Financial Ratios as Predictors of Failure: Evidence from ... - ERIM

Table 6(b) Correlations (Sample Two, 1 st year before default)
CFTD1 RETA1 WCTA1 CR1 NITA1 TDTA1 SATA1 Size1 EQTC1 ROE1
CFTD1 1.000 0.100 0.113 0.219* 0.087 -0.104 0.038 0.044 0.218* 0.015
RETA1 0.100 1.000 0.930** 0.167 0.927** -0.940** -0.003 0.352** 0.257** 0.032
WCTA1 0.113 0.930** 1.000 0.281** 0.944** -0.956** -0.021 0.281** 0.287** 0.092
CR1 0.219* 0.167 0.281** 1.000 0.175 -0.163 -0.128 0.070 0.215* -0.009
NITA1 0.087 0.927** 0.944** 0.175 1.000 -0.212 -0.043 0.293** 0.238* 0.249*
TDTA1 -0.104 -0.940** -0.956** -0.163 -0.212 1.000 0.033 -0.266** -0.313** -0.108
SATA1 0.038 -0.003 -0.021 -0.128 -0.043 0.033 1.000 -0.215* 0.090 -0.075
Size1 0.044 0.352** 0.281** 0.070 0.293** -0.266** -0.215* 1.000 -0.139 -0.032
EQTC1 0.218* 0.257** 0.287** 0.215* 0.238* -0.313** 0.090 -0.139 1.000 0.104
ROE1 0.015 0.032 0.092 -0.009 0.249 -0.108 -0.075 -0.032 0.104 1.000
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
5.2 Estimation results of the Logit model
Similar to linear regression, logistic regression also gives estimation for the coefficient of
each parameter and its relevant significance (based on t-ratios) to the dependent variable
(PD). But the interpretation of logit regression is different, since it assumes a non-linear
relationship between probability and the independent variables. Remember that
Li=ln[pi/(1-pi)], after taking the antilog of the estimated logit, we get pi/(1-pi) (that is, the
odds ratio). In this case, since p represents probability of default, pi/(1-pi) can also be
called as odds of default. Therefore, instead of looking at parameter β (which is used to
explain the ln(odds of default)), Exp(β) should be considered the equivalent value when
interpreting odds of default directly. Table 7 shows the logit results by using matched
samples (sample one). The variables included in the model (RETA, NITA, TDTA, and
ROE) are those which jointly make the model valid and maximize the predictive power
of the model.
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