Evaluating financial distress in Portuguese firms : revisiting Altman's Z-score model

Bibliographic Details
Main Author: Martins, Diogo Reis
Publication Date: 2024
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.14/47845
Summary: This thesis significantly advances the field of financial distress prediction in Portuguese companies by meticulously refining Altman's seminal Z-Score model (1983). With strategic updates to the model's parameters using the latest financial data, this research not only improves predictive accuracy but fundamentally transforms the tool to meet the contemporary needs of Portugal's dynamic economy. By transitioning from multiple discriminant analysis to logistic regression, the study introduces a robust methodological enhancement that substantially increases the model’s predictive precision. Furthermore, the integration of macroeconomic indicators such as GDP has revolutionized its predictive capabilities, proving indispensable in today's interconnected financial landscape. However, the research also unveils limitations; elements such as year dummies, company size, age, and sector-specific factors did not markedly influence the model’s effectiveness, prompting a revaluation of traditional assumptions in distress prediction. This thorough analysis of diverse firm characteristics emphasizes the critical need for financial models that are specifically adapted to the distinct economic features of the Portuguese market. These insights offer invaluable guidance for financial institutions, investors, and policymakers, significantly enhancing the utility and application of distress prediction models across diverse economic environments. Ultimately, the refined model does not just promise better risk management—it guarantees more informed, strategic decision-making for stakeholders within the SME-dominated Portuguese market.
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spelling Evaluating financial distress in Portuguese firms : revisiting Altman's Z-score modelFinancial distressZ-score modelPortuguese companiesLogistic regressionRisk managementMacroeconomic factorsIndustry-specific variablesPredictive accuracySMEs (small and medium enterprises)Financial modellingDificuldades financeirasModelo Z-scoreEmpresas portuguesasRegressão logísticaGestão de riscosFatores macroeconómicosVariáveis específicas do sectorPrecisão preditivaPME (pequenas e médias empresas)Modelagem financeiraThis thesis significantly advances the field of financial distress prediction in Portuguese companies by meticulously refining Altman's seminal Z-Score model (1983). With strategic updates to the model's parameters using the latest financial data, this research not only improves predictive accuracy but fundamentally transforms the tool to meet the contemporary needs of Portugal's dynamic economy. By transitioning from multiple discriminant analysis to logistic regression, the study introduces a robust methodological enhancement that substantially increases the model’s predictive precision. Furthermore, the integration of macroeconomic indicators such as GDP has revolutionized its predictive capabilities, proving indispensable in today's interconnected financial landscape. However, the research also unveils limitations; elements such as year dummies, company size, age, and sector-specific factors did not markedly influence the model’s effectiveness, prompting a revaluation of traditional assumptions in distress prediction. This thorough analysis of diverse firm characteristics emphasizes the critical need for financial models that are specifically adapted to the distinct economic features of the Portuguese market. These insights offer invaluable guidance for financial institutions, investors, and policymakers, significantly enhancing the utility and application of distress prediction models across diverse economic environments. Ultimately, the refined model does not just promise better risk management—it guarantees more informed, strategic decision-making for stakeholders within the SME-dominated Portuguese market.Reis, RicardoVeritatiMartins, Diogo Reis2025-01-21T12:31:01Z2024-10-152024-09-122024-10-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.14/47845urn:tid:203730127enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-05-13T01:36:59Zoai:repositorio.ucp.pt:10400.14/47845Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T01:55:52.223422Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Evaluating financial distress in Portuguese firms : revisiting Altman's Z-score model
title Evaluating financial distress in Portuguese firms : revisiting Altman's Z-score model
spellingShingle Evaluating financial distress in Portuguese firms : revisiting Altman's Z-score model
Martins, Diogo Reis
Financial distress
Z-score model
Portuguese companies
Logistic regression
Risk management
Macroeconomic factors
Industry-specific variables
Predictive accuracy
SMEs (small and medium enterprises)
Financial modelling
Dificuldades financeiras
Modelo Z-score
Empresas portuguesas
Regressão logística
Gestão de riscos
Fatores macroeconómicos
Variáveis específicas do sector
Precisão preditiva
PME (pequenas e médias empresas)
Modelagem financeira
title_short Evaluating financial distress in Portuguese firms : revisiting Altman's Z-score model
title_full Evaluating financial distress in Portuguese firms : revisiting Altman's Z-score model
title_fullStr Evaluating financial distress in Portuguese firms : revisiting Altman's Z-score model
title_full_unstemmed Evaluating financial distress in Portuguese firms : revisiting Altman's Z-score model
title_sort Evaluating financial distress in Portuguese firms : revisiting Altman's Z-score model
author Martins, Diogo Reis
author_facet Martins, Diogo Reis
author_role author
dc.contributor.none.fl_str_mv Reis, Ricardo
Veritati
dc.contributor.author.fl_str_mv Martins, Diogo Reis
dc.subject.por.fl_str_mv Financial distress
Z-score model
Portuguese companies
Logistic regression
Risk management
Macroeconomic factors
Industry-specific variables
Predictive accuracy
SMEs (small and medium enterprises)
Financial modelling
Dificuldades financeiras
Modelo Z-score
Empresas portuguesas
Regressão logística
Gestão de riscos
Fatores macroeconómicos
Variáveis específicas do sector
Precisão preditiva
PME (pequenas e médias empresas)
Modelagem financeira
topic Financial distress
Z-score model
Portuguese companies
Logistic regression
Risk management
Macroeconomic factors
Industry-specific variables
Predictive accuracy
SMEs (small and medium enterprises)
Financial modelling
Dificuldades financeiras
Modelo Z-score
Empresas portuguesas
Regressão logística
Gestão de riscos
Fatores macroeconómicos
Variáveis específicas do sector
Precisão preditiva
PME (pequenas e médias empresas)
Modelagem financeira
description This thesis significantly advances the field of financial distress prediction in Portuguese companies by meticulously refining Altman's seminal Z-Score model (1983). With strategic updates to the model's parameters using the latest financial data, this research not only improves predictive accuracy but fundamentally transforms the tool to meet the contemporary needs of Portugal's dynamic economy. By transitioning from multiple discriminant analysis to logistic regression, the study introduces a robust methodological enhancement that substantially increases the model’s predictive precision. Furthermore, the integration of macroeconomic indicators such as GDP has revolutionized its predictive capabilities, proving indispensable in today's interconnected financial landscape. However, the research also unveils limitations; elements such as year dummies, company size, age, and sector-specific factors did not markedly influence the model’s effectiveness, prompting a revaluation of traditional assumptions in distress prediction. This thorough analysis of diverse firm characteristics emphasizes the critical need for financial models that are specifically adapted to the distinct economic features of the Portuguese market. These insights offer invaluable guidance for financial institutions, investors, and policymakers, significantly enhancing the utility and application of distress prediction models across diverse economic environments. Ultimately, the refined model does not just promise better risk management—it guarantees more informed, strategic decision-making for stakeholders within the SME-dominated Portuguese market.
publishDate 2024
dc.date.none.fl_str_mv 2024-10-15
2024-09-12
2024-10-15T00:00:00Z
2025-01-21T12:31:01Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.14/47845
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instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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