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A Hybrid Radial Basis Function and Particle Swarm Optimization Neural Network Approach in Forecasting the EUR/GBP Exchange Rates Returns

  • Georgios Sermpinis
  • Konstantinos Theofilatos
  • Andreas Karathanasopoulos
  • Efstratios Georgopoulos
  • Christian Dunis
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)

Abstract

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.

Keywords

PSO RBF Neural Networks Financial Forecasting Trading Leverage Transaction Costs 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Georgios Sermpinis
    • 1
  • Konstantinos Theofilatos
    • 2
  • Andreas Karathanasopoulos
    • 3
  • Efstratios Georgopoulos
    • 4
  • Christian Dunis
    • 5
  1. 1.Business SchoolUniversity of GlasgowGlasgowUK
  2. 2.Department of Computer Engineering and InformaticsUniversity of PatrasGreece
  3. 3.London Metropolitan Business SchoolLondon Metropolitan UniversityLondonUK
  4. 4.Technological Educational Institute of KalamataKalamataGreece
  5. 5.Liverpool Business SchoolJMULiverpoolUK

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