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Investment Strategies Optimization based on a SAX-GA Methodology

by Canelas, António M.L.
Authors: Neves, Rui F.M.F.%author. | Horta, Nuno C.G.%author. | SpringerLink (Online service) Series: SpringerBriefs in Applied Sciences and Technology, 2191-530X Physical details: XII, 81 p. 81 illus., 19 illus. in color. online resource. ISBN: 3642331106 Subject(s): Engineering. | Artificial intelligence. | Finance. | Engineering. | Computational Intelligence. | Artificial Intelligence (incl. Robotics). | Financial Economics. | Quantitative Finance.
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E-Book E-Book AUM Main Library 006.3 (Browse Shelf) Not for loan

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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