LONG-TERM RISK CLASS MIGRATIONS OF NON-BANKRUPT AND BANKRUPT ENTERPRISES

Tomasz Korol

doi:10.3846/jbem.2020.12224

Abstract

This paper investigates how the process of going bankrupt can be recognized much earlier by enterprises than by traditional forecasting models. The presented studies focus on the assessment of credit risk classes and on determination of the differences in risk class migrations between non-bankrupt enterprises and future insolvent firms. For this purpose, the author has developed a model of a Kohonen artificial neural network to determine six different classes of risk. Long-term analysis horizon of 15 years before the enterprises went bankrupt was conducted. This long forecasting horizon allows one to identify, visualize and compare the intensity and pattern of changes in risk classes during the 15-year trajectory of development between two separate groups of companies (150 bankrupt and 150 non-bankrupt firms). The effectiveness of the forecast of the developed model was compared to three popular statistical models that predict the financial failure of companies. These studies represent one of the first attempts in the literature to identify the long-term behavioral pattern differences between future “good” and “bad” enterprises from the perspective of risk class migrations.

Highlights

In the literature, credit risk is mainly considered from the viewpoint of banks granting loans to companies (Bluhm et al, 2003; Schonfeld et al, 2018; Shimko, 2004)
For all 600 enterprises, the author calculated the value of 30 financial ratios for the last 15 years before the moment that the company was at risk of bankruptcy or was considered a good firm
The gray areas represent the classification of a company in danger of bankruptcy, and the white area shows the classification of non-bankrupt firms

Summary

Introduction

Credit risk is mainly considered from the viewpoint of banks granting loans to companies (Bluhm et al, 2003; Schonfeld et al, 2018; Shimko, 2004). Global model of self-organizing map (Kohonen model) with the assessment of six different risk classes for wide variety of enterprises from different regions of the world Such a model with a risk map can enhance the prediction of financial failures in three ways. It can identify and visualize the long-term pattern of firm collapse (in the form of migration between individual classes); second, it can more precisely define the level of risk (the most popular previous multivariate discriminant analyses models only specify if the company is at risk with no identification of the level of such risk); and third, it can improve the stability of forecast effectiveness.

Characteristics of the common types of bankrupting enterprises

Variable and sample selection

Basic concepts of Kohonen model

Traditional statistical forecasting models used in the studies

Corporate bankruptcy forecasting model

Results and discussion

Conclusions