4. Comparative analysis of bankruptcy risk forecasting methods for European banks under uncertainty

#### 4.1 Introduction

In Figure 2, the probability of error for different forecasting methods and

% of errors

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

2012—4 12 24 7 5 2013—4 14 28 5 9 2014—4 9 18 5 4

Number of first type errors

Number of second type errors

In Figure 3, dependence of error probability versus number of rules in FNN TSK

Analyzing the performed experiments, the following conclusions may be made.

1. The experimental investigations of efficiency of different forecasting methods FNN TSK, matrix method of Nedosekin, and rating system CAMELS were carried out for the problem of bankruptcy risk forecasting of Ukrainian banks.

2. Results obtained by FNN TSK are the best (min error%). Mean forecasting accuracy by TSK with Gaussian MF is equal to 85%, with trapezoidal MF mean accuracy is 82%, triangular MF gives 79%, matrix method of Nedosekin 70%,

3.With the increase of rules, number error probability first decreases, then

Various methods for Ukrainian banks financial state forecasting were considered and analyzed. The following methods were considered [3, 4]: fuzzy neural network

while standard rating system CAMELS has 75% accuracy.

3.4 General conclusions of investigations for Ukrainian banks

attains minimum and then begins to raise.

Bankruptcy risk forecasting results for different methods.

Error probability for different rules number in FNN.

General number of errors

various MF is presented.

Forecasting results of rating system CAMEL.

Year and number of

rules

Table 19.

is presented.

Figure 2.

Figure 3.

96

The results of successful application of fuzzy methods for bankruptcy risk forecasting of Ukrainian banks under uncertainty stimulated the further investigations of these methods application for financial state analysis of European leading banks.

The main goal of this exploration was to investigate novel methods of European banks bankruptcy risk forecasting, which may work under uncertainty with incomplete and unreliable data.

Besides, the other goal of this investigation was to determine which factors (indicators) are to be used in forecasting models to obtain results close to real data. Therefore, we used a set of financial indicators (factors) of European banks according to the International accountant standard IFRS. The annual financial indicators of about 300 European banks were collected in 2004–2008, preceding the start of crisis of bank system in Europe in 2009. The data source is the information system Bloomberg [8]. The resulting sample included the reports only from the largest European banks as system Bloomberg contains the financial reports only from such banks. For correct utilization, input data were normalized in interval [0,1].
