A Genetic Programming Based Stock Price Predictor together with Mean-Variance Based Sell/Buy Actions

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Rajabioun:2008:WCE,

• author = "Ramin Rajabioun and Ashkan Rahimi-Kian",
• title = "A Genetic Programming Based Stock Price Predictor together with Mean-Variance Based Sell/Buy Actions",
• booktitle = "Proceedings of the World Congress on Engineering, WCE 2008",
• year = "2008",
• editor = "S. I. Ao and Len Gelman and David WL Hukins and Andrew Hunter and A. M. Korsunsky",
• pages = "1136--1141",
• address = "London",
• month = "2-4 " # jul,
• organisation = "International Association of Engineers (IAENG)",
• publisher = "Newswood Limited",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-988-17012-3-7",
• URL = "http://www.iaeng.org/publication/WCE2008/WCE2008_pp1136-1141.pdf",
• abstract = "In this paper first a precise mathematical model is obtained for four competing or cooperating companies' stock prices and then the optimal buy/sell signals are ascertained for five different agents which are trading in a virtual market and are trying to maximize their wealth over one trading year period. The model is so that gives a good prediction of the next 30th day stock prices. The companies used in this modeling are all chosen from Boston Stock Market. Genetic Programming (GP) is used to produce the predictive mathematical model. The interaction among companies and the effect imposed by each of five agents on future stock prices are also considered in our modeling. Namely, we have chosen eight companies in order that there is some kind of interrelation among them. Comparison of the GP models with Artificial Neural Networks (ANN) and Neuro-Fuzzy Networks (trained by the LoLiMoT algorithm) shows the superior potential of GP in prediction. Using these models; five players, each with a specific strategy and all with one common goal (wealth maximization), start to trade in a virtual market. We have also relaxed the short-sales constraint in our work. Each of the agents has a different objective function and all are going to maximize themselves. We have used Particle Swarm Optimization (PSO) as an evolutionary optimization method for wealth maximization.",
• notes = "See also International MultiConference of Engineers and Computer Scientists 2008, 19-21 March, Hong Kong http://www.iaeng.org/IMECS2008/schedule/schedule.html

  and doi:10.1007/978-90-481-2311-7_45",

}

Genetic Programming entries for Ramin Rajabioun Ashkan Rahimi-Kian

Citations