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1019. New drivers of bankruptcy based on Complex Networks
Invited abstract in session MD-31: Network Analytics, stream Analytics.
Monday, 14:30-16:00Room: 046 (building: 208)
Authors (first author is the speaker)
| 1. | Jinxian Zhao
|
| Business School, University of Edinburgh |
Abstract
This study proposes a novel approach to devise new drivers for bankruptcy prediction using complex network analysis. These drivers are company relational information-based drivers (CRIs) derived from the board of directors’ networks with different network configurations. The effectiveness of these new drivers is demonstrated on a dataset of UK companies listed on the London Stock Exchange. Numerical results suggest a significant improvement in predicting corporate bankruptcy.
Our research establishes the impact of incorporating network analysis of company relationships into bankruptcy prediction models. It sets the stage for more sophisticated financial analysis techniques that synergize traditional financial metrics with cutting-edge network analysis, and the advancement holds substantial promise for financial institutions and analysts, providing a more nuanced understanding of corporate bankruptcy risks.
Keywords
- Machine Learning
- Graphs and Networks
- Artificial Intelligence
Status: accepted
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