期刊
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
卷 6, 期 1, 页码 113-140出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S021962200700237X
关键词
artificial neural networks; finance forecasting; economic forecasting; input variables selection; performance comparisons
Artificial neural networks (ANNs) have been widely applied to finance and economic forecasting as a powerful modeling technique. By reviewing the related literature, we discuss the input variables, type of neural network models, performance comparisons for the prediction of foreign exchange rates, stock market index and economic growth. Economic fundamentals are important in driving exchange rates, stock market index price and economic growth. Most neural network inputs for exchange rate prediction are univariate, while those for stock market index prices and economic growth predictions are multivariate in most cases. There are mixed comparison results of forecasting performance between neural networks and other models. The reasons may be the difference of data, forecasting horizons, types of neural network models and so on. Prediction performance of neural networks can be improved by being integrated with other technologies. Nonlinear combining forecasting by neural networks also provides encouraging results.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据