A Genetic Programming Model for S\&P 500 Stock Market Prediction

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@Article{Sheta2013b,
  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",
  URL =          "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",
}

Genetic Programming entries for Alaa Sheta Hossam Faris Mouhammd Al-Kasassbeh

Citations