Enhancing Bankruptcy Prediction: A Two-Layered Network Approach Using Latent Space Models

Music City Center 

In this study, we present a novel statistical approach to corporate bankruptcy prediction by leveraging complex network analysis. We introduce a two-layered network structure that captures both supply chain relationships and investment-co-investment patterns among companies, providing a more comprehensive view of corporate interdependencies than traditional methods. To analyze this complex structure, we develop a flexible multi-layered latent position model that efficiently extracts key features from the network. Our methodology employs advanced statistical techniques to estimate latent positions underlying this two-layered network, which are then utilized as predictors in a bankruptcy prediction model. Using the US public company data, we demonstrate that incorporating these network-derived features significantly enhances the predictive power of bankruptcy models. Our results reveal that these latent positions estimated from network structure capture crucial relational information that is highly relevant to a company's financial stability. This approach not only outperforms traditional prediction methods but also provides interpretable insights into the role of corporate interconnectedness in financial risk. Our work aims to offer a robust statistical framework for integrating complex relational data into predictive modeling for bankruptcy risk assessment.

Network Analysis

Corporate Bankruptcy