4.5 Concluding experiments

In the final series of experiments, investigations and detailed analysis of various methods for forecasting bankruptcy risk were performed. The following methods were investigated: FNN ANFIS, FNN TSK, FGMDH, regression models, logit models, and probit models.

Period of input data was 2007 (1 year before possible bankruptcy).

Comparative analysis of all the forecasting methods is presented in Table 27.

As one may see from this table, fuzzy methods and models show much better results than crisp methods: ARMA, logit models, and probit models. When forecasting by one year prior to current date, fuzzy neural network TSK shows better results than FGMDH. But when forecasting for longer intervals (several years), FGMDH is the best method.

In a whole, the conclusions of experiments with European banks completely confirmed the conclusions of experiments with Ukrainian banks.


Table 26. Forecasting results of probit models. Banks Financial State Analysis and Bankruptcy Risk Forecasting with Application of Fuzzy… DOI: http://dx.doi.org/10.5772/intechopen.82534


Table 27.

4.4.3 Probit models

was obtained.

exclusion of insignificant variables.

4.5 Concluding experiments

models, and probit models.

FGMDH is the best method.

Input data Testing

Forecasting results of probit models.

Table 26.

102

sample

input variables. It has the following form:

IY ¼ Cð Þþ 1 Cð Þ2 ∗X<sup>1</sup> þ Cð Þ3 ∗X<sup>2</sup> þ Cð Þ 4 ∗X<sup>3</sup> þ Cð Þ5 ∗X<sup>4</sup> þ Cð Þ 6 ∗X<sup>5</sup> þ Cð Þ7 ∗X<sup>6</sup> þ Cð Þ 8 ∗X<sup>7</sup> þ Cð Þ 9 ∗X<sup>8</sup>

Y ¼ 1 � @CLOGISTICð�ðCð Þþ 1 Cð Þ2 ∗X<sup>1</sup> þ Cð Þ3 ∗X<sup>2</sup>

The next experiments were carried out with probit models [9, 10]. The first constructed model was the linear probit model based on 206 banks using all the

Accounting and Finance - New Perspectives on Banking, Financial Statements and Reporting

þ Cð Þ 4 ∗X<sup>3</sup> þ Cð Þ5 ∗X<sup>4</sup> þ Cð Þ 6 ∗X<sup>5</sup> þ Cð Þ7 ∗X<sup>6</sup> þ Cð Þ 8 ∗X<sup>7</sup> þ Cð Þ 9 ∗X8ÞÞ As the experiments had shown that the inputs net interest margin (X3) and net financial result (X8) very weakly influence on the forecasting accuracy, they were excluded in the next experiments. The next probit model included six variables. Furthermore, in this model insignificant variables debt/assets (X1) and loans/ deposits (X2) were excluded, and as a result, linear probit model with four variables

Each of the constructed probit models was checked on the test sample of 50 banks. The results of application of all probit models are presented in Table 26. As one may see from Table 26, the application of all the probit models gives relative error 14–18%, which is much worse than results obtained by fuzzy neural networks. It is worth to mention the decrease of model forecasting quality after

In the final series of experiments, investigations and detailed analysis of various methods for forecasting bankruptcy risk were performed. The following methods were investigated: FNN ANFIS, FNN TSK, FGMDH, regression models, logit

Comparative analysis of all the forecasting methods is presented in Table 27. As one may see from this table, fuzzy methods and models show much better results than crisp methods: ARMA, logit models, and probit models. When forecasting by one year prior to current date, fuzzy neural network TSK shows better results than FGMDH. But when forecasting for longer intervals (several years),

In a whole, the conclusions of experiments with European banks completely

Second type of errors

Total number of errors

% of errors

Period of input data was 2007 (1 year before possible bankruptcy).

confirmed the conclusions of experiments with Ukrainian banks.

First type of errors

All variables (eight) 50 5 2 7 14 Six variables 50 5 2 7 14 Four variables 50 6 3 9 18 Comparative analysis of methods for banks bankruptcy forecasting.
