This paper contributes to the growing body of research on private firms, particularly private firm accounting. We explore the economic factors that drive improvements in the default prediction of unlisted private firms using peers’ market-based information. Specifically, we examine how the market-based default probability of a peer firm can provide valuable insights into the often noisy accounting data of private firms. Our analysis delves deeply into these economic issues to uncover essential insights. To address our research question, we utilize a granular proprietary dataset of 10,136 Italian micro-, small-, and mid-sized enterprises (MSMEs) that are required to disclose their financial statements publicly. We propose a novel public–private firm mapping approach to investigate whether incorporating peers’ market-based information improves the accuracy of default predictions for private unlisted firms. Our mapping approach matches the market information of listed firms with private firms through a data-driven clustering technique using Neural Network Autoencoder. This method enables us to link the Merton Probability of Default (PD) of public peers to the corresponding private firms within the same cluster. We then apply five statistical techniques – linear models, multivariate adaptive regression splines, support vector machines , k-nearest neighbours and random forests – to predict corporate default among private firms, comparing model performance with and without the inclusion of Merton’s PD estimated using peers’ market-based information. To assess the contribution of each predictor, we employ Shapley values . Our results demonstrate a significant improvement in default prediction for unlisted private firms when incorporating peers’ market-based information, confirming that the noisy accounting data of private firms alone hinders accurate default prediction. Furthermore, our findings highlight the importance for banks to broaden the scope of information used in credit risk assessments of private firms. These results have important policy implications for financial institutions and policymakers, providing a tool to mitigate the challenges posed by the noisy information disclosure of MSMEs while ensuring more accurate credit risk assessments.

Growing financial failure at firm-level can have serious consequences for banks in terms of rising non-performing assets, in the absence of a strong bankruptcy system. Such a scenario in India made its dysfunctional insolvency system to be reformed, introducing the new Insolvency and Bankruptcy Code (IBC) in 2016. Using a panel of 33,845 Indian firms over the period of 2008–2019 and by employing a difference-in-differences approach, we investigate how the IBC has supported financially distressed firms in mitigating their intrinsic vulnerability during the post-IBC period, compared to their non-distressed counterparts. We find that through expanded credit availability and lower cost of debt financing during the post-IBC period, distressed firms are able to improve their performance relative to non-distressed firms. Furthermore, we provide evidence that the benefits stemming from the implementation of the IBC policy are more prominent for those financially distressed firms that are larger, younger and more collateralized. Our results are robust to a battery of tests and identification strategies. Our conclusions are relevant in contributing to the current academic and policy debates on safeguarding and preserving business performance and continuity under stressed scenarios.