A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications

This chapter aims to help future researchers and practitioners explore different statistical methods (discriminant analysis, logistic regression, probit analysis) and modern analytical methods (support vector machines, artificial intelligence, neural networks) to provide improved predictions. It is also intended to facilitate their research by providing a clear overview of their interests and finding relevant information for further research.Over the past decades, the topic of bankruptcy prediction methods has developed significantly, becoming a relevant research area in many disciplines, including business economics, computer science, operations research, finance and accounting. Motivated by the severe impact that the 2007–2009 financial crisis and the recent COVID-19 global health crisis have had on companies of all sizes, and subsequently the need to develop new methodologies for predicting corporate failures, this chapter provides a systematic literature review, based on bibliometric analysis of 993 reviewed articles, and an in-depth review of 103 articles published on bankruptcy prediction methods over the period 1997–2019.