Constructing Two-Stage Credit Rating Predicting Model With Data Mining Technique

• Main Authors: Huang, Yenhao, 黃衍浩
• Other Authors: Wu, Hsuche
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/51760092720637157599

碩士 === 國立中正大學 === 會計與資訊科技研究所 === 100 === As the financial crisis resulted in unprecedented attention of financial institutions on credit risk, and under the New Basel Capital Accord, Basel III, the real and precise estimation of the unexpected loss not only affects the capital adequacy, but also has influence on the evaluation of risk management. As a result, the classification categories for credit rating concentrated on classifying 2 classes, good credit and bad credit from recent research, are limited. In addition, most research so focused on employing data mining techniques to construct models that they lost sight of the importance in data preparation i.e. data pre-process. This paper uses two-stage way, 1st stage is data pre-process methods including feature selection, cluster, as well as resample, and 2nd is data mining techniques comprising DT, BN, ANN, SVM, Bagging, and Vote, to construct prediction model for Taiwan credit rating. Taiwan Corporate Credit Risk Index (TCRI) from TEJ is used for experimental analysis, and the research collects 41 related studies and selects 10 noticeable papers to acquire 30 input variables; moreover, output variable is TCRI and has 9 classification categories. In experimental results, the contribution is not only research for the optimal model, Bagging (DT) with Resample method to achieve excellent accuracy 82.96%, as well as demonstrates that two-stage prediction model is better than one-stage model. The limitation is simply employs three kinds of data pre-process methods on account of needing more time to prepare, the future study could involve more features and research for more data pre-process methods such as backwards and another cluster algorithm.