Earticle

초록

영어

The stock market is considered one of the most standard investments due to its high revenues. Stock market investment can be risky due to its unpredictable activities. That is why, there is an urgent need to develop intelligent models to predict the for stock market index to help managing the economic activities. In the literature, several models have been proposed to give either short- term or long-term prediction, but what makes these models supersede the others is the accuracy of their prediction. In this paper, a prediction model for the Standards & Poors 500 (S&P500) index is proposed based Genetic Programming (GP). The experiments and analysis conducted in this research show some unique advantages of using GP over other soft computing techniques in stock market modeling. Such advantages include generating mathematical models, which are simple to evaluate and having powerful variable selection mechanism that identifies significant variables.

목차

Abstract
 1. Introduction
 2. Proposed Model Structure
 3. Linear Regression Model
 4. Fuzzy Modeling
 5. Genetic Programming
 6. Experiments Results
 7. Conclusions and Future Work
 References

저자

• Alaa Sheta [ A. F. Sheta is a Professor with the Computers and Systems Department, Electronics Research Institute (ERI), Cairo, Egypt. ]
• Hossam Farisy [ H. Faris is an Assistant Professor at Business Information Technology Department, The University of Jordan, Am- man, Jordan ]
• Mouhammd Alkasassbeh [ M. Alkasassbeh is an Assistant Professor with the Computer Science Department, Mutah University, Jordan ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605