The Stock Price Prediction and Sell-buy Strategy Model by Genetic Network Programming

Shigeo Mori, Kotaro Hirasawa, Jinglu Hu

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Various stock prices predicting and sell-buy strategy models have been so far proposed. They are classified as the fundamental analysis using the achievements of the companies and the trend of business, etc., and the technical analysis which carries out the numerical analysis of the movement of stock prices. On the other hand, as one of the methods for data mining which finds out the regularity from a vast quantity of stock price data, Genetic Algorithm (GA) has been so far applied widely. As a concrete example, the optimal values of parameters of stock indices like various moving averages and rates of deviation, etc. is computed by GA, and there have been developed various methods for predicting stock prices and determinig sell-buy strategy based on it. However, it is hard to determine which is the most effective index by the conventional GA. Moreover, the most effective one depends on the brands. So in this paper, a stock price prediction and sell-buy strategy model which searches for the optimal combination of various indices in the technical analysis has been proposed using Genetic Network programming and its effectiveness is confirmed by simulations.

Original languageEnglish
Pages (from-to)631-636
Number of pages6
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number4
Publication statusPublished - 2005


  • Evolutionary Computation
  • GA
  • GNP
  • GP
  • Sell-buy strategy
  • Stock price prediction

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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