DocumentCode
1648731
Title
Notice of Retraction
Study on financial distress prediction of listed companies based on Logistic Regression
Author
Ai-mei, Lin ; Chun-mei, Wei
Author_Institution
School of management China University of Mining and Technology Xuzhou, China
fYear
2011
Firstpage
1
Lastpage
4
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 uses listed companies as research object, selects 102 2006–2008 ST companies and 102 paired normal companies as an analysis sample, the other 40 selected in 2009 as a test sample. Logistic Regression is used to constructed Early warning model, the results show that: The model that contains the three indicators — a return on assets, asset-liability ratio and total asset turnover is able to make more accurate forecasts, the accurate rate of the model constructed is 87.85%, the accurate rate to verify the model is 80%.
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 uses listed companies as research object, selects 102 2006–2008 ST companies and 102 paired normal companies as an analysis sample, the other 40 selected in 2009 as a test sample. Logistic Regression is used to constructed Early warning model, the results show that: The model that contains the three indicators — a return on assets, asset-liability ratio and total asset turnover is able to make more accurate forecasts, the accurate rate of the model constructed is 87.85%, the accurate rate to verify the model is 80%.
Keywords
Accuracy; Analytical models; Companies; Indexes; Logistics; Predictive models; Logistic regression; financial crisis; prediction;
fLanguage
English
Publisher
ieee
Conference_Titel
E -Business and E -Government (ICEE), 2011 International Conference on
Conference_Location
Shanghai, China
Print_ISBN
978-1-4244-8691-5
Type
conf
DOI
10.1109/ICEBEG.2011.5882207
Filename
5882207
Link To Document