Graphical Data Representation in Bankruptcy Analysis

Abstract

Graphical data representation is an important model selection tool in bankruptcy analysis, since this problem is highly nonlinear and its numerical representation is not very transparent. In classical rating models, the convenient representation of the ratings in a closed form reduces the need for graphical tools. In contrast to this, more accurate nonlinear nonparametric models often rely on visualisation. We demonstrate the utilisation of visualisation techniques at different stages of corporate default analysis, which is based on the application of support vector machines (SVM). These stages are the selection of variables (predictors), probability of default (PD) estimation, and the representation of PDs for two- and higher dimensional models with colour coding.The selection of a proper colour scheme becomes crucial to the correct visualisation of PDs at this stage.The mapping of scores into PDs is done as a nonparametric regression with monotonisation. The SVM learns a nonparametric score function that is, in turn, nonparametrically transformed into PDs. Since PDs cannot be represented in a closed form, other ways of displaying themmust be found. Graphical tools make this possible.