Predicting SMEs’ default risk: Evidence from bank-firm relationship data

Michele Modina, Filomena Pietrovito, Carmen Gallucci and Vincenzo Formisano

The Quarterly Review of Economics and Finance, 2023, vol. 89, issue C, 254-268

Abstract: This paper uses a probit model on a unique dataset of 13,081 Italian firms and 111 co-operative banks involved in the lending process to provide empirical evidence suggesting that the use and violations of credit lines and long-term loan overruns predict one-year and two-year probability of default (PD). The analysis controls for balance sheet indicators and time varying bank characteristics, captured by bank-time fixed effects. When combined with accounting data, credit-related indicators obtained from private internal banking sources improve small and medium-sized enterprises’ (SMEs) default prediction. The marginal benefit of the bank-firm specific information is also assessed by comparing the default prediction accuracy of a model that incorporates accounting information with that of a full model including private information. In terms of heterogeneity, the association between the balance sheet indicators and data on bank-firm relationships and default probability can vary across sectors and geographies. This highlights the importance for banks of specific analysis to better assess risk at the firm level.

Keywords: Bank-firm relationship; Credit risk; Default prediction; Lending process; SMEs (search for similar items in EconPapers)
JEL-codes: G20 G21 G33 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:quaeco:v:89:y:2023:i:c:p:254-268

DOI: 10.1016/j.qref.2023.04.008

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