Regression neural network for error correction in foreign exchange forecasting and trading

Predicting exchange rates has long been a concern in international finance as most standard econometric methods are unable to produce significantly better forecasts than the random walk model. Recent studies provide some evidence for the ability of using multivariate time series models to generate better forecasts. At the same time, artificial neural networks have been emerging as alternatives to predict exchange rates. In this paper, we propose an adaptive forecasting approach which combines the strengths of neural networks and multivariate econometric models. This hybrid approach contains two forecasting stages. In the first stage, a time series model generates estimates of the exchange rates. In the second stage, General Regression Neural Network is used to correct the errors of the estimates. A number of tests and statistical measures are then applied to compare the performances of the two-stage models (with error-correction by neural network) with those of the single-stage models (without error-correction by neural network). Both empirical and trading simulation experiments suggest that the proposed hybrid approach not only produces better exchange rate forecasts but also results in higher investment returns than the single-stage models. The effect of risk aversion in currency trading is also considered.