A complex network analysis approach to bankruptcy prediction using company relational information-based drivers

Abstract

Corporate bankruptcy prediction is a long-standing topic of interest for a variety of stakeholders. Various prediction methodologies have been proposed to achieve more accurate predictions. So far, most studies have focused on making predictions using artificial intelligence models fed with accounting information only or a combination of accounting information and other category or categories of information. This study proposes new corporate governance drivers that exploit company relational information using board of directors’ networks of companies and the concept of node embeddings obtained by mapping large directors’ networks to lower-dimensional spaces. The contribution of these new drivers to the overall prediction performance is assessed using data on UK companies listed on the London Stock Exchange. The proposed two-stage methodology consists of devising new drivers in the first stage followed by risk class prediction in the second stage. We examine different complex network structures that capture the diversity of connectivity patterns observed in directors’ networks. Empirical results suggest that the proposed network-based drivers that use company relational corporate governance information significantly improve the performance of bankruptcy prediction.