A stock price prediction model by using genetic network programming

Shigeo Mori*, Kotaro Hirasawa, Jinglu Hu

*この研究の対応する著者

研究成果: Paper査読

抄録

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.

本文言語English
ページ1477-1482
ページ数6
出版ステータスPublished - 2004 12 1
イベントSICE Annual Conference 2004 - Sapporo, Japan
継続期間: 2004 8 42004 8 6

Conference

ConferenceSICE Annual Conference 2004
国/地域Japan
CitySapporo
Period04/8/404/8/6

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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