- author = "Alaa Sheta and Hossam Faris and Mouhammd Alkasassbeh",
- title = "A Genetic Programming Model for {S\&P 500} Stock Market Prediction",
- journal = "International Journal of Control and Automation",
- year = "2013",
- volume = "6",
- number = "5",
- pages = "303--314",
- keywords = "genetic algorithms, genetic programming",
- ISSN = "2005-4297",
- broken = "http://www.sersc.org/journals/IJCA/vol6_no6.php",
-
URL = "
http://www.sersc.org/journals/IJCA/vol6_no6/29.pdf",
-
URL = "
http://dx.doi.org/10.14257/ijca.2013.6.6.29",
- size = "12 pages",
- abstract = "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 Standard and 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 modelling. Such advantages include generating mathematical models, which are simple to evaluate and having powerful variable selection mechanism that identifies significant variables.",
-
notes = "Science & Engineering Research Support
soCiety
Management Office: 20 Virginia Court, Sandy Bay, Tasmania, Australia Phone no.: +61-3-9016-9027 Email: ijca@sersc.org",
