The use of non-parametric methodologies, the introduction of non-financial variables,
and the development of models geared towards the homogeneous characteristics of
corporate sub-populations have recently experienced a surge of interest in the bankruptcy
literature. However, no research on default prediction has yet focused on micro-entities
(MEs), despite such firms’ importance in the global economy. This paper builds the first
bankruptcy model especially designed for MEs by using a wide set of accounts from 1999
to 2008 and applying artificial neural networks (ANNs). Our findings show that ANNs
outperform the traditional logistic regression (LR) models. In addition, we also report
that, thanks to the introduction of non-financial predictors related to age, the delay
in filing accounts, legal action by creditors to recover unpaid debts, and the ownership
features of the company, the improvement with respect to the use of solely financial
information is 3.6%, which is even higher than the improvement that involves the use
of the best ANN (2.6%)
