Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default

Carmen Gallucci, Rosalia Santullli (), Michele Modina and Vincenzo Formisano
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Carmen Gallucci: University of Salerno, IPAG Business School
Rosalia Santullli: University of Genoa
Michele Modina: University of Molise
Vincenzo Formisano: University of Cassino and Southern Lazio

Journal of Management & Governance, 2023, vol. 27, issue 3, No 9, 873-892

Abstract: Abstract The paper aims to jointly combine three different categories of variables (financial ratios, corporate governance data and bank-firm information) to predict SMEs’ default. To this end, a merged longitudinal predictive model was applied to a sample of 973 Italian SMEs that are clients of 36 different co-operative banks. The collected data (for a total of 23 variables included in the model) relate to the years 2012–2014. The main findings reveal the high predictive power of leverage ratio, CEO tenure and ownership concentration, and the number of loans overdue for more than 180 days in the previous 12 months.

Keywords: Default risk; Financial distress; Small and medium-sized enterprises; Bayesian methodology (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10997-021-09614-5

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