Title :
The Application of Data Mining in Predicting Power Industry Financial Risks
Author :
Tao, Jie ; Tao Jie
Author_Institution :
Sch. of Bus. Adm., North China Electr. Power Univ., Beijing, China
Abstract :
This paper applies DEA model to a sample of 58 power plate listed companies in the securities market in China in 2008, with a view to identifying the financial risk companies and non-financial risk companies, instead of using ST in the past. Then, after comparing logit regression model and neural network LVQ in predicting the company financial risks, the conclusion was drawn that neural network LVQ is better in predicting.
Keywords :
data envelopment analysis; data mining; electricity supply industry; financial data processing; learning (artificial intelligence); neural nets; regression analysis; risk management; China; DEA model; data envelopment analysis; data mining; financial risk companies; learning vector quantization; logit regression model; neural network LVQ; power industry financial risks prediction; power plate listed companies; Accuracy; Artificial neural networks; Biological system modeling; Companies; Data models; Neurons; Predictive models; DEA; LVQ Neural Networks; Logit Model; Power Plate Companies;
Conference_Titel :
E-Business and E-Government (ICEE), 2010 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-0-7695-3997-3
DOI :
10.1109/ICEE.2010.371
