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

  • Sermpinis, Georgios
  • Theofilatos, Konstantinos
  • Karathanasopoulos, Andreas
  • Georgopoulos, Efstratios F.
  • Dunis, Christian
Registered author(s):

    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 naı¨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 2years for out-of-sample testing.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0377221712007667
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    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 225 (2013)
    Issue (Month): 3 ()
    Pages: 528-540

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    Handle: RePEc:eee:ejores:v:225:y:2013:i:3:p:528-540
    Contact details of provider: Web page: http://www.elsevier.com/locate/eor

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