A stock price prediction model by using genetic network programming

Shigeo Mori*, Kotaro Hirasawa, Jinglu Hu

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review


A new stock price prediction model is proposed based on Genetic Network Programming (GNP), i.e., an evolutionary computation recently developed. In the proposed prediction model, GNP is applied to searching for an optimal combination of two or more appropriate stock price indices, which is different from a conventional GA or GP based stock price prediction model, where GA or GP is usually used as an optimization technique to search for an optimal value of parameters in the stock price index. In this paper, a combination of several indices is shown to be more effective than a single index, because the most effective index usually differs from one brand to another. A series of simulation studies are carried out to confirm the effectiveness of the proposed new model.

Original languageEnglish
Number of pages6
Publication statusPublished - 2004 Dec 1
EventSICE Annual Conference 2004 - Sapporo, Japan
Duration: 2004 Aug 42004 Aug 6


ConferenceSICE Annual Conference 2004

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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