A portfolio optimization model using Genetic Network Programming with control nodes

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Yan Chen and Etsushi Ohkawa and Shingo Mabu and 
                 Kaoru Shimada and Kotaro Hirasawa",
  title =        "A portfolio optimization model using Genetic Network
                 Programming with control nodes",
  journal =      "Expert Systems with Applications",
  volume =       "36",
  number =       "7",
  pages =        "10735--10745",
  year =         "2009",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2009.02.049",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-4VPD6KS-2/2/3cf6750a5518ab6e7d6cf817197d96bd",
  keywords =     "genetic algorithms, genetic programming, Portfolio
                 optimization, Genetic Network Programming, Control
                 node, Reinforcement learning",
  abstract =     "Many evolutionary computation methods applied to the
                 financial field have been reported. A new evolutionary
                 method named 'Genetic Network Programming' (GNP) has
                 been developed and applied to the stock market
                 recently. The efficient trading rules created by GNP
                 has been confirmed in our previous research. In this
                 paper a multi-brands portfolio optimisation model based
                 on Genetic Network Programming with control nodes is
                 presented. This method makes use of the information
                 from technical indices and candlestick chart. The
                 proposed optimization model, consisting of technical
                 analysis rules, are trained to generate trading advice.
                 The experimental results on the Japanese stock market
                 show that the proposed optimization system using GNP
                 with control nodes method outperforms other traditional
                 models in terms of both accuracy and efficiency. We
                 also compared the experimental results of the proposed
                 model with the conventional GNP based methods, GA and
                 Buy&Hold method to confirm its effectiveness, and it is
                 clarified that the proposed trading model can obtain
                 much higher profits than these methods.",

Genetic Programming entries for Yan Chen Etsushi Ohkawa Shingo Mabu Kaoru Shimada Kotaro Hirasawa