DocumentCode
517952
Title
Notice of Retraction
An empirical analysis of the financial distress prediction based on data mining
Author
Luzhuang Wang ; Hong Zhou ; Jingyi Wang
Author_Institution
City Coll., Sch. of Bus., Zhejiang Univ., Hangzhou, China
Volume
4
fYear
2010
fDate
16-18 April 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.
With 112 listed companies from A-share market in Chinese securities markets as research sample, authors selected 56 newly ST companies among 2007 and 2008 as distressed enterprises group, and other 56 non-ST companies as comparison group. 13 financial indicators of these companies at three year before being ST were screened out, and the factor analysis and logistic regression were conducted. It found that the effect of the regression as well as the prediction are quite good, so it should be useful as basic discrimination for investors, creditors and regulatory agencies.
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.
With 112 listed companies from A-share market in Chinese securities markets as research sample, authors selected 56 newly ST companies among 2007 and 2008 as distressed enterprises group, and other 56 non-ST companies as comparison group. 13 financial indicators of these companies at three year before being ST were screened out, and the factor analysis and logistic regression were conducted. It found that the effect of the regression as well as the prediction are quite good, so it should be useful as basic discrimination for investors, creditors and regulatory agencies.
Keywords
data mining; financial management; logistics data processing; regression analysis; A-share market; Chinese securities market; data mining; distressed enterprises group; factor analysis; financial distress prediction; financial indicator; logistic regression; Cities and towns; Companies; Data mining; Data security; Dynamic range; Financial management; Forward contracts; Logistics; Predictive models; Regression analysis; Data mining; Factor analysis; Financial distress prediction; Logistic model;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5485282
Filename
5485282
Link To Document