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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. Outline. Background Data Cross-sectional Analysis

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credit performance of the uk smes through the crisis

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

outline
Outline
  • Background
  • Data
  • Cross-sectional Analysis
  • Panel Data with Dummies
  • Panel Data with Macroeconomic Variables
  • Future plans?
  • Conclusion
smes cornerstone of the economy
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
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
credit scoring and smes
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)
credit risk approaches
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

slide7
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
slide8
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
initial analysis
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
2 comments
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
panel analysis
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

panel analysis1
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
start up models coefficient
Start-Up Models’ Coefficient

Variable in list order

start up models coefficient1
Start-Up Models’ Coefficient

Variable in list order

non start up results
Non-Start-up Results

Variable list order

non start up results1
Non-Start-up Results

Incept + variable in listed order

panel with macro economic variable
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
start up models1
Start-up Models

Incept + variable in listed order

non macro economic variables
Non Macro-Economic Variables

Incept + variable in listed order

start up performance
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

auroc within sample
AUROC Within Sample

models in listed order

non start up model
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

auroc in sample
AUROC In Sample

models in listed order

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