Generating Trading Rules for Stock Markets Using Robust Genetic Network Programming and Portfolio Beta
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- @Article{DBLP:journals/jaciii/ChenS16,
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author = "Yan Chen and Zhihui Shi",
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title = "Generating Trading Rules for Stock Markets Using
Robust Genetic Network Programming and Portfolio Beta",
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journal = "Journal of Advanced Computational Intelligence and
Intelligent Informatics",
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year = "2016",
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volume = "20",
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number = "3",
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pages = "484--491",
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month = may,
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keywords = "genetic algorithms, genetic programming, portfolio
beta, genetic relation algorithm, robust genetic
network programming, stock trading",
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ISSN = "1343-0130",
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timestamp = "Fri, 18 Sep 2020 01:00:00 +0200",
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biburl = "https://dblp.org/rec/journals/jaciii/ChenS16.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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URL = "https://doi.org/10.20965/jaciii.2016.p0484",
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DOI = "doi:10.20965/jaciii.2016.p0484",
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size = "8 pages",
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abstract = "Robust Genetic Network Programming (R-GNP) for
generating trading rules for stocks is described. R-GNP
is a new evolutionary algorithm, where solutions are
represented using graph structures. It has been
clarified that R-GNP works well especially in dynamic
environments. In the proposed hybrid model, R-GNP is
applied to generating stock trading rules with variance
of fitness values. The unique point is that the
generalization ability of R-GNP is improved by using
the robust fitness function, which consists of the
fitness functions with the original data and a good
number of correlated data. Generally speaking, the
hybrid intelligent system consists of three steps:
priority selection by the portfolio beta, optimization
by the Genetic Relation Algorithm (GRA), and stock
trading by R-GNP. In the simulations, the trading model
is trained using the stock prices of 10 brands on the
Tokyo Stock Exchange, and then the generalization
ability is tested. From the simulation results, it is
clarified that the trading rules created by the
proposed R-GNP model obtain much higher profits than
the traditional methods even in the world-wide
financial crisis of 2007. Hence, its effectiveness has
been confirmed.",
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notes = "also known as \cite{Chen_2016jaciii}
School of Statistics and Management, Shanghai
University of Finance and Economics, Shanghai 200433,
China",
- }
Genetic Programming entries for
Yan Chen
Zhihui Shi
Citations