Data mining method for listed companies’ financial distress prediction

2008 ◽  
Vol 21 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Jie Sun ◽  
Hui Li
Keyword(s):  
Data Mining ◽  
Financial Distress ◽  
Listed Companies ◽  
Mining Method ◽  
Data Mining Method ◽  
Financial Distress Prediction ◽  
Distress Prediction

scholarly journals Financial distress prediction of listed companies – empirical evidence on the Vietnamese stock market

2020 ◽  
Vol 17 (2) ◽  
pp. 377-388
Author(s):  
Tran Quoc Thinh ◽  
Dang Anh Tuan ◽  
Nguyen Thanh Huy ◽  
Tran Ngoc Anh Thu
Keyword(s):  
Stock Market ◽  
Financial Distress ◽  
Financial Statements ◽  
Listed Companies ◽  
Research Model ◽  
Z Score ◽  
Economic Decisions ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
High Level

Financial distress is a matter of concern in the recent period as Vietnam gradually enters global markets. This paper aims to examine the factors of Altman Z-score to detect the financial distress of Vietnamese listed companies. The authors use a sample of 30 delisted companies due to financial problems and 30 listed companies on the Vietnamese stock market from 2015 to 2018. They employ Independence Samples T-test to test the research model. It is found that there are significant differences in the factors of Altman Z-score between the group of listed companies and the group of delisted companies. Further analyses using subsamples of delisted companies show that the factors of Altman Z-score are also statistically different between companies with a low level of financial distress and those with a high level of financial distress. Based on the results, there are some suggestions to assist practitioners and the State Securities Commission in detecting, preventing, and strictly controlling financially distressed businesses. These results also enable users of financial statements to make more rational economic decisions accordingly.

PREDICTING FINANCIAL DISTRESS OF CHINESE LISTED COMPANIES USING ROUGH SET THEORY AND SUPPORT VECTOR MACHINE

2011 ◽  
Vol 28 (01) ◽  
pp. 95-109 ◽  
Author(s):  
YU CAO ◽  
GUANGYU WAN ◽  
FUQIANG WANG
Keyword(s):  
Support Vector Machine ◽  
Set Theory ◽  
Rough Set ◽  
Financial Distress ◽  
Rough Set Theory ◽  
Listed Companies ◽  
Support Vector ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Chinese Listed Companies

Effectively predicting corporate financial distress is an important and challenging issue for companies. The research aims at predicting financial distress using the integrated model of rough set theory (RST) and support vector machine (SVM), in order to find a better early warning method and enhance the prediction accuracy. After several comparative experiments with the dataset of Chinese listed companies, rough set theory is proved to be an effective approach for reducing redundant information. Our results indicate that the SVM performs better than the BPNN when they are used for corporate financial distress prediction.

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