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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, that is, 'codified' and 'uncodified' discretion. Finally, 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. [ABSTRACT FROM AUTHOR]

A large literature has developed on the distinction between hard and soft information with much of this literature focused on displacement of soft information with hard information. We investigate whether the propensity of loan officers at local branches to incorporate soft information in the credit scoring process is affected by the geographical distance between the branch and the bank's headquarters. We find that hardening soft information is significantly sensitive to branch-to-headquarters distance. We also find that organizational distance affects time for loan approval, increasing approval time for applicants receiving a good credit score (i.e. low probability of default) originated at distant branches and reducing approval time for applicants with poor credit scores (i.e. high probability of default). Finally, we find that on average organizational distance has no direct impact on the likelihood of the occurrence of negative credit events. However, the final rating being equal, the hardening of soft information has an influence on loan performance that varies with organizational distance. Overall, our findings are consistent with the presence of spatially based communication frictions within banking organizations. [ABSTRACT FROM AUTHOR]