Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization

Sermpinis, G. , Theofilatos, K., Karathanasopoulos, A. and Dunis, C. (2013) Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization. European Journal of Operational Research, 225(3), pp. 528-540. (doi:10.1016/j.ejor.2012.10.020)

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Publisher's URL: http://dx.doi.org/10.1016/j.ejor.2012.10.020

Abstract

The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts 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, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time series over the period January 1999 to March 2011 using the last two years for out-of-sample testing.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Sermpinis, Professor Georgios
Authors: Sermpinis, G., Theofilatos, K., Karathanasopoulos, A., and Dunis, C.
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:European Journal of Operational Research
Publisher:Elsevier BV
ISSN:0377-2217
ISSN (Online):1872-6860
Published Online:01 January 2012
Copyright Holders:Copyright © 2012 Elsevier BV
First Published:First published in European Journal of Operational Research 225(3):528-540
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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