Modelling and trading the London, New York and Frankfurt stock exchanges with a new gene expression programming trader tool
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- @Article{journals/isafm/Karathanasopoulos17,
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author = "Andreas Karathanasopoulos",
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title = "Modelling and trading the London, New York and
Frankfurt stock exchanges with a new gene expression
programming trader tool",
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journal = "Intelligent Systems in Accounting, Finance and
Management",
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year = "2017",
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number = "1",
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volume = "24",
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pages = "3--11",
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keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
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bibdate = "2017-05-28",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/isafm/isafm24.html#Karathanasopoulos17",
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DOI = "doi:10.1002/isaf.1401",
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abstract = "The scope of this manuscript is to present a new
short-term financial forecasting and trading tool: the
Gene Expression Programming (GEP) Trader Tool. It is
based on the gene expression programming algorithm.
This algorithm is based on a genetic programming
approach, and provides supreme statistical and trading
performance when used for modelling and trading
financial time series. The GEP Trader Tool is offered
through a user-friendly standalone Java interface. This
paper applies the GEP Trader Tool to the task of
forecasting and trading the future contracts of
FTSE100, DAX30 and S&P500 daily closing prices from
2000 to 2015. It is the first time that gene expression
programming has been used in such massive datasets. The
model's performance is benchmarked against linear and
nonlinear models such as random walk model, a
moving-average convergence divergence model, an
autoregressive moving average model, a genetic
programming algorithm, a multilayer perceptron neural
network, a recurrent neural network a higher order
neural network. To gauge the accuracy of all models,
both statistical and trading performances are measured.
Experimental results indicate that the proposed
approach outperforms all the others in the in-sample
and out-of-sample periods by producing superior
empirical results. Furthermore, the trading
performances are improved further when trading
strategies are imposed on each of the models.",
- }
Genetic Programming entries for
Andreas S Karathanasopoulos
Citations