Credit-scoring by enlarged discriminant models

Abstract

A large part of the research devoted to the application of Discriminant analysis techniques to give early warning of bankruptcy has only partially used balance sheet information, as discriminatory variables typically consisted of ‘single year’ financial and operating ratios.

Lengthening the time scale of such ratios, permits us to look more deeply into the economic reality of firms, since in this way, some strategies may be directly observed. Such an effort even becomes, in certain cases, necessary, for example in certain ‘window dressing’ operations on balance sheets. Such an event may seriously compromise performance of the usual discriminant models.

The problem of ‘make-up accountancy’ is dealt with here, and a solution is proposed through an extension of the variables to be included among discriminatory models. Our aim is to demonstrate that passing from ‘level’ ratios to dynamic variables, such as trend and stability, improves the performance of discriminatory models and addresses problems of ‘window dressing’ implicitly.

After the determination of a discriminant model of the traditional kind, we proceeded with an integration of such aspects (i.e., trend and stability) into the model, in order to show separately their contribution to the improvement in the total discriminatory power.

While constantly keeping an eye on the bank credit selection problem, where discriminant models represent a straightforward solution, this methodology constitutes an extension of Discriminant Analysis technique in financial applications.