Evaluating financial distress in Portuguese firms : revisiting Altman's Z-score model
Main Author: | |
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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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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 |
format |
masterThesis |
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publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.14/47845 urn:tid:203730127 |
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http://hdl.handle.net/10400.14/47845 |
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urn:tid:203730127 |
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eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
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