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Modeling Credit Risk For SMEs : Evidence From The US Market

Published: September, 2007
ABACUS Vol. 43, Issue. 3
By Gabriele Sabato, and Edward I. Altman
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Abstract

Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behavior of financial measures for SMEs is analyzed and the most significant variables in predicting the entities’ credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on a panel of over 2,000 US firms (with sales less than $65 million) over the period 1994-2002, we develop a one-year default prediction model. This model has an out of sample prediction power which is almost 30% higher than a generic corporate model. An associated objective is to observe our model’s ability to lower bank capital requirements considering the new Basel Capital Accord’s rules for SMEs.

"The main goal of this paper is to analyze a rather complete set of financial ratios linked to US SMEs and find out which are the most predictive ones of the entities’ credit worthiness.> "

Introduction

Small and medium sized enterprises (SMEs) are reasonably considered the backbone of the economy of many countries all over the world. For OECD members, the percentage of SMEs out of the total number of firms is greater than 97%. In the US, SMEs provide approximately 75% of the net jobs added to the economy and employ around 50% of the private workforce, representing 99.7% of all employers. Thanks to the simple structure of most SMEs, they can respond quickly to changing economic conditions and meet local customers’ needs, growing sometimes into large and powerful corporations or failing within a short time of the firm’s inception. From a credit risk point of view, SMEs are different from large corporates for many reasons. For example, Dietsch and Petey (2004) analyze a panel of German and French SMEs and conclude that they are riskier but have a lower asset correlation with each other than large businesses do. Indeed, we hypothesize that applying a default prediction model developed on large corporate data to SMEs will result in lower prediction power and likely a poorer performance of the entire corporate portfolio than with separate models for SMEs and large corporates.

The main goal of this paper is to analyze a rather complete set of financial ratios linked to US SMEs and find out which are the most predictive ones of the entities’ credit worthiness. One motivation of our study is to show the significant importance for banks of modeling credit risk for SMEs separately from large corporates. The only study that we are aware of that focused on modeling credit risk specifically for SMEs is a fairly distant article by Edmister (1972). He analyzed 19 financial ratios and, using multivariate discriminant analysis, developed a model to predict small business defaults. His study is carried on a sample of small and medium sized enterprises over the period 1954-1969. We expand and improve his work using, for the first time, the definition of SME as contained in new Basel Capital Accord (sales less than €50 million) and applying a logit regression analysis to develop the model. We extensively analyze a large number of relevant financial measures in order to select the most predictive ones. Then, we use these variables as predictors of the default event. The final output is not only an extensive study of SME financial characteristics, but also a model to predict their PD, specifically the one year PD required under Basel II.

The performance of this model is also compared with the performance of a well-known generic corporate model in order to show the importance of modeling SME credit risk separately from a generic corporate model. We acknowledge that our analysis could still be improved using qualitative variables as predictors in the failure prediction model to better discriminate between SMEs (as recent literature, e.g. Lehmann (2003) and Grunet et al. (2004), demonstrate). The database that we use (COMPUSTAT), however, does not contain qualitative variables. Nevertheless, the performance accuracy of the model that we develop specifically to predict SME default is significantly high both on an absolute and relative basis. While there have been many successful models developed for corporate distress prediction purposes, and at least two are commonly used by practitioners on a regular basis, none were developed specifically for SMEs.

In addition, we feel that the original Z-Score models (developed by one of the authors) can be improved upon by transforming several of the variables to adjust for the changing values and distributions of several of the key variables of that model. We feel that a parsimonious selection of variables, some of which are transformed, can compensate for the fact that our model cannot make use of qualitative variables that are available only from banks and other lending institutions files.

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