Divergências entre os modelos de análise discriminante de Kanitz, Matias e Elizabetsky- um estudo exploratório no novo Mercado da Bovespa.

Abstract

Discriminant analysis is based on the use of multivariate statistical techniques through which it is possible to predict insolvency of companies, based on data obtained from the financial statements. With this focus, this study stands out for assisting in the process of credit concession, playing a relevant role for development of several researches in the area. It is inferred that in the first research studies conducted in Brazil, with the intention of to estimate bankruptcy prediction models aimed at improving the credit analysis process, date from the 1970’s. To the insolvency or solvency prevision models construction were used the statistical probability prediction from resources of discriminant analysis. The models analyzed in this work were developed by Stephen Charles Kanitz (1978), Roberto Elizabetsky (1976) and Matias Alberto Borges (1978). The models highlighted were estimated from a combination of the financial indicators that measure and classify statistically a solvency/insolvency state of a company. Given the relevance of authors' perceptions and the use of different indexes for models prediction, we intend to evaluate the following question: Are there differences between discriminant analysis models about the solvent and insolvent company’s classification proposed by Kanitz, Elizabetsky and Matias? The objective of this work is to propose an exploratory study into companies listed on the New Market of the Stock Exchange of São Paulo (BOVESPA), with approach mainly at checking the differences between insolvency models prediction proposed by Kanitz, Elizabetsky and Matthias. This study discusses the issue concerning 75 non-financial companies listed on BOVESPA. The research methodology can be classified as exploratory according to the purposes and bibliographic, according to the means. The results obtained show a significant number of contradictions when occurs the confrontation of the models. The study suggests that credit analysis from discriminant models should be run based on more than one technique for a good level of prediction on insolvency forecast.