4.2 Application of fuzzy neural networks for European banks bankruptcy risk forecasting

The period for which the data were collected was 2004–2008. The possible bankruptcy was analyzed in 2009. The indicators of 165 banks were considered among which more than 20 banks displayed the worsening of the financial state in that year. Fuzzy neural networks and Fuzzy Group Method of Data Handling (FGMDH) were used for bank financial state forecasting.

In accordance with the above stated goal, the investigations were carried out for detecting the most informative indicators (factors) for financial state analysis and bankruptcy forecasting. Taking into account incompleteness and unreliability of input data, FNN ANFIS and TSK were suggested for bankruptcy risk forecasting.

After performing a number of experiments, the data set of financial indicators was found using which FNN made the best forecast. These indicators are the following:

debt/assets = (short-term debt + long-term debt)/total assets;

loans to deposits ratio;

net interest margin (NIM) = net interest income/earning assets;

return on equity (ROE) = net income/stockholder equity;

return on assets (ROA) = net income/assets equity;

cost/income = operating expenses/operating income; and

equity/assets = total equity/total assets.

A series of experiments was carried out for determining the influence of the number of rules and period of data collection on forecasting results.

debt/assets = (short-term debt + long-term debt)/total assets;

%% of errors

% of errors

Banks Financial State Analysis and Bankruptcy Risk Forecasting with Application of Fuzzy…

—5 8 16 0 8 —5 7 14 0 7 —5 5 10 0 5 2007—5 12 0 1 —10 8 16 0 8 —10 8 16 1 7 —10 11 22 7 4 —10 4 8 0 4

—5 8 16% 0 8 —5 8 16% 1 7 —5 8 16% 4 4 —5 4 8% 0 4

Number of first type errors

Number of first type errors

> Number of second type errors

Number of second type errors

net interest margin (NIM) = net interest income/earning assets;

return on equity (ROE) = net income/stockholder equity;

cost/income = operating expenses/operating income; and

Input data collection period (forecasting interval) makes influence on forecast-

In next experiments, Fuzzy Group Method of Data Handling (FGMDH) was applied for European banks financial state forecasting. Fuzzy GMDH enables to construct forecasting models using experimental data automatically without expert [3]. The additional advantage of FGMDH is possibility to work with the

As the input data in these experiments, the same indicators as in experiments with FNN TSK were used. In Table 22, forecasting results are presented in depen-

4.3 The application of fuzzy GMDH for bank financial state forecasting

return on assets (ROA) = net income/assets equity;

Total errors number

Forecasting results for FNN TSK versus number of rules and data period.

Total errors number

DOI: http://dx.doi.org/10.5772/intechopen.82534

Forecasting results for FNN ANFIS versus number of rules and data period.

equity/assets = total equity/total assets.

dence on input data period collection for FGMDH

loans to deposits;

Experiment/number

Experiment/number of

rules

of rules

Table 20.

Table 21.

ing results.

fuzzy information.

99

In the first series of experiments, FNN TSK was used for forecasting.
