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Stock Trading by Modelling Price Trend with Dynamic Bayesian Networks

  • O Jangmin
  • Jae Won Lee
  • Sung-Bae Park
  • Byoung-Tak Zhang
Part of the Lecture Notes in Computer Science book series (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.

Keywords

Stock Prex Trading Performance Hide State Dynamic Bayesian Network Closing Price 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • O Jangmin
    • 1
  • Jae Won Lee
    • 2
  • Sung-Bae Park
    • 1
  • Byoung-Tak Zhang
    • 1
  1. 1.School of Computer Science and EngineeringSeoul National UniversitySeoulKorea
  2. 2.School of Computer Science and EngineeringSungshin Women’s UniversitySeoulKorea

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