Can market information outperform hard and soft information in predicting corporate defaults?

Recent evidence has shown that hybrid models for credit ratings are important when assessing the risk of firms. Within this stream of literature, we aim to provide novel evidence on how hard (quantitative), soft (qualitative) and market information predict corporate defaults for unlisted firms by implementing the Cox proportional hazard model. We address this research question by exploiting a unique proprietary dataset comprising of detailed information on internal credit ratings of European unlisted mid-sized firms and compute their Merton’s distance-to-default (DD) measure of credit risk with market data collected on comparable publicly listed companies. Our results show that the bank’s use of hard, soft, and market information when assessing the credit ratings of borrowers has a significant influence on the prediction of their defaults. Further, we investigate the significant influence of soft information in predicting corporate defaults by drawing on two separate processes through which loan officers can inject soft information in credit scoring, i.e., “codified” and “uncodified” discretion. Lastly, when we distinguish between the loan officer’s discretion to upgrade or downgrade an applicant’s credit score, we find that it is the upgrade that is likely to predict a lower probability of a firm defaulting. This study contributes to the policy debate on safeguarding the banking sector’s continuity by positing that integrating market information into banks’ hybrid methods of credit rating helps to improve the accuracy in predicting unlisted firms’ credit risk that is useful to policy makers for the design of future forward-looking financial risk management frameworks.