SpringerBriefs in Applied Sciences and Technology

Canelas, António M.L.; Neves, Rui F.M.F.; Horta, Nuno C.G.

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.

Details

Published by: Springer

Publication Date: 2012-09-28

Format: Paperback

ISBN-13: 9783642331091

DOI: 10.1007/978-3-642-33110-7

Dimensions: 235cm x155cm

Pages: 81