A portfolio optimization model using Genetic Network Programming with control nodes
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- @Article{Chen200910735,
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author = "Yan Chen and Etsushi Ohkawa and Shingo Mabu and
Kaoru Shimada and Kotaro Hirasawa",
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title = "A portfolio optimization model using Genetic Network
Programming with control nodes",
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journal = "Expert Systems with Applications",
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volume = "36",
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number = "7",
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pages = "10735--10745",
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year = "2009",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2009.02.049",
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URL = "http://www.sciencedirect.com/science/article/B6V03-4VPD6KS-2/2/3cf6750a5518ab6e7d6cf817197d96bd",
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keywords = "genetic algorithms, genetic programming, Portfolio
optimization, Genetic Network Programming, Control
node, Reinforcement learning",
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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
Citations