A Genetic Programming Model for S\&P 500 Stock Market Prediction
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- @Article{Sheta2013b,
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author = "Alaa Sheta and Hossam Faris and Mouhammd Alkasassbeh",
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title = "A Genetic Programming Model for {S\&P 500} Stock
Market Prediction",
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journal = "International Journal of Control and Automation",
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year = "2013",
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volume = "6",
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number = "5",
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pages = "303--314",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2005-4297",
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broken = "http://www.sersc.org/journals/IJCA/vol6_no6.php",
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URL = "http://www.sersc.org/journals/IJCA/vol6_no6/29.pdf",
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URL = "http://dx.doi.org/10.14257/ijca.2013.6.6.29",
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size = "12 pages",
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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.",
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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