Investment Strategies Optimization based on a SAX-GA Methodology

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Canelas, António M.L (Autor), Neves, Rui F.M.F (Autor), Horta, Nuno C.G (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Colección:SpringerBriefs in Computational Intelligence,
Temas:
Computational intelligence.
Artificial intelligence.
Macroeconomics.
Social sciences-Mathematics.
Computational Intelligence.
Artificial Intelligence.
Macroeconomics and Monetary Economics.
Mathematics in Business, Economics and Finance.
Acceso en línea:Texto Completo
Descripción
Sumario: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.
Descripción Física:XII, 81 p. 81 illus., 19 illus. in color. online resource.
ISBN:9783642331107
ISSN:2625-3712

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