Credit performance of the UK
SMEs 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
1
Outline
• Background
• Data
• Cross-sectional Analysis
• Panel Data with Dummies
• Panel Data with Macroeconomic Variables
• Future plans?
• Conclusion
2
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
3
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
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
Credit risk approaches
• Lending to individuals
• Lending to businesses
- Relatively small amounts
of money lent to a large
number of customers
- focus more on prediction,
less on causality
- Management Science
and Data Mining
- Large amounts of money
lent to a relatively small
number of businesses
- focus more on causality,
less on prediction
- Finance and Accounting
6
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
7
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
Impairment Rate in UK (%)
18
16
14
12
10
8
Series1
6
4
2
0
2006
2007
2008
2009
2010
2011
9
Impairment Rate by Region
25
20
London
Scotland
North East
15
North West
West Midlands
Wales
10
South West
East Midlands
South East
5
0
2007
2008
2009
2010
10
Impairment by SIC code
11
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
13
Cox and Snell/Nagelkerke
2007
2008
All
0.308
0.517
Start-Up 0.149 0.324
Start-Up 0.329
0.500
Non SU 0.052 0.196
Non SU 0.205
0.427
All
0.211
0.401
Start-Up 0.235 0.390
Start-Up 0.238
0.393
Non SU 0.126 0.336
Non SU 0.148
0.372
All
0.120 0.300
0.207 0.390
2009
2010
All
14
AUROC Results
In
Sample
CI
CI
2007
Difference
0.82
0.816
0.824
0.82
0
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
0.84
0.837
0.844
0.826
0.014
0.843
0.837
0.85
0.837
0.006
All
Start2007
Up
2008
StartUp
Non SU
15
AUROC Results
In
Sample
CI
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
16
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
17
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
18
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
Impairment in Panel Sample
30.00
25.00
20.00
15.00
10.00
5.00
0.00
2007
2008
non_startups
2009
startups
2010
whole sample
20
Non-Start-Ups: SIC Code
Non-Start-up SMEs 'Bad' Rate: 1992 SIC Code
0.3
missing
angriculture
0.25
manufacture
constraction
retail trade
0.2
Axis Title
hotels and restaurants
transport, storage
0.15
financial intermediation
property manegment
0.1
computers
R&D legal consult
0.05
other professional
education, health and social
0
private households with employee
APR07
APR08
APR09
APR10
21
Non-Start-Up by Region
0.3
London
0.25
Scotland
East Midlands
0.2
West Midlands
North West
0.15
North East
Wales/South West
0.1
South West
South East
0.05
Other
0
APR07
APR08
APR09
APR10
22
Variable Start-Up Model
1. Legal Form
8. 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
6. Oldest Age Of Current
Directors/Proprietors supplied (Years)
7. Number Of Directors Holding
Shares
12. Time Since Last Annual Return
13. Total Assets
23
Start-Up Models’ Coefficient
15
10
2007
2008
5
2009
2010
0
0
2
4
6
8
Panel
Panel + Year
-5
-10
Variable in list order
24
Start-Up Models’ Coefficient
1.8
1.6
1.4
2007
1.2
2008
1
2009
0.8
2010
0.6
Panel
0.4
Panel + Year
0.2
0
7
8
9
10
11
Variable in list order
12
13
25
Non-Start-up Variables
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
26
Non-Start-up Results
1
0
0
2
4
6
-1
8
10
2007
2008
2009
-2
2010
Panel
-3
Panel+Year
-4
-5
Variable list order
27
Non-Start-up Results
1.4
1.2
1
2007
0.8
2008
0.6
2009
0.4
2010
0.2
Panel
0
-0.2 8
9
10
11
12
13
14
15
16
17
Panel+Year
-0.4
-0.6
Incept + variable in listed order
28
Dummy Effects
0
1
2
3
-0.5
-1
-1.5
-2
non
st
-2.5
-3
-3.5
-4
29
Panel with Macro-economic
Variable
Currently Exploring of Macro-economic
Variables:
1.
2.
3.
4.
5.
6.
7.
UNEMPLOYMENT RATE
INFLATION ANNUAL CHANGE
CPI
CPI ANNUAL CHANGE
FTSE ALL SHARE INDEX CHANGE
FTSE100 ANNUAL INDEX CHANGE
FTSE 100 ANNUAL RETURN
30
Annual Macro variables
Annual MVs
GDP growth rate
40.0
ftsall index change rate
unemployment
30.0
inflation
20.0
FTS100 change rate
Axis Title
10.0
CPI rate
0.0
2005
-10.0
-20.0
-30.0
2006
2007
2008
2009
2010
2011
non_year dummy
non_default rate
st_year dummy
st_default rate
whole sample default rate
-40.0
31
Averaged Annual Macro
Variables
Averaged Annual MVs
30.0
gdp_growth rate
25.0
cpir
ftsall index
20.0
unemployment
Axis Title
15.0
inflation
FTS100
10.0
non_year dummy
5.0
non_default rate
0.0
2007
-5.0
2008
2009
2010
st_year dummy
st_default rate
whole sample default rate
-10.0
32
Correlations
gdp3
gdp3
FAI
une
infl
F100
cpir
FAI
une
infl
F100
cpir
1
0.993632
1
0.791506 0.786689
1
-0.98125 -0.95905
-0.7189
1
0.978212 0.986059 0.781223
-0.9262
1
0.948904 0.972196 0.826953 -0.87191 0.982503
1
33
Start-Up Models
1
2
GDP
Growth
GDP
Growth
Lag 1
RPI
RPI Lag 1
FTSE 100
FTSE 100
Lag1
3
GDP
Growth
Average
last 3
Years
4
5
GDP
Growth
GDP
Growth
Lag 1
6
GDP
Growth
Average
last 3
Years
RPI
Average
Last 3
Years
FTSE
Average
Last 3
Years
34
Start-up Models
2.0000
1.0000
0.0000
0
2
4
6
8
10
12
14
-1.0000
Series1
-2.0000
Series2
Series3
-3.0000
Series4
Series5
-4.0000
Series6
-5.0000
-6.0000
-7.0000
-8.0000
Incept + variable in listed order
35
Non-Start-Up Models
1
GDP
Growth
Average
Last 3
Years
RPI
Average
Last 3
Years
FTSE 100
Average
Last 3
Years
2
GDP
Growth
Average
Last 3
Years
3
4
5
6
GDP
Growth
Lag 1
GDP
Growth
Lag 1
GDP
Growth
GDP
Growth
FTSE 100
Lag 1
RPI
Lag 1
CPI
FTSE 100
36
Non Macro-Economic
Variables
1
0.5
0
0
2
4
6
8
10
12
14
-0.5
16
18
1
2
-1
3
-1.5
4
5
-2
6
-2.5
-3
-3.5
Incept + variable in listed order
37
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
38
AUROC Within Sample
.900
.880
.860
.840
.820
.800
.780
.760
0
1
2
3
4
5
6
models in listed order
7
8
9
10
39
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
40
AUROC In Sample
.900
.880
.860
.840
.820
.800
.780
0
1
2
3
4
5
6
models in listed order
7
8
9
10
41
Out-of-Sample Performance
2010
Model
Non
logistic regression
.837
St
.753
panel model
.828
.757
panel model with year dummy
.843
.769
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
.843
.843
.843
.758
.758
.758
panel model with one year lagged GDP_growth rate
.832
.759
.758
panel model with averaged GDP_growth rate
.842
.758
.833
42
Future?
• Continue to explore macro-economic
variables
• Model based on normal
• Non-parametric models
• Larger range of data
• Out-of-Time Sample
43
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