Purpose: This paper aims to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between different authors (co-authorship), and to identify the primary models and methods that are used and studied by authors of this area in the past five decades.Design/methodology/approach: A systematic literature review (SLR) has been conducted, using the Scopus database for identifying core international academic papers related to the established research topic from the year 1968 to 2017.Findings: It has been verified, firstly, that bankruptcy prediction in the corporate world is a field of growing interest, as the number of papers has increased significantly, especially after 2008 global financial crisis, demonstrating the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as the researchers with a lot of influence were basically not working together during the last five decades. Thirdly, it has been identified that the two most frequently used and studied models in bankruptcy prediction area are Logistic Regression (Logit) and Neural Network. However, there are many other innovative methods as machine learning models applied in this field lately due to the emerging technology of computer science and artificial intelligence.Originality/value: We applied the SLR approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this contributes as the link among different elements of the concept studied, and it demonstrates the growing interest in this area.

Bibliometric analysis is an effective method to carry out quantitative study of academic output to address the research trends on a given area of investigation through analysing existing documents. This paper aims to explore the application of intelligent techniques in bankruptcy predictions so as to assess its progress and describe the research trend through bibliometric analysis over the last five decades. The results indicate that, although there is a significant increase in publication number since the 2008 financial crisis, the collaboration among authors is weak, especially at the international dimension. Also, the findings provide a comprehensive view of interdisciplinary research on bankruptcy modelling in finance, business management and computer science fields.The authors sought to contribute to the theoretical development of bankruptcy prediction modeling by bringing new knowledge and key insights. Artificial intelligent techniques are now serving as important alternatives to statistical methods and demonstrate very promising results. This paper has both theoretical and practical implications. First, it provides insights for scholars into the theoretical evolution and intellectual structure for conducting future research in this field. Second, it sheds light on identifying under-explored machine learning techniques applied in bankruptcy prediction which can be crucial in management and decision-making for corporate firm managers and policy makers.

Due to the COVID-induced global collapse in demand for air travel, the year 2020 was a catastrophic one for the aviation industry. A dramatic drop in operating revenues along with continuing fixed expenses drained the cash reserves of airlines, with consequent risks of financial distress and, potentially, even of bankruptcy. Flag-carriers are a special group in the airline business—they are considered to have privileges in terms of the support given by governments while, on the other hand, are often viewed as having low efficiency and performance. This study aims to estimate for European airlines the interaction effect of being a flag-carrier (flagship) with the relationship between leverage, liquidity, profitability, and the degree of financial distress. Findings obtained from analysing 99 European airlines over a period of ten years, indicate that the negative influence of leverage on financial stability is higher in the case of flag carriers (flagship). The impact of liquidity and profitability on financial health is more positive for flagship than for non-flagship carriers. These findings are not limited to contributing to the existing literature, but also have significant practical implications for executives, managers, and policy makers in the European air transport sector.

The tremendous impact of the coronavirus pandemic on the global aviation industry has led to many cases of airline financial distress and bankruptcy. The Asia–Pacific region (APAC) contains more than half of the world’s population, and its airlines had the highest profit margin of any region. In this study, we investigate whether corporate sustainability practice can reduce the financial distress risk of air carriers, and, if so, what would the effect be in APAC? We first examine the relationship between environmental, social, and governance disclosure and the likelihood of financial distress of airlines as measured by the Altman Z″-score. Second, we analyze the moderating role of being an APAC airline in this relationship. The findings support the claim that implementing environmental actions may increase financial distress risk, and by improving social and governance activities, airlines can mitigate the risk of financial distress. The negative influence of the environmental pillar and the positive influence of the social pillar can be smaller for APAC airlines. Our study provides empirical evidence of the influence of environmental, social, and governance (ESG) on the likelihood of financial distress in the airline industry. Moreover, we analyze the moderating role of being an APAC airline in the relationship between sustainability and financial distress. This study has significant implications for executives, managers, and policymakers in the aviation industry on ESG strategy decisions and the general issue of sustainability.