The robustness of neural networks for modelling and trading the EUR/USD exchange rate at the ECB fixing

The objective of this study is to investigate the use, the stability and the robustness 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 autoregressive terms as inputs. This is achieved by benchmarking the forecasting performance of three different NN designs representing a Higher Order Neural Network (HONN), a Recurrent Neural Network (RNN) and the classic Multilayer Perceptron (MLP) with some traditional techniques, either statistical, such as an autoregressive moving average model, or technical, such as a moving average convergence/divergence model, plus a naïve 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 January 1999 – August 2008 using the last 8 months for out-of-sample testing. Our results in terms of their robustness and stability are compared with a previous study by the authors, who apply the same models and follow the same methodology forecasting the same series, using as out-of-sample the period from July 2006 to December 2007. As it turns out, the HONN and MLP networks present a robust performance and do remarkably well in outperforming all other models in a simple trading simulation exercise in both studies. Moreover, when transaction costs are considered and leverage is applied, the same networks continue to outperform all other NN and traditional statistical models in terms of annualised return – a robust and stable result as it is identical to that obtained by the authors in their previous study, examining a different period for