The current research develops a theoretical framework based on the ResourceAdvantage Theory of Competition (Hunt, 2000) for the selection of appropriate
variables. Using a review of the literature as well as to interviews and a survey, 170
potential retail performance variables were identified as possible for inclusion in the
model. To produce a relative simple model with the aim of avoiding over-fitting, a
limited number of key variables or principal components were selected to predict
default. Five credit-scoring techniques: Naive Bayes, Logistic Regression, Recursive
Partitioning, Artificial Neural Network, and Sequential Minimal Optimization (SMO)
were employed on a sample of 195 healthy and 51 distressed businesses from the
USA market over five time periods: 1994-1998, 1995-1999, 1996-2000, 1997-2001
and 1998-2002.Analyses provide sufficient evidence that the five credit scoring methodologies
have sound classification ability in the year before financial distress. Moreover, they
still remained sound even five years prior to financial distress. However, it is difficult
to conclude which modelling technique has the highest classification ability
uniformly, since model performance varied in terms of different time scales. The
analysis also showed that external environment influences do impact on default
assessment for all five credit-scoring techniques, but these influences are weak.
These findings indicate that the developed models are theoretically sound. There is
however a need to compare their performance to other approaches.To explore the issue of the model's performance two approaches are taken. First,
rankings from the study were compared with those from a standard rating system—in
this case the well-established Moody's Credit Rating. It is assumed that the higher
the degree of similarity between the two sets of rankings, the greater the credibility
of the prediction model. The results indicated that the logistic regression model and
the SMO model were most comparable with Moody's. Secondly, the model's
performance was assessed by applying it to different geographical areas. The original
USA model was therefore applied to a new US data set as well as the European and
Japanese markets. Results indicated that all market models displayed similar
discriminating ability one year prior to financial distress. However, the USA model
performed relatively better than European and Japanese models five years before
financial distress. This implied that a financial distress model has potentially better
prediction ability when based on a single market.Following this result it was decided to explore the performance of a generic global
model, since model construction is time-consuming and costly. A composite model
was constructed by combining data from USA, European and Japanese markets. This
composite model had sound prediction performance, even up to five years before
financial distress, as the accuracy rate was above 85.15% and AUROC value was
above 0.7202. Comparing with the original USA model, the composite model has
similar prediction performance in terms of the accuracy rate. However, the composite
model presented a worse prediction utility based on the AUROC value. A future
research direction might be to include more world retailing markets in order to
ensure the model's prediction utility and practical applicability
