Investment Strategies Optimization based on a SAX-GA Methodology

• Κύριοι συγγραφείς: Canelas, António M.L (Συγγραφέας), Neves, Rui F.M.F (Συγγραφέας), Horta, Nuno C.G (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
• Σειρά: SpringerBriefs in Applied Sciences and Technology,
• Θέματα: Engineering.; Artificial intelligence.; Economics, Mathematical.; Computational intelligence.; Macroeconomics.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).; Macroeconomics/Monetary Economics//Financial Economics.; Quantitative Finance.
• Φυσική περιγραφή: XII, 81 p. 81 illus., 19 illus. in color. online resource.
• ISBN: 9783642331107
• ISSN: 2191-530X

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.