A Cross‐Validation Analysis of Neural Network Out‐of‐Sample Performance in Exchange Rate Forecasting
研究了两种交叉验证方案下神经网络模型在汇率预测中的表现，发现其比随机游走模型更稳健，且在小样本下预测也较准确。
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.