Anticipating corporate’s distresses

Flávio Luiz de Moraes Barboza; Denize Lemos Duarte; Michele Aparecida Cunha

doi:10.5585/exactaep.2021.17494

Authors

Flávio Luiz de Moraes Barboza Federal University of Uberlândia Professor of Finance at School of Business and Management - Federal University of Uberlândia https://orcid.org/0000-0002-3449-5297
Denize Lemos Duarte Federal University of Uberlândia Professor at the Faculty of Accounting Sciences https://orcid.org/0000-0002-5074-3385
Michele Aparecida Cunha Federal University of Uberlândia Master's degree in Business Administration from the Faculty of Business https://orcid.org/0000-0003-1546-0504

DOI:

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

Keywords:

Credit Risk, Financially distressed company, Artificial intelligence, Extreme gradient boosting, Variable importance.

Abstract

The objective of this study is to present a model for predicting Financial Distress (FD) from the perspective of machine learning techniques (TAMs). We applied and compared the XGBoost, Random Forest and Logistic Regression models using financial indicators to seek better forecasts of FDs one year before the event in Latin American companies in the period from 2000 to 2017. Our results showed that TAMs outperform the logit model, reaching an overall accuracy of 96% (XGBoost). In addition, five indicators were relevant to its success. The study expands knowledge and discussions by focusing on the predictive power of comparing models, highlighting the benefits of using algorithms applied to financial research. It helps in risk management, loss prevention, allowing greater balance and health for the financial system, which contributes to the economic, social and sustainable development of a society.

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Author Biographies

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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