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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lisboa, P., Vellido, A.: Business Applications of Neural Networks. In: Lisboa, P., Edisbury, B., Vellido, A. (eds.) Business Applications of Neural Networks: The State-of-the-Art of Real-World Applications, pp. vii–xxii. World Scientific, Singapore (2000)
Ding, H., Xiao, Y., Yue, J.: Adaptive Training of Radial Basis Function Networks Using Particle Swarm Optimization Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005, Part I. LNCS, vol. 3610, pp. 119–128. Springer, Heidelberg (2005)
Fulcher, J., Zhang, M., Xu, S.: The Application of Higher-Order Neural Networks to Financial Time Series. In: Kamruzzaman, J., Begg, R., Sarker, R. (eds.) Artificial Neural Networks in Finance and Manufacturing, Hershey, PA. Idea Group, London (2006)
Panda, C., Narasimhan, V.: Forecasting exchange rate better with artificial neural network. Journal of Policy Modeling 29(2), 227–236 (2007)
Kiani, K., Kastens, T.: Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures. Computational Economics 4(32), 383–406 (2008)
Khashei, M., Bijari, M., Ardali, G.: Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs). Neurocomputing 4(6), 956–967 (2009)
Dunis, C., Laws, J., Sermpinis, G.: Modelling and trading the EUR/USD exchange rate at the ECB fixing. The European Journal of Finance 16(6), 541–560 (2010)
Jarque, C.M., Bera, A.: A Test for Normality of Observations and Regression Residuals. International Statistical Review 55(2), 163–172 (1987)
Broomhead, S., Lowe, D.: Multivariate Functional Interpolation and Adaptive Networks. Complex Systems 2, 321–355 (1988)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Moody, J., Darken, C.J.: Fast learning in networks of locally tuned processing units. Neural Computation 1(2), 281–294 (1989)
Diebold, F.X., Mariano, R.S.: Comparing Predictive Accuracy. Journal of Business and Economic Statistics 13, 253–263 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sermpinis, G., Theofilatos, K., Karathanasopoulos, A., Georgopoulos, E., Dunis, C. (2012). A Hybrid Radial Basis Function and Particle Swarm Optimization Neural Network Approach in Forecasting the EUR/GBP Exchange Rates Returns. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-32909-8_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32908-1
Online ISBN: 978-3-642-32909-8
eBook Packages: Computer ScienceComputer Science (R0)