Does ESG Predict Business Failure in Brazil? An Application of Machine Learning Techniques

Mehwish Kaleem, Hassan Raza (), Sumaira Ashraf, António Martins Almeida () and Luiz Pinto Machado
Additional contact information
Mehwish Kaleem: Faculty of Business & Management, Universiti Sultan Zainal Abidin, Kampung Gong Badak 21300, Terengganu, Malaysia
Hassan Raza: Department of Management Sciences, Shaheed Zulfikar Ali Bhutto Institute of Science & Technology University, Islamabad 44000, Pakistan
Sumaira Ashraf: ADVANCE/CSG Research Center, ISEG—Institute of Economics and Management, University of Lisbon, 1649-004 Lisbon, Portugal
António Martins Almeida: CEEAplA (Centre of Applied Economic Studies of the Atlantic), University of Madeira, 9000-072 Funchal, Portugal
Luiz Pinto Machado: CITUR (Centre for Tourism Research, Development and Innovation), University of Madeira, 9000-072 Funchal, Portugal

Risks, 2024, vol. 12, issue 12, 1-22

Abstract: The aim of this study is to explore the influence of environmental, social, and governance (ESG) factors on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted principal component analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of seven variables that predict business failure. The results indicate that ESG factors significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making.

Keywords: environmental, social, and governance (ESG); business failure in Brazil; principal component analysis (PCA); machine learning techniques; confusion matrix; precision score (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-9091/12/12/185/pdf (application/pdf)
https://www.mdpi.com/2227-9091/12/12/185/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:12:y:2024:i:12:p:185-:d:1528902

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().