Comparisons of the different frequencies of input data for neural networks in foreign exchange rates forecasting

Authors:
Wei Huang;Lean Yu;Shouyang Wang;Yukun Bao;Lin Wang
Affiliations:
School of Management, Huazhong University of Science and Technology, WuHan, China;Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing, China;Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing, China;School of Management, Huazhong University of Science and Technology, WuHan, China;School of Management, Huazhong University of Science and Technology, WuHan, China
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Year:
2006

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Abstract

We compare the predication performance of neural networks with the different frequencies of input data, namely daily data, weekly data, monthly data. In the 1 day and 1 week ahead prediction of foreign exchange rates forecasting, the neural networks with the weekly input data performs better than the random walk models. In the 1 month ahead prediction of foreign exchange rates forecasting, only the special neural networks with weekly input data perform better than the random walk models. Because the weekly data contain the appropriate fluctuation information of foreign exchange rates, it can balance the noise of daily data and losing information of monthly data.