SMEs Default Prediction Model: An Analysis of the Existing Literature

Abstract

The ability to accurately and consistently predict small firms’ default would be beneficial for both lending banks and firms and hence could help to build a more stable and prosperous economy. In this study we use more than 30,000 UK small and medium sized enterprises (SMEs) covering the period from 2000 to 2008 and from 22 manufacturing industries to analyse the existing accounting-based default prediction model for UK SMEs. We tested the prominent SMEs default prediction model introduced by Altman et al. (2010) using logistic regressions to establish their reliability and prediction power for UK sample. The evaluation of the SMEs model has been carried out by comparing the accuracy of the model in predicting default events with Ohlson model which is mainly developed for large firms. Moreover, different sample tests revealed that SMEs’ credit risk behavior is unique and it is crucial to build the default prediction models specifically for these firms to gain the most accurate and reliable outcome. This study also sheds light on effect of economic cycles on SMEs default prediction accuracy. Finally, our results ascertain that controlling for the effects of industry and ownership structure significantly enhances the accuracy of the default prediction model for UK SMEs.