Corporate bankruptcy prediction: a comparison of logistic regression and random forest on portuguese company data

Detalhes bibliográficos
Autor(a) principal: Bruhn, Sina
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/105622
Resumo: In the currentfield ofbankruptcy prediction studies, the geographical focus usually is on larger economiesrather than economies the size of Portugal. For the purpose of this studyfinancial statement data from five consecutive years prior to the event of bankruptcy in 2017 was selected. Within the data328,542healthy and unhealthy Portuguese companieswere included.Two predictive models using the Logistic Regression and Random Forest algorithm were fitted to be able to predict bankruptcy.Both developed models deliver good results even though the RandomForestmodel performs slightly better than the one based on Logistic Regression.
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spelling Corporate bankruptcy prediction: a comparison of logistic regression and random forest on portuguese company dataBankruptcy predictionLogistic regressionRandom forestPortugalDomínio/Área Científica::Ciências Sociais::Economia e GestãoIn the currentfield ofbankruptcy prediction studies, the geographical focus usually is on larger economiesrather than economies the size of Portugal. For the purpose of this studyfinancial statement data from five consecutive years prior to the event of bankruptcy in 2017 was selected. Within the data328,542healthy and unhealthy Portuguese companieswere included.Two predictive models using the Logistic Regression and Random Forest algorithm were fitted to be able to predict bankruptcy.Both developed models deliver good results even though the RandomForestmodel performs slightly better than the one based on Logistic Regression.Pereira, Ricardo João GilRUNBruhn, Sina2020-10-15T08:40:37Z2020-01-232020-01-032020-01-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/105622TID:202493709enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:50:49Zoai:run.unl.pt:10362/105622Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:40:32.203013Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Corporate bankruptcy prediction: a comparison of logistic regression and random forest on portuguese company data
title Corporate bankruptcy prediction: a comparison of logistic regression and random forest on portuguese company data
spellingShingle Corporate bankruptcy prediction: a comparison of logistic regression and random forest on portuguese company data
Bruhn, Sina
Bankruptcy prediction
Logistic regression
Random forest
Portugal
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Corporate bankruptcy prediction: a comparison of logistic regression and random forest on portuguese company data
title_full Corporate bankruptcy prediction: a comparison of logistic regression and random forest on portuguese company data
title_fullStr Corporate bankruptcy prediction: a comparison of logistic regression and random forest on portuguese company data
title_full_unstemmed Corporate bankruptcy prediction: a comparison of logistic regression and random forest on portuguese company data
title_sort Corporate bankruptcy prediction: a comparison of logistic regression and random forest on portuguese company data
author Bruhn, Sina
author_facet Bruhn, Sina
author_role author
dc.contributor.none.fl_str_mv Pereira, Ricardo João Gil
RUN
dc.contributor.author.fl_str_mv Bruhn, Sina
dc.subject.por.fl_str_mv Bankruptcy prediction
Logistic regression
Random forest
Portugal
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Bankruptcy prediction
Logistic regression
Random forest
Portugal
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description In the currentfield ofbankruptcy prediction studies, the geographical focus usually is on larger economiesrather than economies the size of Portugal. For the purpose of this studyfinancial statement data from five consecutive years prior to the event of bankruptcy in 2017 was selected. Within the data328,542healthy and unhealthy Portuguese companieswere included.Two predictive models using the Logistic Regression and Random Forest algorithm were fitted to be able to predict bankruptcy.Both developed models deliver good results even though the RandomForestmodel performs slightly better than the one based on Logistic Regression.
publishDate 2020
dc.date.none.fl_str_mv 2020-10-15T08:40:37Z
2020-01-23
2020-01-03
2020-01-23T00:00:00Z
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.uri.fl_str_mv http://hdl.handle.net/10362/105622
TID:202493709
url http://hdl.handle.net/10362/105622
identifier_str_mv TID:202493709
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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