Enhancing corporate bankruptcy and financial distress prediction: a multi-method approach with profiling and network analysis

Abstract

This thesis encompasses three interrelated studies on the prediction of corporate bankruptcy and financial distress, a critical area for various stakeholders including businesses, financial institutions, investors, regulatory bodies, auditors, and academics.

The first study presents a comprehensive survey, classification, and critical analysis of the literature on corporate bankruptcy and financial distress prediction. It covers definitions, prediction methodologies, data pre-processing, feature selection, model implementation, performance criteria, and evaluation methodologies, providing a critical analysis to inspire future research directions.

The second study introduces a novel approach to bankruptcy prediction by leveraging company relational information through complex network analysis. Using board of directors’ networks, node embeddings and communities, the study devises new corporate governance drivers to enhance prediction accuracy. Empirical results from UK companies listed on the London Stock Exchange demonstrate that these network-based drivers significantly improve prediction performance.

The third study proposes a two-step methodology for bankruptcy prediction utilising companies’ financial profiles. Initially, unsupervised cluster analysis generates group financial profiles from accounting information. Subsequently, supervised artificial intelligence models predict bankruptcy based on these profiles. This approach, tested on data from UK companies listed on the London Stock Exchange, shows improved prediction performance and enhanced model interpretability.

Together, these studies contribute to the advancement of bankruptcy prediction methodologies by integrating comprehensive literature analysis, innovative network-based drivers, and a two-step financial profile-based prediction approach.