Improving the predictive performance of longitudinal risk models for UK SMEs
Abstract
In 2008, the whole world was a picture of economic depression. During the credit
crisis, the viability of Small and Medium-sized Enterprises (SMEs) has been
profoundly jeopardised because of their vulnerability. After the credit crisis, raising
the awareness of risk management in the banking sector has been needed. The
launch of the Basel III regulations proposes a more stringent requirement on
capital and liquidity to promote stability in the financial system. Regarding credit
risk, the probability of defaults (PDs) models essentially remains unchanged. On
the other hand, the expected credit loss (ECL) model under International Financial
Reporting Standard (IFRS) 9 proposed by International Accounting Standard
Board (IASB) is expected to bring significant influence on SMEs and Banking
sectors since there is an increase of loan loss provision undoubtedly.
This thesis aims to explore the performance of SMEs due to the fundamental role
played in a country’s economic development. A large dataset used in this thesis
includes 79 characteristics of UK SMEs from 2007 to 2010. The SMEs were
pigeonholed into start-ups (growing businesses) and non-start-ups (developed
businesses) considering their different behaviour during the credit crisis. However,
the dataset contains a substantial number of incomplete observations, and the
analysis of such dataset is a handicap. In light of this, Multiple Imputation by Chain
Equations (MICE), a state-of-the-art and flexible technique, has been employed to
deal with missing data. Although this technique is widely used in the medical field
but not in credit risk modelling, it takes into account the uncertainty within the
process of combing multiple imputed dataset to produce estimated coefficient, and
each type of variables has its specific model for imputation.
Once getting over the missing data problem, cross-section analysis is followed to
build up the credit risk model. Logistic regression and shrinkage regression are
used to analyse the relationship among the selected variables and Generalised
Additive Models (GAM) is performed to capture their non-linear relationship, derive
a direct marginal trend and plot how explanatory variables influence SMEs
performance. Subsequently, time effects are accounted for by employing a panel
model controlling the time effect using year dummy variables or macroeconomic
variables. It can be found that the panel data models with firm-specific and
macroeconomic variables are preferred as the AUROC is at least as other models,
especially during the credit crisis.
Again, the ex-post regulations, the 12-month ECL may not capture a significant
increase in credit risk if the economic downturn is expected to occur at a later
stage. The lifetime ECL captures this downturn and will, therefore, identify a
significant increase in credit risk sooner. The panel data models are believed to
capture the change in the macroeconomics during the credit risk and are
appropriate to apply to meet Basel III and IFRS 9 requirements as these
regulations require to consider the economic cycle.