Investment Strategies Optimization based on a SAX-GA Methodology
Autres auteurs :
Neves, Rui F.M.F.
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author.
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Horta, Nuno C.G.
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author.
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SpringerLink (Online service)
Collection :
SpringerBriefs in Applied Sciences and Technology,
2191-530X
Détails physiques : XII, 81 p. 81 illus., 19 illus. in color. online resource.
ISBN :9783642331107.
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Horta, Nuno C.G.
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author.
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SpringerLink (Online service)
Sujet(s) :
Engineering.
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Artificial intelligence.
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Economics, Mathematical.
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Computational intelligence.
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Macroeconomics.
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Engineering.
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Computational Intelligence.
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Artificial Intelligence (incl. Robotics).
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Macroeconomics/Monetary Economics//Financial Economics.
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Quantitative Finance.
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Artificial intelligence.
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Economics, Mathematical.
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Computational intelligence.
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Macroeconomics.
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Engineering.
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Computational Intelligence.
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Artificial Intelligence (incl. Robotics).
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Macroeconomics/Monetary Economics//Financial Economics.
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Quantitative Finance.
Ressources en ligne :
Exemplaires : http://dx.doi.org/10.1007/978-3-642-33110-7
Introduction -- Market Analysis Background and Related Work -- SAX-GA Approach -- Results -- Conclusions and Future Work.
This book presents a new computational finance approach combining a Symbolic Aggregate approXimation (SAX) technique with an optimization kernel based on genetic algorithms (GA). While the SAX representation is used to describe the financial time series, the evolutionary optimization kernel is used in order to identify the most relevant patterns and generate investment rules. The proposed approach considers several different chromosomes structures in order to achieve better results on the trading platform The methodology presented in this book has great potential on investment markets.
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