Financial distress prediction
: the application of machine learning-based algorithms on portuguese small and medium enterprises

  • Bernardo Pedrosa Afonso Sarrasqueiro (Student)

Student thesis: Master's Thesis


Financial distress prediction is an issue of great importance to several financial institutions and companies’ stakeholders. Detecting the early signs of it allows for corrective measures, reducing bankruptcies. This study examines the predictive power of 6 models, establishing a comparison between machine learning (ML) based models and others like Logistic Regression and Linear Discriminant Analysis. There are two main issues that this study strives to confront. The first one is to test if, in the context of Portuguese Small and Medium Enterprises, ML models' use in determent of others proves to be true. The second is to add to recent literature in testing novel statistical approaches to the classification problem by comparing different ensemble techniques. The results show that the Stacking Classifier outperforms both non-ML models but also other ensemble techniques. The results prove that ML models outperform non-ML methods and that Stacking is the best ensemble technique of the three presented in this study. For Portuguese stakeholders and financial institutions, the models presented in this study serve as proof of concept that it’s possible to reliably predict financial distress by implementing these techniques.
Date of Award28 Apr 2021
Original languageEnglish
Awarding Institution
  • Universidade Católica Portuguesa
SupervisorRicardo Ferreira Reis (Supervisor)


  • Financial distress prediction
  • Ensemble methods
  • Bagging
  • Boosting
  • Stacking


  • Mestrado em Finanças

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