Predicting changes in federal funds target rate using artificial neural networks.
The aim of this paper is to apply established neural network architecture, the backpropagation neural network and the general regressive neural network, will be used in this study.
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Language: | English |
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2008
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Online Access: | http://hdl.handle.net/10356/7180 |
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sg-ntu-dr.10356-71802024-01-12T10:19:45Z Predicting changes in federal funds target rate using artificial neural networks. Chong, Wee Lee. Srinivasan, Bobby Sundaravaradhan Nanyang Business School DRNTU::Business::Finance::Monetary policy The aim of this paper is to apply established neural network architecture, the backpropagation neural network and the general regressive neural network, will be used in this study. Master of Business 2008-09-18T07:41:09Z 2008-09-18T07:41:09Z 2002 2002 Thesis http://hdl.handle.net/10356/7180 en Nanyang Technological University 114 p. application/pdf |
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DRNTU::Business::Finance::Monetary policy Chong, Wee Lee. Predicting changes in federal funds target rate using artificial neural networks. |
description |
The aim of this paper is to apply established neural network architecture, the backpropagation neural network and the general regressive neural network, will be used in this study. |
author2 |
Srinivasan, Bobby Sundaravaradhan |
author_facet |
Srinivasan, Bobby Sundaravaradhan Chong, Wee Lee. |
format |
Theses and Dissertations |
author |
Chong, Wee Lee. |
author_sort |
Chong, Wee Lee. |
title |
Predicting changes in federal funds target rate using artificial neural networks. |
title_short |
Predicting changes in federal funds target rate using artificial neural networks. |
title_full |
Predicting changes in federal funds target rate using artificial neural networks. |
title_fullStr |
Predicting changes in federal funds target rate using artificial neural networks. |
title_full_unstemmed |
Predicting changes in federal funds target rate using artificial neural networks. |
title_sort |
predicting changes in federal funds target rate using artificial neural networks. |
publishDate |
2008 |
url |
http://hdl.handle.net/10356/7180 |
_version_ |
1789483042099691520 |