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Credit performance of the UK SMEs Through the Crisis

Jake AnsellCredit Research Centre,

The University of Edinburgh Business SchoolJ.Ansell@ed.ac.uk

Joint work with Dr Galina Andreeva, Paul Orton,Dr Ma Yigui and Ma Meng

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Outline

• Background

• Data

• Cross-sectional Analysis

• Panel Data with Dummies

• Panel Data with Macroeconomic Variables

• Future plans?

• Conclusion

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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

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

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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)

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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

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

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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

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Impairment Rate in UK (%)

9

2006 2007 2008 2009 2010 20110

2

4

6

8

10

12

14

16

18

Series1

Impairment Rate by Region

10

0

5

10

15

20

25

2007 2008 2009 2010

London

Scotland

North East

North West

West Midlands

Wales

South West

East Midlands

South East

Impairment by SIC code

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Impairment by Age

12

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

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Cox and Snell/Nagelkerke

2007 All 0.120 0.300

Start-Up

0.149 0.324

Non SU 0.052 0.196

2008 All 0.207 0.390

Start-Up

0.235 0.390

Non SU 0.126 0.336

2009 All 0.308 0.517

Start-Up 0.329 0.500

Non SU 0.205 0.427

2010 All 0.211 0.401

Start-Up 0.238 0.393

Non SU 0.148 0.372

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AUROC Results

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  In

SampleCI CI 2007 Difference

All 0.82 0.816 0.824 0.82 0

2007Start-Up

0.82 0.8155 0.8245 0.82 -0.003

Non SU 0.794 0.785 0.803 0.793 0.002

All 0.852 0.849 0.854 0.841 0.011

2008Start-Up

0.84 0.837 0.844 0.826 0.014

Non SU 0.843 0.837 0.85 0.837 0.006

AUROC Results

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  In

SampleCI CI 2007 Difference

All 0.886 0.884 0.888 0.876 0.01

2009 Start-Up 0.868 0.865 0.87 0.853 0.015

Non SU 0.87 0.865 0.874 0.889 -0.019

All 0.851 0.849 0.854 0.84 0.011

2010 Start-Up 0.83 0.826 0.833 0.811 0.019

Non SU 0.85 0.845 0.856 0.851 -0.001

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

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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

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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

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Impairment in Panel Sample

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2007 2008 2009 20100.00

5.00

10.00

15.00

20.00

25.00

30.00

non_startups startups whole sample

Non-Start-Ups: SIC Code

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APR07 APR08 APR09 APR100

0.05

0.1

0.15

0.2

0.25

0.3

Non-Start-up SMEs 'Bad' Rate: 1992 SIC Code

missing

angriculture

manufacture

constraction

retail trade

hotels and restaurants

transport, storage

financial intermediation

property manegment

computers

R&D legal consult

other professional

education, health and social

private households with employee

Axis Title

Non-Start-Up by Region

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APR07 APR08 APR09 APR100

0.05

0.1

0.15

0.2

0.25

0.3

LondonScotlandEast MidlandsWest MidlandsNorth WestNorth EastWales/South WestSouth WestSouth EastOther

Variable Start-Up Model

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1. Legal Form8. Total Value Of Judgements In The Last 12

Months

2. Company is Subsidiary 9. Number Of Previous Searches (last 12m)

3. 1992 SIC Code 10. Time since last derogatory data item (months)

4. Region 11. 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

 

Start-Up Models’ Coefficient

24 Variable in list order

0 1 2 3 4 5 6 7 8

-10

-5

0

5

10

15

2007200820092010PanelPanel + Year

Start-Up Models’ Coefficient

25 Variable in list order

7 8 9 10 11 12 130

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2007200820092010PanelPanel + Year

Non-Start-up Variables

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1. Legal Form 9. Number Of Previous Searches (last 12m)

2. Parent Company – derog details 10. Time since last derogatory data item (months)

3. 1992 SIC Code 11. Lateness Of Accounts

4. Region 12. 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

Non-Start-up Results

27Variable list order

0 1 2 3 4 5 6 7 8 9

-5

-4

-3

-2

-1

0

1

2007200820092010PanelPanel+Year

Non-Start-up Results

28Incept + variable in listed order

8 9 10 11 12 13 14 15 16 17

-0.5

0

0.5

1

1.5

2007200820092010PanelPanel+Year

Dummy Effects

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1 2 3

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

nonst

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

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Annual Macro variables

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2005 2006 2007 2008 2009 2010 2011

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

30.0

40.0

Annual MVs GDP growth rate

ftsall index change rate

unemployment

inflation

FTS100 change rate

CPI rate

non_year dummy

non_default rate

st_year dummy

st_default rate

whole sample default rate

Axis Title

Averaged Annual Macro Variables

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2007 2008 2009 2010

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

Averaged Annual MVs

gdp_growth ratecpirftsall indexunemploymentinflationFTS100non_year dummynon_default ratest_year dummyst_default ratewhole sample default rate

Axis Title

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Correlations

 gdp3 FAI une infl F100 cpir

gdp31

FAI0.993632 1

une0.791506 0.786689 1

infl-0.98125 -0.95905 -0.7189 1

F1000.978212 0.986059 0.781223 -0.9262 1

cpir0.948904 0.972196 0.826953 -0.87191 0.982503 1

Start-Up Models

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1 2 3 4 5 6

GDP Growth

GDP Growth Lag 1

GDP Growth Average

last 3 Years

GDP Growth

GDP Growth Lag 1

GDP Growth Average

last 3 Years

RPI RPI Lag 1RPI

Average Last 3 Years

FTSE 100 FTSE 100 Lag1

FTSE Average Last 3 Years

Start-up Models

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0 2 4 6 8 10 12 14

-8.0000

-7.0000

-6.0000

-5.0000

-4.0000

-3.0000

-2.0000

-1.0000

0.0000

1.0000

2.0000

Series1Series3Series5Series7Series9Series11

Incept + variable in listed order

Non-Start-Up Models

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1 2 3 4 5 6GDP

Growth Average Last 3 Years

GDP Growth Average Last 3 Years

GDP Growth Lag 1

GDP Growth Lag 1

GDP Growth

GDP Growth

RPI Average Last 3 Years

FTSE 100Lag 1

CPI

FTSE 100 Average Last 3 Years

RPI Lag 1

FTSE 100

Non Macro-Economic Variables

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0 2 4 6 8 10 12 14 16 18

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

123456

Incept + variable in listed order

Start-Up Performance

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logistic regressionpanel modelpanel model with year dummypanel 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 ratepanel model with one year lagged GDP_growth ratepanel model with averaged GDP_growth rate

AUROC Within Sample

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0 1 2 3 4 5 6 7 8 9 10.700

.720

.740

.760

.780

.800

.820

.840

.860

.880

.900

models in listed order

Non-Start-Up Model

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logistic regressionpanel modelpanel model with year dummypanel 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 ratepanel model with one year lagged GDP_growth ratepanel model with averaged GDP_growth rate

AUROC In Sample

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0 1 2 3 4 5 6 7 8 9 10.740

.760

.780

.800

.820

.840

.860

.880

.900

.920

models in listed order

Out-of-Sample Performance 2010

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 Model Non St

logistic regression .837 .753

panel model .828 .757

panel model with year dummy .843 .769

panel model with selected no lagged MV (highest AIC in each category)

.843.758

panel model with selected one year lagged MV (highest AIC in each category)

.843.758

panel model with selected averaged MV (highest AIC in each category)

.843.758

panel model with no lagged GDP_growth rate .833 .759

panel model with one year lagged GDP_growth rate .832 .758

panel model with averaged GDP_growth rate .842 .758

Future?

• Continue to explore macro-economic variables

• Model based on normal • Non-parametric models• Larger range of data• Out-of-Time Sample

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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

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