This study is about bankruptcy prediction modeling and explores the benefits from its application. Bankruptcies affect all stakeholders: from employees to regulators, investors or managers. Therefore, it is very interesting to understand the phenomenon that leads to bankrupt in order to take advantage of it. The study begins with an exhaustive literature review with the purpose of understanding well the topic of bankruptcy prediction. Most of the models and techniques of bankruptcy prediction modeling up to this date are covered here. The main research questions that define this study are: (i) How to predict bankruptcies on a specific industry? (ii) How to attribute probabilities of bankruptcy and classes of risk to these predictions? (iii) How to determine the contributing variables to a predicted bankrupt and to benefit from it? Linear discriminant analysis (LDA) method is used to answer these questions. Empirical evidence supports the developed model and study. The rate of good classification is equal to 86.36% of the holdout sample. Type I and II errors are in equivalent proportions after being rebalanced with a cut-off modification achieved by nonlinear programming optimization. Various testing of the model robustness are performed, such as logistic regression, which confirms the significance of the most of the explanatory variables. In order to refine the classification output of the model (either bankrupt or non-bankrupt firms), five classes of risks are developed – from the most to the least risky. In addition, probabilities of default and confidence intervals of the results are presented. Finally, a deeper examination of the results’ outputs is conducted and contributions from the different ratios that influence the model are analyzed.
|Educations||MSc in Applied Economics and Finance, (Graduate Programme) Final Thesis|
|Number of pages||125|