| AN EMPIRICAL ANALYSIS OF SAMPLING INTERVAL FOR EXCHANGE RATE FORECASTING WITH NEURAL NETWORKS |
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| 作者姓名： | K.K.Lai Y.Nakamori WANGShouyang |
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| 作者单位： | [1]DepartmentofManagementSciences,CityUniversityofHongKong,TatCheeAvenue,Kowloon,HongKong [3]SchoolofKnowledgeScience,JapanAdvancedInstituteofScienceandTechnology,1-1,Asahidai,Tatsunokuchi,Ishikawa,923-1292,Japan [4]InstituteofSystemsScience,AcademyofMathematicsandSystemsSciences,ChineseAcademyofSciences,Beijing100080,China |
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| 基金项目： | This research is Partially supported by NSFC, CAS. MADIS and RGC of Hong Kong. |
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| 摘 要： | Artificial neural networks (ANNs) have been widely used as a promising alternative approach for forecast task because of their several distinguishing features. In this paper, we investigate the effect of different sampling intervals on predictive performance of ANNs in forecasting exchange rate time series. It is shown that selection of an appropriate sampling interval would permit the neural network to model adequately the financial time series. Too short or too long a sampling interval does not provide good forecasting accuracy. In addition, we discuss the effect of forecasting horizons and input nodes on the prediction performance of neural networks.
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| 关 键 词： | 神经网络 经验分析 抽样间隔 汇率 预测 金融研究 |
AN EMPIRICAL ANALYSIS OF SAMPLING INTERVAL FOR EXCHANGE RATE FORECASTING WITH NEURAL NETWORKS |
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| K.K.Lai Y.Nakamori WANGShouyang.AN EMPIRICAL ANALYSIS OF SAMPLING INTERVAL FOR EXCHANGE RATE FORECASTING WITH NEURAL NETWORKS[J].Journal of Systems Science and Complexity,2003,16(2):165-176. |
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| Authors: | HUANG Wei |
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| Abstract: | Artificial neural networks (ANNs) have been widely used as a promising alternative approach for forecast task because of their several distinguishing features. In this paper, we investigate the effect of different sampling intervals on predictive performance of ANNs in forecasting exchange rate time series. It is shown that selection of an appropriate sampling interval would permit the neural network to model adequately the financial time series. Too short or too long a sampling interval does not provide good forecasting accuracy. In addition, we discuss the effect of forecasting horizons and input nodes on the prediction performance of neural networks. |
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| Keywords: | Neural networks sampling interval exchange rate forecasting |
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