A novel approach on hybrid Support Vector Machines into optimal portfolio selection
Authors: Loukeris, Nikolaos 
Eleftheriadis, Iordanis 
Livanis, Efstratios 
Issue Date: 1-Dec-2013
Conference: IEEE International Symposium on Signal Processing and Information Technology, 12-15 December 2013, Athens, Greece 
Book: Proceedings of the IEEE International Symposium on Signal Processing and Information Technology 
Keywords: Bankruptcy, Genetic algorithms, Portfolio selection, Support vector machines
Abstract: 
The efficient representation of the accurate corporate value on the stock price is vital to investors and fund managers that desire to optimize the net worth of the overall stock portfolio. Although Efficient Market Hypothesis sets limits, the practice of markets is an ideal place of manipulation, and corruption on prices. The accounting statements, evaluated by Support Vector Machines and the SVM Hybrids under Genetic Algorithms provide excellence in portfolio selection. A specific Neuro-genetic Hybrid SVM outperformed all examined SVM models being a powerful tool in financial analysis.
ISBN: 978-1-4799-4796-6
DOI: 10.1109/ISSPIT.2013.6781852
URI: https://uniwacris.uniwa.gr/handle/3000/2220
Type: Conference Paper
Department: Department of Business Administration 
School: School of Administrative, Economics and Social Sciences 
Affiliation: University of West Attica (UNIWA) 
Appears in Collections:Book Chapter / Κεφάλαιο Βιβλίου

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