Forecasting exchange rates using general regression neural networks

被引:134
|
作者
Leung, MT [1 ]
Chen, AS
Daouk, H
机构
[1] Univ Texas, Coll Business, Div Management & Mkt, San Antonio, TX 78249 USA
[2] Natl Chung Cheng Univ, Dept Finance, Kyoto 612, Japan
[3] Indiana Univ, Kelley Sch Business, Dept Finance, Bloomington, IN 47405 USA
关键词
general regression neural networks; currency exchange rate; forecasting;
D O I
10.1016/S0305-0548(99)00144-6
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this study, we examine the forecastability of a specific neural network architecture called general regression neural network (GRNN) and compare its performance with a variety of forecasting techniques, including multi-layered feedforward network (MLFN), multivariate transfer function, and random walk models. The comparison with MLFN provides a measure of GRNN's performance relative to the more conventional type of neural networks while the comparison with transfer function models examines the difference in predictive strength between the non-parametric and parametric techniques. The difficult to beat random walk model is used for benchmark comparison. Our findings show that GRNN not only has a higher degree of forecasting accuracy but also performs statistically better than other evaluated models for different currencies.
引用
收藏
页码:1093 / 1110
页数:18
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