Development of 2D curve-fitting genetic/gene-expression programming technique for efficient time-series financial forecasting

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@InProceedings{Alghieth:2015:INISTA,
  author =       "Manal Alghieth and Yingjie Yang and 
                 Francisco Chiclana",
  booktitle =    "2015 International Symposium on Innovations in
                 Intelligent SysTems and Applications (INISTA)",
  title =        "Development of {2D} curve-fitting
                 genetic/gene-expression programming technique for
                 efficient time-series financial forecasting",
  year =         "2015",
  abstract =     "Stock market prediction is of immense interest to
                 trading companies and buyers due to high profit
                 margins. Therefore, precise prediction of the measure
                 of increase or decrease of stock prices also plays an
                 important role in buying/selling activities. 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
                 research presented in this paper aims at the modelling
                 and prediction of short-to-medium term stock value
                 fluctuations in the market via genetically tuned stock
                 market parameters. The technique uses hierarchically
                 defined GP and GEP techniques to tune algebraic
                 functions representing the fittest equation for stock
                 market activities. The proposed methodology is
                 evaluated against five well-known stock market
                 companies with each having its own trading
                 circumstances during the past 20+ years. The proposed
                 GEP/GP methodologies were evaluated based on variable
                 window/population sizes, selection methods, and
                 Elitism, Rank and Roulette selection methods. The
                 Elitism-based approach showed promising results with a
                 low error-rate in the resultant pattern matching with
                 an overall accuracy of 93.46percent for short-term
                 5-day and 92.105 for medium-term 56-day trading
                 periods.",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  DOI =          "doi:10.1109/INISTA.2015.7276734",
  month =        sep,
  notes =        "Fac. of Technol., De Montfort Univ., Leicester,
                 UK

                 Also known as \cite{7276734}",
}

Genetic Programming entries for Manal Alghieth Yingjie Yang Francisco Chiclana

Citations