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Notice of Retraction
Designing an early warning system of sovereign debt crises using Bp neural networks

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2 Author(s)
Hong Kang ; Sch. of software & Commun. Eng., Jiangxi Univ. of Finance & Econ., Nanchang, China ; Yuan-Cheng Hu

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.

Published in:

Advanced Management Science (ICAMS), 2010 IEEE International Conference on  (Volume:1 )

Date of Conference:

9-11 July 2010

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