Stock Trading Rules Using Genetic Network Programming with Actor-Critic

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Mabu:2007:cec,
  author =       "Shingo Mabu and Yan Chen and Kotaro Hirasawa and 
                 Jinglu Hu",
  title =        "Stock Trading Rules Using Genetic Network Programming
                 with Actor-Critic",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "508--515",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1684.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424513",
  abstract =     "Genetic Network Programming (GNP) is an evolutionary
                 computation which represents its solutions using graph
                 structures. Since GNP can create quite compact programs
                 and has an implicit memory function, it has been
                 clarified that GNP works well especially in dynamic
                 environments. In this paper, GNP is applied to creating
                 a stock trading model. The first important point is to
                 combine GNP with Actor-Critic which is one of the
                 reinforcement learning algorithms. Evolution-based
                 methods evolve their programs after task execution
                 because they must calculate fitness values, while
                 reinforcement learning can change programs during task
                 execution, therefore the programs can be created
                 efficiently. The second important point is that GNP
                 with Actor-Critic (GNP-AC) can select appropriate
                 technical indexes to judge the buying and selling
                 timing of stocks using Importance Index especially
                 designed for stock trading decision making. In the
                 simulations, the trading model is trained using the
                 stock prices of 20 brands in 2001, 2002 and 2003. Then
                 the generalisation ability is tested using the stock
                 prices in 2004. From the simulation results, it is
                 clarified that the trading rules of GNP-AC obtain
                 higher profits than Buy and Hold method.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",
}

Genetic Programming entries for Shingo Mabu Yan Chen Kotaro Hirasawa Jinglu Hu

Citations