Credit rating systems have existed for a long time in most financial markets and played a major role in corporate capital raising, providing investment information for both individual investors and institutional investors, and credit granting in banks. The purpose of credit ratings is to measure the credit worthiness of credit securities' issuers so as to provide investors valuable information in making financial decisions. Due to the fact that the subordination of bonds has a great impact on the bond's rating (hence render the rating problem much easier to solve), most of the early researches have focused on industrial bond ratings rather than issuers' credit rating. In terms of classification approaches, early researches relied on conventional statistic methods, while recent studies tended to apply artificial intelligence based techniques, such as artificial neural networks and case-based reasoning. The main objective of this research is to propose a classification model for the issuers' credit ratings based on support vector machines, a novel classification algorithm famous for dealing with high dimension classification. To verify the capability of the proposed model, a set of Standard and Poor's issuers' credit rating data as used as the test bed. To construct our classification models, the ten key financial variables used by Standard and Poor's (S&P), and country risk were chosen as the input variables. An artificial neural network based classification model as selected as the benchmark. Our empirical results showed the superiority of the support vector machine model over the neural artificial network model.
|Translated title of the contribution||A Study of SVM Classification Models in Issuers' Credit Ratings|
|Original language||Chinese (Traditional)|
|Number of pages||24|
|Publication status||Published - 2007|
- backpropagation neural networks
- credit ratings
- support vector machines