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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. ISSN 0377-2217 (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:Article
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Sermpinis, Dr 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: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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