Exportação concluída — 

Previsão de rating empresarial com o uso de índices contábeis

Detalhes bibliográficos
Ano de defesa: 2021
Autor(a) principal: Ferreira, Danilo Alves Veras
Orientador(a): Souza, Sérgio Aquino de
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://www.repositorio.ufc.br/handle/riufc/59886
Resumo: Credit rating is a tool that provides interested professionals with an efficient way to analyze a company’s credit risk. In this work, forecast models for the credit rating of Brazilian companies were estimated, using the S&P credit rating and accounting ratios for the period 2017 to 2019. The rating of the companies in the sample is between B and AAA ratings. The first estimated model was the ordered post-lasso probit with five statistically significant variables. The results of this model corroborate those obtained by Damasceno et al. (2008), but they have a higher hit rate, correctly predicting 72.39% of the sample. The model had a low hit rate for rating categories B, A and AAA, while category AA had a high hit rate of 97.65%. The third estimated model was the ordered probit model with the variables selected by Damasceno et al. (2008), using data from this survey. The two variables present in the model were statistically significant. The hit rate of the model was 64.92%, being lower than the first model. The third model was not able to predict any ratings B or AAA, but correctly predicted all ratings AA.
id UFC-7_439a1eb7d47bf618c30770908da0be5c
oai_identifier_str oai:repositorio.ufc.br:riufc/59886
network_acronym_str UFC-7
network_name_str Repositório Institucional da Universidade Federal do Ceará (UFC)
repository_id_str
spelling Ferreira, Danilo Alves VerasSouza, Sérgio Aquino de2021-08-09T17:23:18Z2021-08-09T17:23:18Z2021FERREIRA, Danilo Alves Veras. Previsão de rating empresarial com o uso de índices contábeis. 2021. 29f. Dissertação (Mestrado em Economia de Empresas) - Faculdade de Economia, Administração, Atuária e Contabilidade - FEAAC, Programa de Economia Profissional - PEP, Universidade Federal do Ceará - UFC, Fortaleza (CE), 2021.http://www.repositorio.ufc.br/handle/riufc/59886Credit rating is a tool that provides interested professionals with an efficient way to analyze a company’s credit risk. In this work, forecast models for the credit rating of Brazilian companies were estimated, using the S&P credit rating and accounting ratios for the period 2017 to 2019. The rating of the companies in the sample is between B and AAA ratings. The first estimated model was the ordered post-lasso probit with five statistically significant variables. The results of this model corroborate those obtained by Damasceno et al. (2008), but they have a higher hit rate, correctly predicting 72.39% of the sample. The model had a low hit rate for rating categories B, A and AAA, while category AA had a high hit rate of 97.65%. The third estimated model was the ordered probit model with the variables selected by Damasceno et al. (2008), using data from this survey. The two variables present in the model were statistically significant. The hit rate of the model was 64.92%, being lower than the first model. The third model was not able to predict any ratings B or AAA, but correctly predicted all ratings AA.O rating de crédito é uma ferramenta que proporciona aos profissionais interessados uma maneira eficiente de analisar o risco de crédito de uma empresa. Nesse trabalho foram estimados modelos previsão de rating de crédito de empresas brasileiras, utilizando o rating de crédito da S&P e índices contábeis para o período de 2017 a 2019. O rating das empresas da amostra está entre as avaliações B e AAA. O primeiro modelo estimado foi o pós-lasso probit ordenado com cinco variáveis estatisticamente significantes. Os resultados desse modelo corroboram com os obtidos por Damasceno et al. (2008), nas contam com uma taxa de acerto maior, prevendo corretamente 72,39% da amostra. O modelo apresentou baixa taxa de acerto para as categorias de rating B, A e AAA, já a categoria AA teve uma alta taxa de acerto de 97,65%. O terceiro modelo estimado foi o modelo probit ordenado com as variáveis selecionadas por Damasceno et al. (2008), utilizando os dados dessa pesquisa. As duas variáveis presentes no modelo foram estatisticamente significantes. A taxa de acerto do modelo foi de 64,92%, sendo inferior ao primeiro modelo. O terceiro modelo não foi capaz de prever nenhum rating B ou AAA, mas previu corretamente todos os ratings AA.Rating de créditoProbit ordenadoLassoContabilidadePrevisão de rating empresarial com o uso de índices contábeisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisporreponame:Repositório Institucional da Universidade Federal do Ceará (UFC)instname:Universidade Federal do Ceará (UFC)instacron:UFCinfo:eu-repo/semantics/openAccessLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.ufc.br/bitstream/riufc/59886/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52ORIGINAL2021_dis_davferreira.pdf2021_dis_davferreira.pdfapplication/pdf415024http://repositorio.ufc.br/bitstream/riufc/59886/1/2021_dis_davferreira.pdf82856e1230a7de04aa9390e55ee4b4cbMD51riufc/598862023-07-12 15:57:50.228oai:repositorio.ufc.br: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Repositório InstitucionalPUBhttp://www.repositorio.ufc.br/ri-oai/requestbu@ufc.br || repositorio@ufc.bropendoar:2023-07-12T18:57:50Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)false
dc.title.pt_BR.fl_str_mv Previsão de rating empresarial com o uso de índices contábeis
title Previsão de rating empresarial com o uso de índices contábeis
spellingShingle Previsão de rating empresarial com o uso de índices contábeis
Ferreira, Danilo Alves Veras
Rating de crédito
Probit ordenado
Lasso
Contabilidade
title_short Previsão de rating empresarial com o uso de índices contábeis
title_full Previsão de rating empresarial com o uso de índices contábeis
title_fullStr Previsão de rating empresarial com o uso de índices contábeis
title_full_unstemmed Previsão de rating empresarial com o uso de índices contábeis
title_sort Previsão de rating empresarial com o uso de índices contábeis
author Ferreira, Danilo Alves Veras
author_facet Ferreira, Danilo Alves Veras
author_role author
dc.contributor.author.fl_str_mv Ferreira, Danilo Alves Veras
dc.contributor.advisor1.fl_str_mv Souza, Sérgio Aquino de
contributor_str_mv Souza, Sérgio Aquino de
dc.subject.por.fl_str_mv Rating de crédito
Probit ordenado
Lasso
Contabilidade
topic Rating de crédito
Probit ordenado
Lasso
Contabilidade
description Credit rating is a tool that provides interested professionals with an efficient way to analyze a company’s credit risk. In this work, forecast models for the credit rating of Brazilian companies were estimated, using the S&P credit rating and accounting ratios for the period 2017 to 2019. The rating of the companies in the sample is between B and AAA ratings. The first estimated model was the ordered post-lasso probit with five statistically significant variables. The results of this model corroborate those obtained by Damasceno et al. (2008), but they have a higher hit rate, correctly predicting 72.39% of the sample. The model had a low hit rate for rating categories B, A and AAA, while category AA had a high hit rate of 97.65%. The third estimated model was the ordered probit model with the variables selected by Damasceno et al. (2008), using data from this survey. The two variables present in the model were statistically significant. The hit rate of the model was 64.92%, being lower than the first model. The third model was not able to predict any ratings B or AAA, but correctly predicted all ratings AA.
publishDate 2021
dc.date.accessioned.fl_str_mv 2021-08-09T17:23:18Z
dc.date.available.fl_str_mv 2021-08-09T17:23:18Z
dc.date.issued.fl_str_mv 2021
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.citation.fl_str_mv FERREIRA, Danilo Alves Veras. Previsão de rating empresarial com o uso de índices contábeis. 2021. 29f. Dissertação (Mestrado em Economia de Empresas) - Faculdade de Economia, Administração, Atuária e Contabilidade - FEAAC, Programa de Economia Profissional - PEP, Universidade Federal do Ceará - UFC, Fortaleza (CE), 2021.
dc.identifier.uri.fl_str_mv http://www.repositorio.ufc.br/handle/riufc/59886
identifier_str_mv FERREIRA, Danilo Alves Veras. Previsão de rating empresarial com o uso de índices contábeis. 2021. 29f. Dissertação (Mestrado em Economia de Empresas) - Faculdade de Economia, Administração, Atuária e Contabilidade - FEAAC, Programa de Economia Profissional - PEP, Universidade Federal do Ceará - UFC, Fortaleza (CE), 2021.
url http://www.repositorio.ufc.br/handle/riufc/59886
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da Universidade Federal do Ceará (UFC)
instname:Universidade Federal do Ceará (UFC)
instacron:UFC
instname_str Universidade Federal do Ceará (UFC)
instacron_str UFC
institution UFC
reponame_str Repositório Institucional da Universidade Federal do Ceará (UFC)
collection Repositório Institucional da Universidade Federal do Ceará (UFC)
bitstream.url.fl_str_mv http://repositorio.ufc.br/bitstream/riufc/59886/2/license.txt
http://repositorio.ufc.br/bitstream/riufc/59886/1/2021_dis_davferreira.pdf
bitstream.checksum.fl_str_mv 8a4605be74aa9ea9d79846c1fba20a33
82856e1230a7de04aa9390e55ee4b4cb
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
repository.name.fl_str_mv Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)
repository.mail.fl_str_mv bu@ufc.br || repositorio@ufc.br
_version_ 1847793154015625216