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dc.contributor.authorAlghieth, Manalen
dc.contributor.authorYang, Yingjieen
dc.contributor.authorChiclana, Franciscoen
dc.date.accessioned2016-04-14T13:27:51Z
dc.date.available2016-04-14T13:27:51Z
dc.date.issued2016
dc.identifier.citationAlghieth, M., Yang, Y. and Chiclana, F. (2016) Development of a Genetic Programming-based GA Methodology for the Prediction of Short-to-Medium-term Stock Markets. Accepted for presentation at the IEEE CEC 2016 (WCCI 2016).en
dc.identifier.urihttp://hdl.handle.net/2086/11896
dc.description.abstractThis 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 95.96% for short-term 5-day and 95.35% for medium-term 56-day trading periods.en
dc.language.isoenen
dc.publisherIEEEen
dc.titleDevelopment of a Genetic Programming-based GA Methodology for the Prediction of Short-to-Medium-term Stock Marketsen
dc.typeConferenceen
dc.identifier.doihttp://dx.doi.org/10.1109/CEC.2016.7744083
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderNAen
dc.projectidNAen
dc.cclicenceCC-BY-NCen


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