Bayesian Network Based Prediction Algorithm of Stock Price Return

  • Yi Zuo
  • Masaaki Harada
  • Takao Mizuno
  • Eisuke Kita
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 15)

Abstract

This paper describes the stock price return prediction using Bayesian network. The Bayesian network gives the probabilistic graphical model that represents previous stock price returns and their conditional dependencies via a directed acyclic graph.When the stock price is taken as the stochastic variable, the Bayesian network gives the conditional dependency between the past and future stock prices. In the present algorithm, the stock price return distribution is transformed to the discrete values set by using Ward method, which is one of the clustering algorithms. The Bayesian network gives the conditional dependency between the past and future stock prices. The stock price is determined from the discrete value set of the stock prices so that its occurrence probability is maximized. Finally, the present algorithm is compared with the traditional time-series prediction algorithms in the TOYOTA motor corporation stock price prediction. The present algorithm show 20% better than the time-series prediction algorithms.

Keywords

Bayesian Network Stock Price Prediction Algorithm Stock Prex Present Algorithm 
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 2012

Authors and Affiliations

  • Yi Zuo
    • 1
  • Masaaki Harada
    • 1
  • Takao Mizuno
    • 1
  • Eisuke Kita
    • 1
  1. 1.Graduate School of Information ScienceNagoya UniversityNagoyaJapan

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