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

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  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
  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
  notes =        "See also International MultiConference of Engineers
                 and Computer Scientists 2008, 19-21 March, Hong Kong

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

Genetic Programming entries for Ramin Rajabioun Ashkan Rahimi-Kian