2012 | OriginalPaper | Buchkapitel
A Hybrid Radial Basis Function and Particle Swarm Optimization Neural Network Approach in Forecasting the EUR/GBP Exchange Rates Returns
verfasst von : Georgios Sermpinis, Konstantinos Theofilatos, Andreas Karathanasopoulos, Efstratios Georgopoulos, Christian Dunis
Erschienen in: Engineering Applications of Neural Networks
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
The motivation for this paper is to introduce in Finance a hybrid Neural Network architecture of Adaptive Particle Swarm Optimization and Radial Basis Function (ARBF-PSO) and a Neural Network fitness function for financial forecasting purposes. This is done by benchmarking the ARBF-PSO results with those of three different Neural Networks architectures and three statistical/technical models. As it turns out, the ARBF-PSO architecture outperforms all other models in terms of statistical accuracy and trading efficiency in the examined forecasting task.