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1 Credit performance of the UK SMEs Through the Crisis Jake Ansell Credit Research Centre, The University of Edinburgh Business School

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Presentation on theme: "1 Credit performance of the UK SMEs Through the Crisis Jake Ansell Credit Research Centre, The University of Edinburgh Business School"— Presentation transcript:

1 1 Credit performance of the UK SMEs Through the Crisis Jake Ansell Credit Research Centre, The University of Edinburgh Business School Joint work with Dr Galina Andreeva, Paul Orton, Dr Ma Yigui and Ma Meng

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

3 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 4

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) 5

6 6 Credit risk approaches Lending to individuals -R-Relatively small amounts of money lent to a large number of customers -f-focus more on prediction, less on causality -M-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 &

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 8

9 Impairment Rate in UK (%) 9

10 Impairment Rate by Region 10

11 Impairment by SIC code 11

12 Impairment by Age 12

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 13

14 Cox and Snell/Nagelkerke 2007All Start-Up Non SU All Start-Up Non SU All Start-Up Non SU All Start-Up Non SU

15 AUROC Results 15 In Sample CI 2007Difference All Start- Up Non SU All Start- Up Non SU

16 AUROC Results 16 In Sample CI 2007Difference All Start-Up Non SU All Start-Up Non SU

17 2Comments Whilst R 2 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 17

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(P g /P b ) =  i +b i x ii +  i +  ii 18

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 19

20 Impairment in Panel Sample 20

21 Non-Start-Ups: SIC Code 21

22 Non-Start-Up by Region 22

23 Variable Start-Up Model Legal Form 8. Total Value Of Judgements In The Last 12 Months 2. Company is Subsidiary9. Number Of Previous Searches (last 12m) SIC Code10. Time since last derogatory data item (months) 4. Region11. Lateness Of Accounts 5. Proportion Of Current Directors To Previous Directors In The Last Year 12. Time Since Last Annual Return 6. Oldest Age Of Current Directors/Proprietors supplied (Years) 13. Total Assets 7. Number Of Directors Holding Shares

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

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

26 Non-Start-up Variables Legal Form9. Number Of Previous Searches (last 12m) 2. Parent Company – derog details 10. Time since last derogatory data item (months) SIC Code11. Lateness Of Accounts 4. Region12. Time Since Last Annual Return 5. No. Of ‘Current’ Directors 13. Total Fixed Assets As A Percentage Of Total Assets 6. Proportion Of Current Directors To Previous Directors In The Last Year 14. Debt Gearing (%) 7. PP Worst (Company DBT - Industry DBT) In The Last 12 Months 15. Percentage Change In Shareholders Funds 8. Total Value Of Judgements In The Last 12 Months 16. Percentage Change In Total Assets

27 Non-Start-up Results 27 Variable list order

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

29 Dummy Effects 29

30 Panel with Macro-economic Variable Currently Exploring of Macro-economic Variables: 1.UNEMPLOYMENT RATE 2.INFLATION ANNUAL CHANGE 3.CPI 4.CPI ANNUAL CHANGE 5.FTSE ALL SHARE INDEX CHANGE 6.FTSE100 ANNUAL INDEX CHANGE 7.FTSE 100 ANNUAL RETURN 30

31 Annual Macro variables 31

32 Averaged Annual Macro Variables 32

33 33 Correlations gdp3FAIuneinflF100cpir gdp3 1 FAI une infl F cpir

34 Start-Up Models GDP Growth GDP Growth Lag 1 GDP Growth Average last 3 Years GDP Growth GDP Growth Lag 1 GDP Growth Average last 3 Years RPIRPI Lag 1 RPI Average Last 3 Years FTSE 100 FTSE 100 Lag1 FTSE Average Last 3 Years

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

36 Non-Start-Up Models GDP Growth Average Last 3 Years GDP Growth Lag 1 GDP Growth Lag 1 GDP Growth RPI Average Last 3 Years FTSE 100 Lag 1 CPI FTSE 100 Average Last 3 Years RPI Lag 1 FTSE 100

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

38 Start-Up Performance 38 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 39 models in listed order

40 Non-Start-Up Model 40 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 41 models in listed order

42 Out-of-Sample Performance Model NonSt 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

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

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 44


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