Anta Klogiri, "Review of corporate Bankruptcy risk models", Diploma Work, School of Production Engineering and Management, Technical University of Crete, Chania, Greece, 2021
https://doi.org/10.26233/heallink.tuc.88435
This paper addresses the issue of business bankruptcy and the models used to predict the corporate failure.Firstly, a brief reference is made to the concept of bankruptcy and the legal dimension of it. The conditions for bankruptcy are analyzed, as well as the main factors that lead to it.Secondly, the main models used for predicting corporate failure are presented. Firstly, reference is made to Univariate Analysis (UA), as a method of forecasting corporate risk, and to the Beaver model (1966).Next, the method of Multivariate Discrete Analysis (MDA) and Altman's Z-Score (1968) and ZETA (1977) models are analyzed.Subsequently, the main Probability Models widely used in the business bankruptcy forecasting process are indicated. Specifically, reference is made to the Linear (LPM), Logarithmic (Logit) and Normal (Probit) Probability Model.Moreover, alternative methods of predicting bankruptcy are presented. Such methods are Hazard Models, Artificial Neural Networks, Decision Support Systems (DSS), Machine Learning Decision Trees, Rough Set, DEHA systems (Dynamic Event History Analysis), the CUSUM model, the Fussy Knowledge - Based Decision Aiding method and the theory of "Disaster" or "chaos".Finally, the general conclusions that emerge from the study of corporate failure prediction models are presented and the relevant literature is submitted.