Previsão e análise de risco de insolvência de empresas do índice agronegócio B3 no Brasil

• Ano de defesa: 2025
• Autor(a) principal: Silva, Vitória Biana da
• Orientador(a): Campos, Kilmer Coelho
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Área do conhecimento CNPq: CNPQ::CIENCIAS SOCIAIS APLICADAS
• Link de acesso: http://repositorio.ufc.br/handle/riufc/80905

Agribusiness is one of the most strategic and dynamic sectors of the Brazilian economy, accounting for a significant share of the country's Gross Domestic Product (GDP) and exports. Given its importance, analyzing the financial health of companies in this sector is crucial not only for corporate management but also for anticipating critical scenarios, such as bankruptcy prevention and mitigating itssocial and financial repercussions. In this context, this study aims to predict and analyze the risk of insolvency among companies listed on the B3 Agribusiness Index (IAGRO) in Brazil. A sample of 30 publicly traded agribusiness companies was used for this purpose. Financial data were collected from the Balance Sheet and Income Statement, covering the period from 2019 to 2022. These data were applied to three widely recognized insolvency prediction models in the literature: Elizabetsky (1976), Kanitz (1978), and Matias (1978). Descriptive analyses were employed to understand the financial profile of the companies, as well as the calculation of the insolvency factor under deterministic and risk conditions. For risk analysis, the Monte Carlo method was used, implemented through the @RISK software, which allowed for the simulation of different scenarios and the assessment of insolvency probability under uncertainty. Discriminant analysis identified the main indicators differentiating solvent and insolvent companies, highlighting net margin, overall indebtedness level, the ratio of financing and bank loans to current assets, and, finally, equity. The discriminant function of the Elizabetsky model demonstrated greater effectiveness in classifying original cases, showing superior ability to distinguish between solvent and insolvent companies compared to the Kanitz and Matias models. The insolvency risk analysis revealed that the pandemic period (starting in 2020) heterogeneously impacted the insolvency risk of companies in the sector. While the Elizabetsky model indicated a high risk of insolvency during this period, the Kanitz and Matias models pointed to a relatively low risk. This divergence underscores the importance of considering multiple approaches in risk assessment, as well as the need to contextualize results within the specific economic and social scenario.