Financial Ratios and Discriminant Analysis 591
potential of firms, both theoretically and practically, is questionable.'' In almost
every case, the methodology was essentially univariate in nature and emphasis
was placed on individual signals of impending problems.* Ratio analysis presented
in this fashion is susceptible to faulty interpretation and is potentially
confusing. For instance, a firm with a poor profitability and/or solvency record
may be regarded as a potential bankrupt. However, because of its above average
liquidity, the situation may not be considered serious. The potential ambiguity
as to the relative performance of several firms is clearly evident. The
crux of the shortcomings inherent in any univariate analysis lies therein. An
appropriate extension of the previously cited studies, therefore, is to build
upon their findings and to combine several measures into a meaningful predictive
model. In so doing, the highlights of ratio analysis as an analytical
technique will be emphasized rather than downgraded. The question becomes,
which ratios are most important in detecting bankruptcy potential, what
weights should be attached to those selected ratios, and how should the weights
be objectively established.
After careful consideration of the nature of the problem and of the purpose
of the paper, a multiple discriminant analysis (MDA) was chosen as the
appropriate statistical technique. Although not as popular as regression analysis,
MDA has been utilized in a variety of disciplines since its first application
in the lQSO's." During those earlier years MDA was used mainly in the biological
and behavioral sciences.^'* More recently this method had been applied
successfully to financial problems such as consumer credit evaluation" and
investment classification. For instance in the latter area, Walter utilized a MDA
model to classify high and low price earnings ratio firms,^^ and Smith applied
the technique in the classification of firms into standard investment categories.^*
MDA is a statistical technique used to classify an observation into one of
several a priori groupings dependent upon the observation's individual characteristics.
It is used primarily to classify and/or make predictions in problems