台灣上市櫃公司建構信用評等預測模型之研究-以CRF、GA、SVM、BPN、Logit、MDA方法之應用

The study of this paper is to develop 11 Credit-Rating Forecasting Models based on the financial ratios、earnings management、corporate government and business cycle variables using the data 5 years prior to company bankruptcy to develop the early credit rating warning system . We collected the data from year 2000 to 2005 to build up the model and then to develop the prediction accuracy ability and using the data from year 2006 to 2007 to examine the stability of each various models. Through employing the 6 different methods of Conditional Random Field (CRF)、Genetic Algorithm (GA)、Support Vector Machine (SVM)、Back Propagation Networks (BPN)、Logistic Regression (Logit)、Multiple Discriminant Analysis (MDA), to forecasting the prediction accounting and test stability of our research model. The empirical results shows that the best model is the one including finance ratios、earnings management、corporate government and business cycles variables. We employed the 6 prediction methods mentioned above to test the research model. Among all the methods, the Conditional Random Field (CRF) indicates the best result and then the Genetic Algorithm (GA)、Support Vector Machines (SVM)、Back Propagation Networks (BPN)、Logistic Regression (Logit)、Multiple Discriminant Analysis (MDA). Regarding the best credit-rating system, we found that using the data one year prior to the company bankruptcy shows the best prediction accuracy and stability.