Determinants of SME loan default: the importance of borrow-level heterogeneity

Original by F. McCann & T. McIndoe-Calder, 2012, 32 pagesHamster_gagarin_linkedin
hamster writter This summary note was posted on 10 January 2017, by in Credit risk Finance #, #
  • Typical financial ratios are good predictors: loan to assets, current ratio, leverage ratio, liquidity ratio and profitability ratio. Significant sector effects remain. Length of time to borrowing firm’s owner has been with the firm mitigates likelihood of default.
  • Data: 6745 loans from unique borrowers
  • Numerous heterogeneous effects: sector of activity, quintiles of firm size, exposure and credit quality: Financial ratios are irrelevant in predicting defaults in small loan. For larger firms no borrower level information significantly predicts default
  • Attention must be paid to financial health of small firms and borrowers with large exposures
  • Soft information has been shown to be important (Lehmann 2003, Berger and Frame 2007)
  • Fidrmuc and Hainz 2010, use data of 700 SMEs in Slovakia using a probit model and finds that indebtedness, liquidity, profitability and sector are all significant determinants
  • Behr, Gütter and Plattner 2004, find similar effect on German SMEs
  • Dyrberg-Rommer 2005 test SMEs from Spain, France and Italy using rating agency data and find that earnings and solvency ratios are consistent determinants of default across countries but that other variables effect vary by countries
  • Most effect from sectors: Hotels and restaurants, Public/local/health, Real estate and Construction
  • Use backwards stepwise probit regression i.e. general to specific procedure
  • The sectors that exhibit propensity to default, unexplained by borrower characteristics are the Manufacturing, Real Estate and Construction sectors
  • Looks at sensitivity, Specificity type I and Type II errors for model performance
  • Examine quintiles of turnover, exposure, loan size
  • Decreasing risk as firs become larger
  • Soft information might be crucial for smallest and larger banks
  • Hence the importance of moving from average effects when analysing drivers of default
  • In economic downturn detailed granular approach by sector is recommended
  • Difference warning signals can be identified for smaller firms and borrowers with small vs large loans