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

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Author Info

  • Sermpinis, Georgios
  • Theofilatos, Konstantinos
  • Karathanasopoulos, Andreas
  • Georgopoulos, Efstratios F.
  • Dunis, Christian
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    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 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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    Bibliographic Info

    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

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Adaptive Radial Basis Function; Partial Swarm Optimization; Forecasting; Quantitative trading strategies;

    References

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    Cited by:
    1. Doyle, John R. & Chen, Catherine H., 2013. "Patterns in stock market movements tested as random number generators," European Journal of Operational Research, Elsevier, vol. 227(1), pages 122-132.
    2. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.

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