Development of a genetic programming based GA methodology for the prediction of short to medium term stock markets
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 (GEP FAMR) 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.
2016 IEEE Congress on Evolutionary Computation (CEC)