A model for the classification of companies credit risk
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Revista Contabilidade & Finanças (Online) |
Texto Completo: | https://www.revistas.usp.br/rcf/article/view/34249 |
Resumo: | The process of credit risk management in financial institutions has been revised in recent years. In this context, large banks have developed and implemented several new techniques for measuring borrowers credit risk. This research aims to develop a risk classification model to assess the credit risk of companies in the Brazilian market. The model was built based on a sample of publicly traded companies classified as solvent or insolvent during the period from 1994 to 2004. Logistic regression was used to develop the model. The independent variables of the model are financial ratios, calculated from the financial statements and used as proxies of companies economic and financial situation. The validation of the model was done using the Jackknife method and a ROC Curve. The results of the study indicate that the risk classification model developed predicts default events one year prior to failure with good level of accuracy. The results also indicate that financial statements contain information that allow for the classification of companies as probably solvent or probably insolvent. |
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A model for the classification of companies credit risk Modelo de classificação de risco de crédito de empresas Modelo de risco de créditoEvento de defaultEmpresas de capital abertoRegressão logísticaÍndices financeirosCredit risk modelDefault eventPublicly traded companiesLogistic regressionFinancial ratios The process of credit risk management in financial institutions has been revised in recent years. In this context, large banks have developed and implemented several new techniques for measuring borrowers credit risk. This research aims to develop a risk classification model to assess the credit risk of companies in the Brazilian market. The model was built based on a sample of publicly traded companies classified as solvent or insolvent during the period from 1994 to 2004. Logistic regression was used to develop the model. The independent variables of the model are financial ratios, calculated from the financial statements and used as proxies of companies economic and financial situation. The validation of the model was done using the Jackknife method and a ROC Curve. The results of the study indicate that the risk classification model developed predicts default events one year prior to failure with good level of accuracy. The results also indicate that financial statements contain information that allow for the classification of companies as probably solvent or probably insolvent. O processo de gerenciamento de risco de crédito em instituições financeiras vem passando por uma revisão ao longo dos últimos anos. Nesse contexto, diversas novas técnicas de mensuração de risco de crédito e tomadores têm sido desenvolvidas e implementadas por grandes Bancos. O objetivo desta pesquisa é desenvolver um modelo de classificação de risco para avaliar o risco de crédito de empresas no mercado brasileiro. O modelo foi construído com base em uma amostra de empresas de capital aberto classificadas como solventes ou insolventes no período entre 1994 e 2004. A técnica estatística utilizada no desenvolvimento do modelo foi a regressão logística. As variáveis independentes são índices financeiros calculados a partir das demonstrações contábeis e utilizados para representar a situação econômico-financeira das empresas. A validação do modelo foi efetuada utilizando o método Jackknife e uma Curva ROC. Os resultados do estudo indicam que o modelo de classificação de risco desenvolvido prevê eventos de default com um ano de antecedência com bom nível de acurácia. Os resultados, também, indicam que as demonstrações contábeis contêm informações que possibilitam a classificação das empresas como prováveis solventes ou prováveis insolventes. Universidade de São Paulo. Faculdade de Economia, Administração, Contabilidade e Atuária2008-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/rcf/article/view/3424910.1590/S1519-70772008000100003Revista Contabilidade & Finanças; v. 19 n. 46 (2008); 18-29 Revista Contabilidade & Finanças; Vol. 19 No. 46 (2008); 18-29 Revista Contabilidade & Finanças; Vol. 19 Núm. 46 (2008); 18-29 1808-057X1519-7077reponame:Revista Contabilidade & Finanças (Online)instname:Universidade de São Paulo (USP)instacron:USPporhttps://www.revistas.usp.br/rcf/article/view/34249/36981Copyright (c) 2018 Revista Contabilidade & Finançasinfo:eu-repo/semantics/openAccessBrito, Giovani Antonio SilvaAssaf Neto, Alexandre2012-07-21T18:22:35Zoai:revistas.usp.br:article/34249Revistahttp://www.revistas.usp.br/rcf/indexPUBhttps://old.scielo.br/oai/scielo-oai.phprecont@usp.br||recont@usp.br1808-057X1519-7077opendoar:2012-07-21T18:22:35Revista Contabilidade & Finanças (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
A model for the classification of companies credit risk Modelo de classificação de risco de crédito de empresas |
title |
A model for the classification of companies credit risk |
spellingShingle |
A model for the classification of companies credit risk Brito, Giovani Antonio Silva Modelo de risco de crédito Evento de default Empresas de capital aberto Regressão logística Índices financeiros Credit risk model Default event Publicly traded companies Logistic regression Financial ratios |
title_short |
A model for the classification of companies credit risk |
title_full |
A model for the classification of companies credit risk |
title_fullStr |
A model for the classification of companies credit risk |
title_full_unstemmed |
A model for the classification of companies credit risk |
title_sort |
A model for the classification of companies credit risk |
author |
Brito, Giovani Antonio Silva |
author_facet |
Brito, Giovani Antonio Silva Assaf Neto, Alexandre |
author_role |
author |
author2 |
Assaf Neto, Alexandre |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Brito, Giovani Antonio Silva Assaf Neto, Alexandre |
dc.subject.por.fl_str_mv |
Modelo de risco de crédito Evento de default Empresas de capital aberto Regressão logística Índices financeiros Credit risk model Default event Publicly traded companies Logistic regression Financial ratios |
topic |
Modelo de risco de crédito Evento de default Empresas de capital aberto Regressão logística Índices financeiros Credit risk model Default event Publicly traded companies Logistic regression Financial ratios |
description |
The process of credit risk management in financial institutions has been revised in recent years. In this context, large banks have developed and implemented several new techniques for measuring borrowers credit risk. This research aims to develop a risk classification model to assess the credit risk of companies in the Brazilian market. The model was built based on a sample of publicly traded companies classified as solvent or insolvent during the period from 1994 to 2004. Logistic regression was used to develop the model. The independent variables of the model are financial ratios, calculated from the financial statements and used as proxies of companies economic and financial situation. The validation of the model was done using the Jackknife method and a ROC Curve. The results of the study indicate that the risk classification model developed predicts default events one year prior to failure with good level of accuracy. The results also indicate that financial statements contain information that allow for the classification of companies as probably solvent or probably insolvent. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-04-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.revistas.usp.br/rcf/article/view/34249 10.1590/S1519-70772008000100003 |
url |
https://www.revistas.usp.br/rcf/article/view/34249 |
identifier_str_mv |
10.1590/S1519-70772008000100003 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://www.revistas.usp.br/rcf/article/view/34249/36981 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2018 Revista Contabilidade & Finanças info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2018 Revista Contabilidade & Finanças |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade de São Paulo. Faculdade de Economia, Administração, Contabilidade e Atuária |
publisher.none.fl_str_mv |
Universidade de São Paulo. Faculdade de Economia, Administração, Contabilidade e Atuária |
dc.source.none.fl_str_mv |
Revista Contabilidade & Finanças; v. 19 n. 46 (2008); 18-29 Revista Contabilidade & Finanças; Vol. 19 No. 46 (2008); 18-29 Revista Contabilidade & Finanças; Vol. 19 Núm. 46 (2008); 18-29 1808-057X 1519-7077 reponame:Revista Contabilidade & Finanças (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Revista Contabilidade & Finanças (Online) |
collection |
Revista Contabilidade & Finanças (Online) |
repository.name.fl_str_mv |
Revista Contabilidade & Finanças (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
recont@usp.br||recont@usp.br |
_version_ |
1787713775973957632 |