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A Cross‐Validation Analysis of Neural Network Out‐of‐Sample Performance in Exchange Rate Forecasting
Author(s) -
Hu Michael Y.,
Zhang Guoqiang Peter,
Jiang Christine X.,
Patuwo B. Eddy
Publication year - 1999
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1999.tb01606.x
Subject(s) - sample (material) , artificial neural network , random walk , computer science , econometrics , sample size determination , cross validation , artificial intelligence , statistics , mathematics , chemistry , chromatography
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.