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Modelling and trading the EUR/USD exchange rate at the ECB fixing

Author

Listed:
  • Christian Dunis
  • Jason Laws
  • Georgios Sermpinis

Abstract

The motivation for this paper is to investigate the use of alternative novel neural network (NN) architectures when applied to the task of forecasting and trading the euro/dollar (EUR/USD) exchange rate, using the European Central Bank (ECB) fixing series with only auto-regressive terms as inputs. This is done by benchmarking four different NN designs representing a higher-order neural network (HONN), a Psi Sigma Network and a recurrent neural network with the classic multilayer perception (MLP) and some traditional techniques, either statistical such as an auto-regressive moving average model, or technical such as a moving average convergence/divergence model, plus a naive strategy. More specifically, the trading performance of all models is investigated in a forecast and trading simulation on the EUR/USD ECB fixing time series over the period 1999-2007 using the last one and half years for out-of-sample testing, an original feature of this paper. We use the EUR/USD daily fixing by the ECB as many financial institutions are ready to trade at this level and it is therefore possible to leave orders with a bank for business to be transacted on that basis. As it turns out, the MLP does remarkably well and outperforms all other models in a simple trading simulation exercise. However, when more sophisticated trading strategies using confirmation filters and leverage are applied, the HONN network produces better results and outperforms all other NN and traditional statistical models in terms of annualized return.

Suggested Citation

  • Christian Dunis & Jason Laws & Georgios Sermpinis, 2010. "Modelling and trading the EUR/USD exchange rate at the ECB fixing," The European Journal of Finance, Taylor & Francis Journals, vol. 16(6), pages 541-560.
  • Handle: RePEc:taf:eurjfi:v:16:y:2010:i:6:p:541-560
    DOI: 10.1080/13518470903037771
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    Citations

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    Cited by:

    1. Ortiz-Arango, Francisco & Cabrera-Llanos, Agustín I. & Venegas-Martínez, Francisco, 2014. "Euro Exchange Rate Forecasting with Differential Neural Networks with an Extended Tracking Procedure," MPRA Paper 57720, University Library of Munich, Germany.
    2. Sermpinis, Georgios & Stasinakis, Charalampos & Theofilatos, Konstantinos & Karathanasopoulos, Andreas, 2015. "Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations," European Journal of Operational Research, Elsevier, vol. 247(3), pages 831-846.
    3. Sermpinis, Georgios & Stasinakis, Charalampos & Rosillo, Rafael & de la Fuente, David, 2017. "European Exchange Trading Funds Trading with Locally Weighted Support Vector Regression," European Journal of Operational Research, Elsevier, vol. 258(1), pages 372-384.
    4. Sermpinis, Georgios & Stasinakis, Charalampos & Dunis, Christian, 2014. "Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 30(C), pages 21-54.

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