Kosmidou and Zopounidis (2008) develop a bank failure prediction
model based on a multicriteria decision technique called UTilites Additives
DIScriminants (UTADIS). The purpose of UTADIS method is to develop a
classification model through an additive value function. Based on the values
obtained from the additive value function, the authors classify banks into
multiple groups by comparing them with some reference profiles (also called
cut-off points). UTADIS is well suited to the ordinal classification problems
and it is not sensitive to the statistical problems because the additive utility
function is performed through mathematical linear programming techniques
instead of statistical methods. Using a sample of US banks for the years
1993—2003, the authors use this technique to differentiate US banks between
failed and non-failed. The results show that UTADIS is quite efficient
for the evaluation of bank failure as early as four years before it occurs.
The authors also compare UTADIS with other traditional multivariate data
analysis techniques and find that UTADIS performs better, and could be used
efficiently for predicting bank failures.
The Multicriteria Decision Aid (MCDA) method is a model that allows
for the analysis of several preference criteria simultaneously. Zopounidis
and Doumpos (1999b) apply MCDA to sorting problems, where a set of
alternative actions is classified into several predefined classes. Based on the
multidimensional nature of financial risk, Doumpos and Zopounidis (2000)
propose a new operational approach called the Multi-Group Hierarchical
Discrimination (M.H.DIS) method — which originates from MCDA — to
determine the risk classes to which the alternatives belong. Using World
Bank data, the authors apply this method to develop a model which classifies
143 countries into four risk classes based on their economic performance and
creditworthiness. The authors conclude that this method performs better than
traditional multiple discriminant analysis.1