3.2 The generalized analysis of crisp and fuzzy forecasting methods

In the concluding experiments, the comparative analysis of application of all the considered methods was carried out. The following methods were considered [4]:

fuzzy neural network ANFIS;

fuzzy neural network TSK; and

crisp forecasting methods: Kromonov's method and Byelorussian bank association method.


Table 11.

Comparative results of forecasting using method FGMDH in dependence on period of input data collection.


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

Table 12.

Input data—financial indices, the same as in experiment 8.

Accuracy of forecasting in dependence on data collection period.

Total number of errors

3.1 The application of fuzzy GMDH for financial state forecasting

so it has advantages in long-term forecasting (2 or more years).

3.2 The generalized analysis of crisp and fuzzy forecasting methods

crisp forecasting methods: Kromonov's method and Byelorussian bank

 10 14 3 7 2005 9 13 3 6 8 11.4 3 5 2007 7 10 2 5 6 8.5 1 5 6 8.5 2 4

Input data period Total error number % of errors First type of errors Second type of errors

Comparative results of forecasting using method FGMDH in dependence on period of input data collection.

are presented.

Experiment/number

rules

Table 10.

ments with FNN TSK.

input data collection period.

fuzzy neural network ANFIS;

fuzzy neural network TSK; and

association method.

Table 11.

92

In Table 10, the comparative results of forecasting versus period of input data

First type of errors

January 1, 2008 5 rules 7 0 7 10 July 1, 2009 5 rules 5 0 5 7 July 1, 2009 10 rules 7 3 4 10

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

Second type of errors

Total % of errors

In the process of investigations, fuzzy group method of data handling (FGMDH) was also suggested for financial state of Ukrainian banks forecasting [3]. GMDH is the inductive modeling method that enables to construct a model automatically by experimental data [3]. As input data, the same indices were used as in the experi-

In Table 11, the forecasting accuracy of FGMDH is presented in dependence on

If we compare the results of FGMDH with the results of FNN TSK, one can see that FNN TSK gives better results for short-term risk forecasting (one year before possible bankruptcy) while FGMDH has better accuracy using older input data and

In the concluding experiments, the comparative analysis of application of all the considered methods was carried out. The following methods were considered [4]:

Comparative results analysis of various forecasting methods.

As input data, the financial indices of Ukrainian banks on July 2007 year were used. The results of application of all methods for bankruptcy risk analysis are presented in Table 12.
