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

Shigeo Mori, Kotaro Hirasawa, Jinglu Hu

研究成果: Article査読

11 被引用数 (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.

ジャーナルIEEJ Transactions on Electronics, Information and Systems
出版ステータスPublished - 2005

ASJC Scopus subject areas

  • 電子工学および電気工学


「The Stock Price Prediction and Sell-buy Strategy Model by Genetic Network Programming」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。