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Modelling Credit Risk for SMEs: Evidence from the U.S. Market
Author(s) -
Altman Edward I.,
Sabato Gabriele
Publication year - 2007
Publication title -
abacus
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.632
H-Index - 45
eISSN - 1467-6281
pISSN - 0001-3072
DOI - 10.1111/j.1467-6281.2007.00234.x
Subject(s) - business , order (exchange) , basel ii , credit risk , sample (material) , panel data , logistic regression , capital requirement , logit , construct (python library) , capital (architecture) , predictive power , basel iii , econometrics , economics , actuarial science , finance , microeconomics , computer science , programming language , chemistry , philosophy , archaeology , chromatography , epistemology , machine learning , history , incentive
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.