Investment Strategies Optimization based on a SAX-GA Methodology [electronic resource] / by António M.L. Canelas, Rui F.M.F. Neves, Nuno C.G. Horta.

By: Canelas, António M.L [author.]Contributor(s): Neves, Rui F.M.F [author.] | Horta, Nuno C.G [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: SpringerBriefs in Applied Sciences and Technology: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XII, 81 p. 81 illus., 19 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642331107Subject(s): Engineering | Artificial intelligence | Finance | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics) | Financial Economics | Quantitative FinanceAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access online
Contents:
Introduction -- Market Analysis Background and Related Work -- SAX-GA Approach -- Results -- Conclusions and Future Work.
In: Springer eBooksSummary: 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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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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