Application of Data Mining on Financial Distressed Models -A study on the Listed Companies in Taiwan in 2005

• Main Authors: Hao-Han Kuo, 郭浩瀚
• Other Authors: Ben-Chang Shia
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/52441114759880201299

碩士 === 輔仁大學 === 應用統計學研究所 === 96 === Whenever a crisis or financial distress occurs to some well-known enterprises, the effect can be catastrophic. Not only can it affect the institution, but also can make a significant impact on investors, banks, as well as government. Therefore, if we can create a financial distress model to forecast the occurrences of such crisis, it will be helpful to the company, investors, banks and government to reduce the possibility of such loss. The main purpose of this research is to apply CRISP-DM and data mining techniques, as well as traditional statistical methods to develop financial distress models. CRISP-DM is a popular method of data mining process. We collected financial and non-financial data, for example, accounting information, ownership structure, from some of the listed companies in Taiwan. We use different oversampling data to build Logistic Regression models, Neural Network models and Decision Tree models in the first. Second is evaluated and compared models by Classification Matrix and Lift Chart. Finally we used Cluster Analysis to distinguish risk. The research display the Logistic Regression Models of oversampling 1:3 are optimal models. From the optimal Logistic Regression Models of oversampling 1:3, We found that “Times Interest Earned Ratio”、 “Current Ratio”、“Growth Rate of Total Assets”、“Liabilities Ratio” are the significant variables. In addition, we use Microsoft SQL Server 2005 Decision Tree algorithm to find the significant variables, including Liabilities Ratio, Growth Rate of Total Assets, Return On Equity. We get 5 rules. It will understand interactive of the significant variables and help government, banks, and investors to assure the characters of financial distress then reduce the possibility of loss.