Article ID: | iaor20003695 |
Country: | United States |
Volume: | 30 |
Issue: | 1 |
Start Page Number: | 197 |
End Page Number: | 216 |
Publication Date: | Dec 1999 |
Journal: | Decision Sciences |
Authors: | Hu Michael Y., Patuwo B.E., Zhang G.Q., Jiang C.Z. |
Keywords: | financial |
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 difference 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.