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

Shigeo Mori, Kotaro Hirasawa, Takayuki Furuzuki

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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
Volume125
Issue number4
DOIs
Publication statusPublished - 2005

Fingerprint

Genetic algorithms
Data mining
Numerical analysis
Industry

Keywords

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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

The Stock Price Prediction and Sell-buy Strategy Model by Genetic Network Programming. / Mori, Shigeo; Hirasawa, Kotaro; Furuzuki, Takayuki.

In: IEEJ Transactions on Electronics, Information and Systems, Vol. 125, No. 4, 2005, p. 631-636.

Research output: Contribution to journalArticle

@article{2e61ff9d8a7943c3922ad55c126c221d,
title = "The Stock Price Prediction and Sell-buy Strategy Model by Genetic Network Programming",
abstract = "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.",
keywords = "Evolutionary Computation, GA, GNP, GP, Sell-buy strategy, Stock price prediction",
author = "Shigeo Mori and Kotaro Hirasawa and Takayuki Furuzuki",
year = "2005",
doi = "10.1541/ieejeiss.125.631",
language = "English",
volume = "125",
pages = "631--636",
journal = "IEEJ Transactions on Electronics, Information and Systems",
issn = "0385-4221",
publisher = "The Institute of Electrical Engineers of Japan",
number = "4",

}

TY - JOUR

T1 - The Stock Price Prediction and Sell-buy Strategy Model by Genetic Network Programming

AU - Mori, Shigeo

AU - Hirasawa, Kotaro

AU - Furuzuki, Takayuki

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

KW - Evolutionary Computation

KW - GA

KW - GNP

KW - GP

KW - Sell-buy strategy

KW - Stock price prediction

UR - http://www.scopus.com/inward/record.url?scp=34547343986&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34547343986&partnerID=8YFLogxK

U2 - 10.1541/ieejeiss.125.631

DO - 10.1541/ieejeiss.125.631

M3 - Article

AN - SCOPUS:34547343986

VL - 125

SP - 631

EP - 636

JO - IEEJ Transactions on Electronics, Information and Systems

JF - IEEJ Transactions on Electronics, Information and Systems

SN - 0385-4221

IS - 4

ER -