Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices

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

@InProceedings{Chen2:2008:cec,
  author =       "Yan Chen and Shingo Mabu and Kaoru Shimada and 
                 Kotaro Hirasawa",
  title =        "Real Time Updating Genetic Network Programming for
                 Adapting to the Change of Stock Prices",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "370--377",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0109.pdf",
  DOI =          "doi:10.1109/CEC.2008.4630824",
  abstract =     "The key in stock trading model is to take the right
                 actions for trading at the right time, primarily based
                 on accurate forecast of future stock trends. Since an
                 effective trading with given information of stock
                 prices needs an intelligent strategy for the decision
                 making, we applied Genetic Network Programming (GNP) to
                 create a stock trading model. In this paper, we present
                 a new method called Real Time Updating Genetic Network
                 Programming (RTU-GNP) for adapting to the change of
                 stock prices. There are two important points in this
                 paper: First, the RTU-GNP method makes a stock trading
                 decision considering both the recommendable information
                 of technical indices and the change of stock prices
                 according to the real time updating. Second, we combine
                 RTU-GNP with a reinforcement learning algorithm to
                 create the programs efficiently. The experimental
                 results on the Japanese stock market show that the
                 trading model with the proposed RTU-GNP method
                 outperforms other models without time updating method.
                 It yielded significantly higher profits than the
                 traditional trading model without time updating. We
                 also compare the experimental results using the
                 proposed method with Buy&Hold method to confirm its
                 effectiveness, and it is clarified that the proposed
                 trading model can obtain much higher profits than
                 Buy&Hold method.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",
}

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

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