Gene Expression Programming and Trading Strategies
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- @InProceedings{conf/ifip12/SermpinisFTK13,
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title = "Gene Expression Programming and Trading Strategies",
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author = "Georgios Sermpinis and Anastasia Fountouli and
Konstantinos A. Theofilatos and
Andreas S. Karathanasopoulos",
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bibdate = "2013-09-04",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ifip12/aiai2013.html#SermpinisFTK13",
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booktitle = "Artificial Intelligence Applications and Innovations -
9th {IFIP} {WG} 12.5 International Conference, {AIAI}
2013, Paphos, Cyprus, September 30 - October 2, 2013,
Proceedings",
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publisher = "Springer",
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year = "2013",
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volume = "412",
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editor = "Harris Papadopoulos and Andreas S. Andreou and
Lazaros S. Iliadis and Ilias Maglogiannis",
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isbn13 = "978-3-642-41141-0",
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pages = "497--505",
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series = "IFIP Advances in Information and Communication
Technology",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, GEP",
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DOI = "doi:10.1007/978-3-642-41142-7_5",
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abstract = "This paper applies a Gene Expression Programming (GEP)
algorithm to the task of forecasting and trading the
SPDR Down Jones Industrial Average (DIA), the SPDR S&P
500 (SPY) and the Powershares Qqq Trust Series 1 (QQQ)
exchange traded funds (ETFs). The performance of the
algorithm is benchmarked with a simple random walk
model (RW), a Moving Average Convergence Divergence
(MACD) model, a Genetic Programming (GP) algorithm, a
Multi-Layer Perceptron (MLP), a Recurrent Neural
Network (RNN) and a Gaussian Mixture Neural Network
(GM). The forecasting performance of the models is
evaluated in terms of statistical and trading
efficiency. Three trading strategies are introduced to
further improve the trading performance of the GEP
algorithm. This paper finds that the GEP model
outperforms all other models under consideration. The
trading performance of GEP is further enhanced when the
trading strategies are applied.",
- }
Genetic Programming entries for
Georgios Sermpinis
Anastasia Fountouli
Konstantinos A Theofilatos
Andreas S Karathanasopoulos
Citations