Contribuições de red flags para detecção de fraudes corporativas
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFG |
dARK ID: | ark:/38995/001300000bq9f |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/10523 |
Resumo: | Research has shown the importance of corporate fraud risk red flags from Cressey's (1953) fraud risk theory. Despite presenting false positives, they can identify a fraudulent situation at an early stage. However, the analysis of the use of financial indicators from financial statements has not yet received due attention from scientific research due to their degree of relevance. Thus, there is timely research that has empirically explored the ability of a set of red flags to help identify signs of fraud. In this sense, the objective of this research is to investigate the contributions of red flags obtained from financial reports in the detection of corporate fraud. In order to achieve the proposed objective, non-financial publicly traded companies with shares traded on the Brazilian stock exchange, called B3 (Brasil Bolsa Balcão), were selected, totaling 277 companies. To construct the database used in the variables analyzed, the information present in the companies' explanatory notes, in the Thonsom Reuters® database, on the website of the Commission of Monetary Values (CVM) and the Federal Police, was considered. For the selection of companies, the years between 2008 and 2018 were considered. For the selection of variables, the period was from 2006 to 2018, allowing data to be collected before the fraud occurred. The method chosen was Logistic Regression for panel data. Indicators identified in the literature with potential to identify evidence of fraud were selected. The variables collected were audit firm, debt, inventory increase, profitability and operating losses. The results confirmed the positive association between liability size and fraud risk. For the other red flags addressed, no statistical significance was found to suggest possible contributions. The findings of the research contribute to the discussion of the theme regarding the prevention of corporate fraud. |
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Piscoya Diaz, Mário Ernestohttp://lattes.cnpq.br/8921949936090276Piscoya Diaz, Mário ErnestoRech, Ilírio JoséMurcia, Fernando Dal-RiPundrinch, Gabriel Pereirahttp://lattes.cnpq.br/8715679995997124Nascimento, Monize Ramos do2020-09-02T11:25:05Z2020-09-02T11:25:05Z2020-01-15NASCIMENTO, Monize Ramos do. Contribuições de red flags para detecção de fraudes corporativas. 2020. 70 f. Dissertação (Mestrado em Ciências Contábeis) - Universidade Federal de Goiás, Goiânia, 2020.http://repositorio.bc.ufg.br/tede/handle/tede/10523ark:/38995/001300000bq9fResearch has shown the importance of corporate fraud risk red flags from Cressey's (1953) fraud risk theory. Despite presenting false positives, they can identify a fraudulent situation at an early stage. However, the analysis of the use of financial indicators from financial statements has not yet received due attention from scientific research due to their degree of relevance. Thus, there is timely research that has empirically explored the ability of a set of red flags to help identify signs of fraud. In this sense, the objective of this research is to investigate the contributions of red flags obtained from financial reports in the detection of corporate fraud. In order to achieve the proposed objective, non-financial publicly traded companies with shares traded on the Brazilian stock exchange, called B3 (Brasil Bolsa Balcão), were selected, totaling 277 companies. To construct the database used in the variables analyzed, the information present in the companies' explanatory notes, in the Thonsom Reuters® database, on the website of the Commission of Monetary Values (CVM) and the Federal Police, was considered. For the selection of companies, the years between 2008 and 2018 were considered. For the selection of variables, the period was from 2006 to 2018, allowing data to be collected before the fraud occurred. The method chosen was Logistic Regression for panel data. Indicators identified in the literature with potential to identify evidence of fraud were selected. The variables collected were audit firm, debt, inventory increase, profitability and operating losses. The results confirmed the positive association between liability size and fraud risk. For the other red flags addressed, no statistical significance was found to suggest possible contributions. The findings of the research contribute to the discussion of the theme regarding the prevention of corporate fraud.Pesquisas demonstraram a importância de red flags a partir da teoria de risco de fraude, desenvolvida por Cressey (1953). Apesar de apresentarem falsos positivos, eles são capazes de identificar uma situação fraudulenta ainda em estágio inicial. No entanto, a análise do uso de indicadores advindos das demonstrações financeiras ainda não recebeu a devida atenção da pesquisa científica, dado o seu grau de relevância. Dessa forma, são oportunas pesquisas que explorarem empiricamente a capacidade de um conjunto de red flags contribuir para identificar indícios de fraude. Nesse sentido, o objetivo da presente pesquisa é investigar as contribuições dos red flags obtidos de relatórios financeiros na detecção de fraudes corporativas. Para o alcance do objetivo proposto, foram selecionadas as companhias não financeiras, abertas, com ações negociadas na bolsa de valores brasileira, denominada B3 (Brasil Bolsa Balcão), totalizando 277 empresas. Para construção da base de dados usados nas variáveis analisadas foram consideradas as informações presentes nas notas explicativas das empresas, na base Thonsom Reuters®, no site da Comissão de Valores Monetários (CVM) e da Polícia Federal. Para a seleção das empresas, foram considerado os anos entre 2008 e 2018. Já para a seleção das variáveis, o período foi de 2006 a 2018, possibilitando coletar dados antes do acontecimento da fraude. O método escolhido foi Regressão Logística para dados em painel. Foram selecionados indicadores identificados na literatura com potencial para identificar indícios de fraudes. As variáveis coletadas foram: firma de auditoria, endividamento, aumento de estoque, rentabilidade e perdas operacionais. Os resultados confirmaram a associação positiva entre o tamanho dos passivos e risco de fraude. Para os demais red flags abordados não foram encontradas significâncias estatísticas que sugerissem possíveis contribuições. Os achados da pesquisa contribuem na discussão da temática a respeito da prevenção de fraudes corporativas.Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2020-09-02T11:23:44Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Dissertacão - Monize Ramos do Nascimento - 2020.pdf: 1585530 bytes, checksum: 0d70fc96038257846cb60fcbc5dc3553 (MD5)Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2020-09-02T11:25:04Z (GMT) No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Dissertacão - Monize Ramos do Nascimento - 2020.pdf: 1585530 bytes, checksum: 0d70fc96038257846cb60fcbc5dc3553 (MD5)Made available in DSpace on 2020-09-02T11:25:05Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Dissertacão - Monize Ramos do Nascimento - 2020.pdf: 1585530 bytes, checksum: 0d70fc96038257846cb60fcbc5dc3553 (MD5) Previous issue date: 2020-01-15Fundação de Amparo à Pesquisa do Estado de GoiásporUniversidade Federal de GoiásPrograma de Pós-graduação em Ciências Contábeis (FACE)UFGBrasilFaculdade de Administração, Ciências Contábeis e Ciências Econômicas - FACE (RG)Attribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessFraudes corporativasRed flags e prevençãoCorporate fraudRed flags and preventionCIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO::CIENCIAS CONTABEISContribuições de red flags para detecção de fraudes corporativasRed flags contribution for corporate fraud detectioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis305005005005007163reponame:Repositório Institucional da UFGinstname:Universidade Federal de Goiás (UFG)instacron:UFGLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.bc.ufg.br/tede/bitstreams/dd734a53-57fe-4b43-a274-dd1d34785384/download8a4605be74aa9ea9d79846c1fba20a33MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811http://repositorio.bc.ufg.br/tede/bitstreams/1088ea8e-8a3a-4dad-adf5-cc1cbada711f/downloade39d27027a6cc9cb039ad269a5db8e34MD52ORIGINALDissertacão - Monize Ramos do Nascimento - 2020.pdfDissertacão - Monize Ramos do Nascimento - 2020.pdfapplication/pdf1585530http://repositorio.bc.ufg.br/tede/bitstreams/54e003b1-a130-4a7d-97e7-1b46b4c40699/download0d70fc96038257846cb60fcbc5dc3553MD53tede/105232020-09-02 08:25:06.218http://creativecommons.org/licenses/by-nc-nd/3.0/br/Attribution-NonCommercial-NoDerivs 3.0 Brazilopen.accessoai:repositorio.bc.ufg.br:tede/10523http://repositorio.bc.ufg.br/tedeRepositório InstitucionalPUBhttp://repositorio.bc.ufg.br/oai/requesttasesdissertacoes.bc@ufg.bropendoar:2020-09-02T11:25:06Repositório Institucional da UFG - Universidade Federal de Goiás (UFG)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 |
dc.title.pt_BR.fl_str_mv |
Contribuições de red flags para detecção de fraudes corporativas |
dc.title.alternative.eng.fl_str_mv |
Red flags contribution for corporate fraud detection |
title |
Contribuições de red flags para detecção de fraudes corporativas |
spellingShingle |
Contribuições de red flags para detecção de fraudes corporativas Nascimento, Monize Ramos do Fraudes corporativas Red flags e prevenção Corporate fraud Red flags and prevention CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO::CIENCIAS CONTABEIS |
title_short |
Contribuições de red flags para detecção de fraudes corporativas |
title_full |
Contribuições de red flags para detecção de fraudes corporativas |
title_fullStr |
Contribuições de red flags para detecção de fraudes corporativas |
title_full_unstemmed |
Contribuições de red flags para detecção de fraudes corporativas |
title_sort |
Contribuições de red flags para detecção de fraudes corporativas |
author |
Nascimento, Monize Ramos do |
author_facet |
Nascimento, Monize Ramos do |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Piscoya Diaz, Mário Ernesto |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/8921949936090276 |
dc.contributor.referee1.fl_str_mv |
Piscoya Diaz, Mário Ernesto |
dc.contributor.referee2.fl_str_mv |
Rech, Ilírio José |
dc.contributor.referee3.fl_str_mv |
Murcia, Fernando Dal-Ri |
dc.contributor.referee4.fl_str_mv |
Pundrinch, Gabriel Pereira |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/8715679995997124 |
dc.contributor.author.fl_str_mv |
Nascimento, Monize Ramos do |
contributor_str_mv |
Piscoya Diaz, Mário Ernesto Piscoya Diaz, Mário Ernesto Rech, Ilírio José Murcia, Fernando Dal-Ri Pundrinch, Gabriel Pereira |
dc.subject.por.fl_str_mv |
Fraudes corporativas Red flags e prevenção |
topic |
Fraudes corporativas Red flags e prevenção Corporate fraud Red flags and prevention CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO::CIENCIAS CONTABEIS |
dc.subject.eng.fl_str_mv |
Corporate fraud Red flags and prevention |
dc.subject.cnpq.fl_str_mv |
CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO::CIENCIAS CONTABEIS |
description |
Research has shown the importance of corporate fraud risk red flags from Cressey's (1953) fraud risk theory. Despite presenting false positives, they can identify a fraudulent situation at an early stage. However, the analysis of the use of financial indicators from financial statements has not yet received due attention from scientific research due to their degree of relevance. Thus, there is timely research that has empirically explored the ability of a set of red flags to help identify signs of fraud. In this sense, the objective of this research is to investigate the contributions of red flags obtained from financial reports in the detection of corporate fraud. In order to achieve the proposed objective, non-financial publicly traded companies with shares traded on the Brazilian stock exchange, called B3 (Brasil Bolsa Balcão), were selected, totaling 277 companies. To construct the database used in the variables analyzed, the information present in the companies' explanatory notes, in the Thonsom Reuters® database, on the website of the Commission of Monetary Values (CVM) and the Federal Police, was considered. For the selection of companies, the years between 2008 and 2018 were considered. For the selection of variables, the period was from 2006 to 2018, allowing data to be collected before the fraud occurred. The method chosen was Logistic Regression for panel data. Indicators identified in the literature with potential to identify evidence of fraud were selected. The variables collected were audit firm, debt, inventory increase, profitability and operating losses. The results confirmed the positive association between liability size and fraud risk. For the other red flags addressed, no statistical significance was found to suggest possible contributions. The findings of the research contribute to the discussion of the theme regarding the prevention of corporate fraud. |
publishDate |
2020 |
dc.date.accessioned.fl_str_mv |
2020-09-02T11:25:05Z |
dc.date.available.fl_str_mv |
2020-09-02T11:25:05Z |
dc.date.issued.fl_str_mv |
2020-01-15 |
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info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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masterThesis |
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publishedVersion |
dc.identifier.citation.fl_str_mv |
NASCIMENTO, Monize Ramos do. Contribuições de red flags para detecção de fraudes corporativas. 2020. 70 f. Dissertação (Mestrado em Ciências Contábeis) - Universidade Federal de Goiás, Goiânia, 2020. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/10523 |
dc.identifier.dark.fl_str_mv |
ark:/38995/001300000bq9f |
identifier_str_mv |
NASCIMENTO, Monize Ramos do. Contribuições de red flags para detecção de fraudes corporativas. 2020. 70 f. Dissertação (Mestrado em Ciências Contábeis) - Universidade Federal de Goiás, Goiânia, 2020. ark:/38995/001300000bq9f |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/10523 |
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por |
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30 |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
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openAccess |
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Universidade Federal de Goiás |
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UFG |
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Faculdade de Administração, Ciências Contábeis e Ciências Econômicas - FACE (RG) |
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Universidade Federal de Goiás |
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