Modelling Credit Risk for SMEs: Evidence from the U.S. Market

TLDR

SMEs play a fundamental role in many economies and are a focus of the new Basel Capital Accord. The study develops a distress prediction model for SMEs and evaluates its effectiveness relative to a generic corporate model, also assessing its potential to reduce bank capital requirements under Basel rules. Using panel data of over 2,000 U.S. SMEs with sales under $65 million from 1994–2002, the authors selected key financial variables and applied logit regression to build a one‑year default prediction model.

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 analyse its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analysed 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 panel data of over 2,000 U.S. 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 per cent 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.