Proceedings

stressed through a dispersion diagram built on a system of factorial axes organized
downwards, according to their importance in explaining the total variance of the
cloud.
The discriminant analysis (DA) is a multi-varied classification method, initially
suggested by Fisher in 1936, in order to differentate between individuals belonging to
the same species, according to a series of specific characteristics. The method aims to
classify a population into predefined groups, based on certain score functions (Z),
which express the relations between the independent variables, Xi, and the categories
of classification variables. The relation between the category dependent variable
(dichotomous or multi-chotomous) and the linear combinations of several metric
independent variables is of the form: Z = α0 + α1X1 + α 2X2 + ... + α nXn. In this
relation, Z is the computed score; Xi with (i=1,...,n) are the independent variables; αi
are the coefficients of the model (unkown). Each company can be associated to a
score computed based on the individual values of the Xi variables. According to the
value of the obtained score, a company can be included in one class or another of the
category variable. In DA, the methodological approach implies: obtaining the
discriminant functions (as a linear combination of Xi), identifying the independent
variables that contribute the most to explaining the differences between the groups,
classifying the individuals for predictive purposes by allocating them to a specific
group according to the score obtained, starting from the specified values of the Xi
variables and evaluating the accuracy of the classification (Lebart at al., 2006).
The logistic regression analysis (LRA) is applied in order to determine the probability
of occurrence of the insolvency risk and uses the regression models with dependent
alternative variables, of the form: Y = β0 + βiXi + ε, where Y = 0 in case there is no
insolvency risk and Y = 1 in case the insolvency risk exists, and Xi represents the
independent variables (factors), βi the coefficients of the logistic regression model,
and ε is the error component. Moreover, since Y is a Bernoulli variable (Gujarati,
2004), it associates to the values one and zero the following probabilities of
occurrence: p for Y = 1 and q for Y = 0. LRA starts from the idea that the conditioned
average, M(Yi/Xi) = pi, is based on a logistic distribution: M(Yi/Xi) = pi = 1/[1+e^-
(β0+βiXi)] = 1/(1+e^-zi). After applying the reverse function, there will result that zi =
ln[pi/(1-pi)], and the logistic model will be defined by the relation Li = ln[pi/(1-pi)] =
β0+βiXi+εi (Gujarati, 2004).
The identification of the profile of the insolvency risk (based on the three evaluation
methods), obtaining the coefficients of the disciminant function, as well as those of
the logistic regression model, have been achieved using the SPSS 19.0 statistic
software.
3. RESEARCH RESULTS AND DISCUSSIONS
After applying the FAMC method on the data in the studied sample, we have obtained
the diagram in Figure 2. This diagram presents the associations between The state of
the company and The index classes corresponding to the three evaluation methods:
based on the EV (own capital), on the MC, and on the VFCF. This method was
applied only after the discretization of the expression variables of the company’s
value (corresponding to the three methods), and for three classes have been built for
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