-gu, Seoul, Korea, 151-744

    O Jangmin, Sung-Bae Park & Byoung-Tak Zhang

  2. School of Computer Science and Engineering, Sungshin Women’s University, Dongsun-dong, Sungbuk-gu, Seoul, Korea, 136-742

    Jae Won Lee

Authors
  1. O Jangmin
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  2. Jae Won Lee
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  3. Sung-Bae Park
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  4. Byoung-Tak Zhang
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Editor information

Editors and Affiliations

  1. School of Engineering, Computing, and Mathematics, University of Exeter, EX4 4QF, Exeter, UK

    Zheng Rong Yang

  2. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  3. School of Engineering, Computer Science and Mathematics, University of Exeter, EX4 4QF, UK

    Richard M. Everson

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© 2004 Springer-Verlag Berlin Heidelberg

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Jangmin, O., Lee, J.W., Park, SB., Zhang, BT. (2004). Stock Trading by Modelling Price Trend with Dynamic Bayesian Networks. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_118

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  • DOI: https://doi.org/10.1007/978-3-540-28651-6_118

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22881-3

  • Online ISBN: 978-3-540-28651-6

  • eBook Packages: Springer Book Archive

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