Altman Z’’-score for predicting bankruptcy : Evidence from the aviation industry

Abstract

Bankruptcy prediction has long been a subject of academic interest, as it provides valuable insights for corporate stakeholders, such as investors and creditors. The aviation industry is particularly exposed to external economic shocks and events, including pandemics, intense international competition and increasing jet fuel prices. As a result, there is a strong demand for reliable tools that enable the early identification of financial distress in this sector. The objective of this thesis is to determine the predictive performance of the Altman Z’’-score model in the scope of the global aviation industry. The study aims to assess whether the model can be used as a reliable tool for identifying financially distressed airlines as well as to investigate whether there are any differences in the model’s predictive accuracy across different geo-graphical regions and market types. To achieve these objectives, an empirical analysis is con-ducted based on a data sample of 20 bankrupt and 30 non-bankrupt airlines. In total, a sam-ple of 100 observations from 50 unique airlines is obtained from the Orbis database and annual financial reports for the period from 2005 to 2025. Based on the classification metrics, including overall accuracy, classified accuracy, precision, specificity, sensitivity and F1 score, the study results indicate a moderate overall predictive accuracy rate, with slightly improved performance when excluding grey zone observations from the classified observations. Based on the high sensitivity rate, it can be concluded that the model displays a strong performance in identifying bankrupt airlines. However, a low specificity rate suggests that the model tends to overestimate financial distress by classifying non-bankrupt airlines as financially distressed. In addition, the results of the chi-square test reveal that while the model’s performance does not vary significantly between developed and emerging markets, there are statistically significant differences in performance across geographical regions, with considerably lower accuracy observed in the APAC region. Overall, the findings of this study suggest the Altman Z’’-score model provides useful insights and can serve as a tool for identifying early warning signals of financial distress, rather than as a precise classifier of bankruptcy in the context of the global aviation industry.