Generalisation Improvement of Radial Basis Function Networks Based on Qualitative Input Conditioning for Financial Credit Risk Prediction

Abstract

The rating is a qualified assessment about the credit risk of bonds issued by a government or a company. There are specialised rating agencies, which classify firms according to their level of risk. These agencies use both quantitative and qualitative information to assign ratings to issues. The final rating is the judgement of the agency’s analysts and reflects the probability of issuer default. Since the final rating has a strong dependency on the experts knowledge, it seems reasonable the application of learning based techniques to acquire that knowledge. The learning techniques applied are neural networks and the architecture used corresponds to radial basis function neural networks. A convenient adaptation of the variables involved in the problem is strongly recommended when using learning techniques. The paper aims at conditioning the input information in order to enhance the neural network generalisation by adding qualitative expert information on orders of magnitude. An example of this method applied to some industrial firms is given.