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Impacto dos modelos de previsão de falência no setor do turismo em Portugal
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A presente dissertação tem como principal objetivo avaliar a aplicabilidade e o desempenho preditivo dos modelos de Altman (1983) e de Altman e Sabato (2007) na previsão de insolvência de empresas portuguesas do setor do turismo, um segmento determinante para a economia nacional. O estudo recorreu a dados financeiros de empresas dos subsetores de alojamento e restauração, recolhidos na base SABI (Sistema de análise de balanços ibéricos), entre 2019 e 2023, com o intuito de identificar os rácios financeiros que melhor explicam o risco de insolvência e de comparar a eficácia de diferentes abordagens estatísticas.
Dado que a Análise discriminante linear (ADL) revelou violação do pressuposto de homogeneidade das covariâncias, procedeu-se à estimação de um modelo de regressão logística binária, utilizando como variáveis independentes rácios de rentabilidade, alavancagem, cobertura, liquidez e atividade. Os resultados empíricos demonstram que o modelo logístico apresenta maior robustez estatística e poder discriminante, sobretudo entre as pequenas e médias empresas, evidenciando o impacto positivo da rentabilidade e da liquidez, e o efeito adverso da alavancagem sobre a probabilidade de insolvência.
Do ponto de vista económico, as evidências indicam que as empresas com estruturas financeiras equilibradas, maior autonomia de capitais próprios e gestão eficiente da liquidez apresentam menor risco de incumprimento. Para os stakeholders, os resultados desta investigação possuem implicações práticas relevantes: os gestores podem adotar os modelos como instrumentos de diagnóstico e prevenção, as instituições financeiras podem aprimorar a avaliação de risco de crédito, e as entidades públicas dispõem de evidência empírica para políticas de apoio à solvência e resiliência do setor.
Conclui-se que este trabalho reforça a utilidade dos modelos de previsão de falência como ferramentas de gestão financeira estratégica, com contributos relevantes para a estabilidade económica e a sustentabilidade do setor do turismo em Portugal.
This dissertation aims to assess the applicability and predictive performance of Altman’s (1983) and Altman and Sabato’s (2007) models in forecasting insolvency among Portuguese tourism companies — a key sector for national economic growth. The study uses financial data from accommodation and restaurant firms retrieved from the SABI database between 2019 and 2023, with the purpose of identifying the financial ratios that best explain insolvency risk and comparing the predictive accuracy of different statistical approaches. As the Linear Discriminant Analysis revealed a violation of covariance homogeneity, a binary logistic regression model was estimated using profitability, leverage, coverage, liquidity, and activity ratios as independent variables. The empirical findings demonstrate that the logistic model provides stronger statistical robustness and higher discriminative power, particularly among small and medium-sized enterprises, highlighting the positive effects of profitability and liquidity and the negative impact of leverage on insolvency probability. From an economic standpoint, the results show that firms with balanced capital structures, stronger equity positions, and effective liquidity management face significantly lower default risk. For stakeholders, these findings offer practical implications: managers can use the models as early-warning and monitoring tools, financial institutions can improve credit risk assessment, and public entities gain empirical support for designing solvency and resilience policies in the tourism sector. Overall, this study reinforces the usefulness of bankruptcy prediction models as strategic financial management instruments, contributing to economic stability and the sustainable development of Portugal’s tourism industry.
This dissertation aims to assess the applicability and predictive performance of Altman’s (1983) and Altman and Sabato’s (2007) models in forecasting insolvency among Portuguese tourism companies — a key sector for national economic growth. The study uses financial data from accommodation and restaurant firms retrieved from the SABI database between 2019 and 2023, with the purpose of identifying the financial ratios that best explain insolvency risk and comparing the predictive accuracy of different statistical approaches. As the Linear Discriminant Analysis revealed a violation of covariance homogeneity, a binary logistic regression model was estimated using profitability, leverage, coverage, liquidity, and activity ratios as independent variables. The empirical findings demonstrate that the logistic model provides stronger statistical robustness and higher discriminative power, particularly among small and medium-sized enterprises, highlighting the positive effects of profitability and liquidity and the negative impact of leverage on insolvency probability. From an economic standpoint, the results show that firms with balanced capital structures, stronger equity positions, and effective liquidity management face significantly lower default risk. For stakeholders, these findings offer practical implications: managers can use the models as early-warning and monitoring tools, financial institutions can improve credit risk assessment, and public entities gain empirical support for designing solvency and resilience policies in the tourism sector. Overall, this study reinforces the usefulness of bankruptcy prediction models as strategic financial management instruments, contributing to economic stability and the sustainable development of Portugal’s tourism industry.
Descrição
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Z-Score Turismo Modelos de previsão de falência Tourism Bankruptcy Bankruptcy predition models