DocumentCode
532580
Title
Notice of Retraction
Predicting Asia sovereign debt crises based on support vector machine
Author
Hong Kang ; Qing Shan Deng
Author_Institution
Sch. of Software & Commun. Eng., Jiangxi Univ. of Finance & Econ., Nanchang, China
Volume
8
fYear
2010
fDate
22-24 Oct. 2010
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 designs and develops an Asia sovereign debt crises predictive model based on support vector machine. A three year time window is defined to observe debt crises probability. Taking 173 samples from eighteen emerging market countries in Asia, the empirical work reveals that the model can predict sovereign debt crises in next three years with accuracy 89.19%. Data analysis also shows repayment history, reserve/MGS ratio and accumulated arrear ratio are top three factors related to debt crisis status.
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 designs and develops an Asia sovereign debt crises predictive model based on support vector machine. A three year time window is defined to observe debt crises probability. Taking 173 samples from eighteen emerging market countries in Asia, the empirical work reveals that the model can predict sovereign debt crises in next three years with accuracy 89.19%. Data analysis also shows repayment history, reserve/MGS ratio and accumulated arrear ratio are top three factors related to debt crisis status.
Keywords
data analysis; economic cycles; financial data processing; support vector machines; Asia sovereign debt crises predictive model; data analysis; support vector machine; Sovereign Debt Crisis; Sovereign default forcasting; Support Vector Machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location
Taiyuan
Print_ISBN
978-1-4244-7235-2
Type
conf
DOI
10.1109/ICCASM.2010.5620780
Filename
5620780
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