DocumentCode
1655481
Title
Notice of Retraction
Designing an early warning system of sovereign debt crises using Bp neural networks
Author
Hong Kang ; Yuan-Cheng Hu
Author_Institution
Sch. of software & Commun. Eng., Jiangxi Univ. of Finance & Econ., Nanchang, China
Volume
1
fYear
2010
Firstpage
351
Lastpage
355
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper has developed an early warning system of sovereign debt crises based on Bp neural networks. Using data related to macroeconomic, debt burden and debt repayment history of 54 developing countries or less developed countries from 1991 to 2006, empirical result reveals that the system can predict sovereign debt crises in next three years effectively and its overall result is 86.7%. At same time, predictive comparison with binary Logistic finds that using BP neural network to predict sovereign debt crisis is relatively advanced to binary Logistic method.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper has developed an early warning system of sovereign debt crises based on Bp neural networks. Using data related to macroeconomic, debt burden and debt repayment history of 54 developing countries or less developed countries from 1991 to 2006, empirical result reveals that the system can predict sovereign debt crises in next three years effectively and its overall result is 86.7%. At same time, predictive comparison with binary Logistic finds that using BP neural network to predict sovereign debt crisis is relatively advanced to binary Logistic method.
Keywords
backpropagation; economic indicators; logistics; neural nets; BP neural networks; binary logistic method; debt burden; debt repayment history; early warning system; macroeconomic; sovereign debt crises; Accuracy; BP Neural Network; Sovereign Debt Crisis; Sovereign default probability;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Management Science (ICAMS), 2010 IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6931-4
Type
conf
DOI
10.1109/ICAMS.2010.5553161
Filename
5553161
Link To Document