- 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",
- size = "8 pages",
- 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
Centre for Computational Intelligence, Faculty of Technology, De Montfort University, Leicester, UK",
