A Genetic Programming Model for S&P 500 Stock Market Prediction
Alaa F. Sheta, Hossam Faris, Mouhammd Sharari Alkasassbeh · International Journal of Control and Automation · 2013
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 shortterm 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.