Constructing Portfolio Investment Strategy Based on Time Adapting Genetic Network Programming

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@InProceedings{Chen2:2009:cec,
  author =       "Yan Chen and Shingo Mabu and Etsushi Ohkawa and 
                 Kotaro Hirasawa",
  title =        "Constructing Portfolio Investment Strategy Based on
                 Time Adapting Genetic Network Programming",
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "2379--2386",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P026.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983238",
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming, Japanese stock market, candlestick
                 chart, evolutionary method, investment advice,
                 portfolio investment strategy, portfolio model,
                 portfolio optimisation problem, portfolio problem,
                 stock prices, technical analysis rules, technical
                 indices, time adapting genetic network programming,
                 investment, stock markets",
  abstract =     "The classical portfolio problem is a problem of
                 distributing capital to a set of stocks. By adapting to
                 the change of stock prices, this study proposes an
                 portfolio investment strategy based on an evolutionary
                 method named {"}Genetic Network Programming{"} (GNP).
                 This method makes use of the information from Technical
                 Indices and Candlestick Chart. The proposed portfolio
                 model, consisting of technical analysis rules, are
                 trained to generate investment advice. Experimental
                 results on the Japanese stock market show that the
                 proposed investment strategy using Time Adapting GNP
                 (TA-GNP) 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 investment strategy is
                 effective on the portfolio optimization problem.",
  notes =        "CEC 2009 - A joint meeting of the IEEE, the EPS and
                 the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known
                 as \cite{4983238}",
}

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

Citations