Title :
Data Mining of Corporate Financial Risks: Financial Indicators or Non-Financial Indicators
Author :
He Ping-lin ; Yu Zhong-fu ; Tao Jie
Author_Institution :
Sch. of Bus. Adm., North China Electr. Power Univ., Beijing, China
Abstract :
Taking 164 ST (Special Treatment) listed companies in China´s stock market and 328 paired risk-free companies as samples, this paper, adopting Logistic Multiple Regression Model to do multi-step regression analysis, performs data mining to study the relationship between the relative financial and non-financial indicators and the corporate financial risks. The study shows that the three indicators including auditor´s opinions, ratio of liabilities to assets and operating income growth rate exert greatest influence on corporate financial risks, and the corporate governance indicators representing by ownership concentration exert relatively noticeable influence on the corporate financial risks warning model.
Keywords :
data mining; financial data processing; regression analysis; stock markets; China stock market; corporate financial risks warning model; corporate governance indicator; data mining; financial indicator; liabilities-assets ratio; logistic multiple regression model; multistep regression analysis; nonfinancial indicator; operating income growth rate; risk-free company; special treatment listed company; Automatic control; Companies; Data mining; Helium; Logistics; Mathematics; Regression analysis; Risk analysis; Statistics; Stock markets;
Conference_Titel :
Management and Service Science, 2009. MASS '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4638-4
Electronic_ISBN :
978-1-4244-4639-1
DOI :
10.1109/ICMSS.2009.5302111
