A cross-validation analysis of neural network out-of-sample performance in exchange rate forecasting

A cross-validation analysis of neural network out-of-sample performance in exchange rate forecasting

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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: , , ,
Keywords: financial
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 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.

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