A Cross-Validation Analysis of Neural Network Out-of-Sample Performance in Exchange Rate Forecasting

ABSTRACT Econometric methods used in foreign exchange rate forecasting have produced inferior out‐of‐sample results compared to a random walk model. Applications of neural networks have shown mixed findings. In this paper, we investigate the potentials of neural network models by employing two cross‐validation schemes. The effects of different in‐sample time periods and sample sizes are examined. Out‐of‐sample performance evaluated with four criteria across three forecasting horizons shows that neural networks are a more robust forecasting method than the random walk model. Moreover, neural network predictions are quite accurate even when the sample size is relatively small.