Anais
Resumo do trabalho
Finanças · Contabilidade para usuários externos
Título
Determinants of Tax Litigation Risk: A Multinomial Logistic Regression Analysis of Brazilian Listed Firms
Palavras-chave
Tax Litigation
Corporate Governance
Financial Leverage
Autores
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Antonio Lopo MartinezUniversidade de Coimbra
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Arquimedes MoraesUNIVERSIDADE FEDERAL DO ESPÍRITO SANTO (UFES)
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Alfredo Sarlo NetoUNIVERSIDADE FEDERAL DO ESPÍRITO SANTO (UFES)
Resumo
Introdução
Tax litigation represents a significant issue within Brazil’s complex regulatory framework, characterized by frequent legislative changes and uncertainty. Identifying determinants that influence litigation risk is essential.
Problema de Pesquisa e Objetivo
This study aims to analyze the impact of corporate characteristics—size, age, debt level, complexity, governance standards, and sectoral affiliation—on Brazilian firms' tax litigation risk.
Fundamentação Teórica
The research is anchored in corporate governance theory and financial economics literature, highlighting how governance structures, financial characteristics, and industry contexts affect firms' litigation propensity.
Metodologia
Using multinomial logistic regression, this study analyzed 3,290 firm-year observations of Brazilian listed companies (B3, 2009-2023) to assess factors influencing their propensity for tax litigation.
Análise dos Resultados
Results show reduced litigation risk among larger, older firms, while higher leverage, Novo Mercado listing, and operation in historically litigious sectors significantly elevate litigation propensity.
Conclusão
Tax litigation risk in Brazil is shaped by a complex interplay of internal characteristics (size, age, leverage) and external factors (sectoral risk, governance visibility), with implications for risk management.
Contribuição / Impacto
The study provides empirical insights for improving managerial practices and regulatory policies aimed at reducing litigation risk, enhancing compliance, and promoting legal certainty in Brazil.
Referências Bibliográficas
Ali et al. (2020); Ararat et al. (2021); Attia et al. (2024); Burnett et al. (2024); Cunningham et al. (2024); Klassen et al. (2016); Martinez (2017); Martinez et al. (2024); Nguyen & Dao (2022).