Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization

Autor: Christian L. Dunis, Konstantinos Theofilatos, Georgios Sermpinis, Andreas Karathanasopoulos, Efstratios F. Georgopoulos
Rok vydání: 2013
Předmět:
Zdroj: European Journal of Operational Research. 225:528-540
ISSN: 0377-2217
DOI: 10.1016/j.ejor.2012.10.020
Popis: 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 nai¨ve 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–March 2011 using the last 2 years for out-of-sample testing. As it turns out, the ARBF–PSO architecture outperforms all other models in terms of statistical accuracy and trading efficiency for the three exchange rates.
Databáze: OpenAIRE
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