Intelligent decision support for bond rating

Abstract

The goal of this study is to automate the real world bond rating procedures. The most important part for achieving this goal is to develop an appropriate classification method for bond rating which has a good predictive performance. In this study, neural networks approach is adopted. Statistical classification methods such as multivariate discriminant analysis have been widely used in bond rating classification in spite of the limitations of the methodology. Recently, neural networks have emerged as new methods for business classification. This neural networks approach is to categorize a new instance as one of the predefined bond classes. Such a conventional approach has limitations in dealing with the ordinal nature of bond rating. In addition, most of the prior studies have used sample data which are evenly divided among the classes. However, most natural population in real application is unevenly divided among the classes. Under such circumstances, it is hard to achieve good predictive performance. As the number of classes to be recognized increases, the predictive performance decreases. In this study, to increase the predictive performance in real world bond rating, we propose the ordinal pairwise partitioning(OPP) approach to training in backpropagation neural networks. The main idea of the OPP approach is to partition the data set in the ordinal and pairwise manner into the output classes. Then, each backpropagation neural networks model is trained by using each partitioned data set and is separately used for classification. Experimental results, using the financial data of Korean companies, show that the predictive performance by the proposed OPP approach in neural networks training can be significantly enhanced, when compared to the conventional neural networks modeling approach as well as multivariate discriminant analysis. The OPP approach has two computation methods of forward and backward, and we discuss under which circumstances one method perform...