Development of a Genetic Programming-based GA Methodology for the Prediction of Short-to-Medium-term Stock Markets

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@InProceedings{Alghieth:2016:CEC,
  author =       "Manal Alghieth and Yingjie Yang and 
                 Francisco Chiclana",
  title =        "Development of a Genetic Programming-based GA
                 Methodology for the Prediction of Short-to-Medium-term
                 Stock Markets",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "2381--2388",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, gene
                 expressing programming, Stock market, Time series
                 financial forecasting",
  isbn13 =       "978-1-5090-0623-6",
  URL =          "https://www.dora.dmu.ac.uk/handle/2086/11896",
  DOI =          "doi:10.1109/CEC.2016.7744083",
  abstract =     "This research presents a specialised extension to the
                 genetic algorithms (GA) known as the genetic
                 programming (GP) and gene expression programming (GEP)
                 to explore and investigate the outcome of the GEP
                 criteria on the stock market price prediction. The aim
                 of this research is to model and predict
                 short-to-medium term stock value fluctuations in the
                 market via genetically tuned stock market parameters.
                 The technology proposes a fractional adaptive mutation
                 rate Elitism (GEPFAMR) technique to initiate a balance
                 between varied mutation rates and between
                 varied-fitness chromosomes, thereby improving
                 prediction accuracy and fitness improvement rate. The
                 methodology is evaluated against different dataset and
                 selection methods and showed promising results with a
                 low error-rate in the resultant pattern matching with
                 an overall accuracy of 95.96percent for short-term
                 5-day and 95.35percent for medium-term 56-day trading
                 periods.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Manal Alghieth Yingjie Yang Francisco Chiclana

Citations