Antecipando dificuldades financeiras nas organizações

Autores

DOI:

https://doi.org/10.5585/exactaep.2021.17494

Palavras-chave:

Risco de Crédito, Dificuldade financeira corporativa, Inteligência artificial, Extreme gradient boosting, Importância de variáveis.

Resumo

O objetivo deste estudo é apresentar um modelo de previsão de Dificuldades Financeiras (DF) a partir da perspectiva das técnicas de aprendizado de máquina (TAMs). Aplicamos e comparamos os modelos XGBoost, Random Forest e Regressão Logística usando indicadores financeiros para buscar melhores previsões das DFs um ano antes do evento em empresas latino-americanas no período de 2000 a 2017. Nossos resultados mostraram que as TAMs superam o modelo de logit, atingindo uma precisão geral de 96 % (XGboost). Além disso, cinco indicadores foram relevantes para o seu sucesso. O estudo amplia o conhecimento e as discussões ao enfocar o poder preditivo na comparação entre os modelos, destacando os benefícios do uso de algoritmos aplicados à pesquisa financeira. Auxilia na gestão de riscos, na prevenção de perdas, permitindo maior equilíbrio e saúde para o sistema financeiro’, que contribui para o desenvolvimento econômico, social e sustentável de uma sociedade.

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Biografia do Autor

Flávio Luiz de Moraes Barboza, Federal University of Uberlândia Professor of Finance at School of Business and Management - Federal University of Uberlândia

Professor of Finance at School of Business and Management - Federal University of Uberlândia. Bachalor's degree in Math - UNESP (2003), MSc. in Physics - UNESP (2007), and PhD in Finance - Mackenzie (2015). Experience in Math applied to Finance, focusing on computational modelling for Risk Management, Credit Risk, Financial Performance, and Investment Analysis.

Denize Lemos Duarte, Federal University of Uberlândia Professor at the Faculty of Accounting Sciences

Experience in Finance, Management, Controllership, Administrative, Logistics, Systems and Methods, Tax and Accounting, FP&A (Tactical and Strategic), Costs, computational modeling for Risk Management, Credit Risk, Financial Performance, and Investment Analysis,  Budget and Commercial Management in large and medium companies, with a degree in Accounting, MBA in Finance and Planning Strategic by the Institute of Economics and Master in Administration.

Michele Aparecida Cunha, Federal University of Uberlândia Master's degree in Business Administration from the Faculty of Business

Graduated in Business Administration and Accounting from the University Center of Patos de Minas (UNIPAM), with a specialization in Business Strategy Management and an MBA in People Management. He is currently a Master's student in Administration at the Faculty of Management and Business (FAGEN) of the Federal University of Uberlândia (UFU). Acts as a substitute professor at the Faculty of Management and Business at the Federal University of Uberlândia, teaching classes at the Undergraduate level. His research interests involve: Third Sector, Organizational Behavior, Consumer Behavior, Business Consulting and Risk Management.

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Publicado

04.04.2022

Como Citar

Barboza, F. L. de M., Duarte, D. L., & Cunha, M. A. (2022). Antecipando dificuldades financeiras nas organizações. Exacta, 20(2), 470–496. https://doi.org/10.5585/exactaep.2021.17494

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v. 20 n. 2 (2022): (abr./jun.)

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