A1028
Title: Enhancing bankruptcy prediction: A two-layered network approach using latent space models
Authors: Tianhai Zu - University of Texas at San Antonio (United States) [presenting]
Abstract: A novel statistical approach is presented to corporate bankruptcy prediction by leveraging complex network analysis. A two-layered network structure is introduced 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, a flexible multi-layered latent position model is developed that efficiently extracts key features from the network. The 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, it is demonstrated that incorporating these network-derived features significantly enhances the predictive power of bankruptcy models. The 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. The aim is to offer a robust statistical framework for integrating complex relational data into predictive modeling for bankruptcy risk assessment.
