Credit performance of the UK SMEs Through the Crisis

1. Credit performance of the UKSMEs Through the Crisis Jake Ansell Credit Research Centre, The University of Edinburgh Business School J.Ansell@ed.ac.uk Joint work with Dr Galina Andreeva, Paul Orton, Dr Ma Yigui and Ma Meng

2. Outline • Background • Data • Cross-sectional Analysis • Panel Data with Dummies • Panel Data with Macroeconomic Variables • Future plans? • Conclusion

3. SMEs - Cornerstone of the Economy Globally 95% Businesses are SMEs, 50% of economic value, 55% of all innovations EU 99% Businesses are SMEs, 68% of total employment, 63% of overall business turnover UK 99% Businesses are SMEs, 59% of total employment, 50% GDP Similar picture for Asian economies

4. Lending in UK • Concern over lending to SMEs in UK (£991m in 2008, £566m in 2010) • Prudent lending requires more stringent criterion • SMEs more conservative in recessionary periods • Anecdotal information that some SMEs feel credit constraints

5. Credit Scoring and SMEs • Business Managers assessing clients – picking winners (Very old model) • Business Relationship Management – plausible for high value clients less for SMEs • But need fast efficient methods credit decisions for many small businesses – Credit Scoring • More recently ‘Management Capability’ – Ma Yigui, Andreeva and Ansell (2011)

6. Credit risk approaches Lending to individuals Relatively small amounts of money lent to a large number of customers focus more on prediction, less on causality Management Science and Data Mining Lending to businesses Large amounts of money lent to a relatively small number of businesses focus more on causality, less on prediction Finance and Accounting

7. Data • There are about 5 million SMEs in UK • Not all SMEs borrow from banks • Database from a Credit Agency • Over 2 million enterprises • Recorded each April: 2007, 2008, 2009 & 2010

8. Data • Financial Impairment: Good/Bad • General Information: legal form, region, SIC, # Employees, Age of Company • Directors’ Information: # Directors, Ownership, Changes etc • Previous Credit history: DBT, judgements etc • Accounting Information: Common financial variables and financial ratios

9. Impairment Rate in UK (%)

10. Impairment Rate by Region

11. Impairment by SIC code

12. Impairment by Age

13. Initial Analysis • Cross-Sectional Analysis • Logistic Model Predicting Default • Model Used Weights of Evidence • Stepwise Regression using % change in Cox & Snell (Nagelkerke) • Interest in Performance and Variable Inclusion

14. Cox and Snell/Nagelkerke

15. AUROC Results

16. AUROC Results

17. 2Comments • Whilst R2 are low the predictive quality is high in sample and out sample • No out of time results • Modelling was naïve • There is some stability over variables or type of variables • There is stability over time – could be due to nature of variables employed

18. Panel Analysis • Obviously can trace behaviour of individual enterprises over time • But only have 4 observation points • Modelling default – No loss measurment • Good = 0, Bad = 1 • Logit Panel Data Model: Log(Pg/Pb) = ai+bixii+di+sii

19. Panel Analysis • Produce Cross-Section Models each Year • Using Panel Sample Tracking Enterprises • Panel Analysis and Panel Analysis with Dummy for Years • Coefficients of Model, Performance, Absolute Mean Square Error

20. Impairment in Panel Sample

21. Non-Start-Ups: SIC Code

22. Non-Start-Up by Region

23. Variable Start-Up Model

24. Start-Up Models’ Coefficient Variable in list order

25. Start-Up Models’ Coefficient Variable in list order

26. Non-Start-up Variables

27. Non-Start-up Results Variable list order

28. Non-Start-up Results Incept + variable in listed order

29. Dummy Effects

30. Panel with Macro-economic Variable Currently Exploring of Macro-economic Variables: • UNEMPLOYMENT RATE • INFLATION ANNUAL CHANGE • CPI • CPI ANNUAL CHANGE • FTSE ALL SHARE INDEX CHANGE • FTSE100 ANNUAL INDEX CHANGE • FTSE 100 ANNUAL RETURN

31. Annual Macro variables

32. Averaged Annual Macro Variables

33. Correlations

34. Start-Up Models

35. Start-up Models Incept + variable in listed order

36. Non-Start-Up Models

37. Non Macro-Economic Variables Incept + variable in listed order

38. Start-Up Performance logistic regression panel model panel model with year dummy panel model with selected no lagged MV (highest AIC in each category) panel model with selected one year lagged MV (highest AIC in each category) panel model with selected averaged MV (highest AIC in each category) panel model with no lagged GDP_growth rate panel model with one year lagged GDP_growth rate panel model with averaged GDP_growth rate

39. AUROC Within Sample models in listed order

40. Non-Start-Up Model logistic regression panel model panel model with year dummy panel model with selected no lagged MV (highest AIC in each category) panel model with selected one year lagged MV (highest AIC in each category) panel model with selected averaged MV (highest AIC in each category) panel model with no lagged GDP_growth rate panel model with one year lagged GDP_growth rate panel model with averaged GDP_growth rate

41. AUROC In Sample models in listed order

42. Out-of-Sample Performance 2010

43. Future? • Continue to explore macro-economic variables • Model based on normal • Non-parametric models • Larger range of data • Out-of-Time Sample

44. Conclusion • There is considerable stability across models - Estimates - Performance Variables • Some variables need reconsideration • GDP seems an important Macro-economic variables • BUT need further exploration