Experiment No. 8:

Input data—the financial indicators (taken from banks financial accountant

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

general liquidity coefficient (liquid assets + defended capital + capitals in

coefficient of profit fund capitalization (own capital/charter fund).

It is worth to note that these financial indicators are also used as input data in Kromonov's method of banks bankruptcy [5–7], whose results are presented below.

Training sample—120 Ukrainian banks and test sample—70 banks.

ROE—return on entity (financial results/entity); ROA—return on assets (financial results/assets); CIN—incomes-expenses ratio (income/expense);

NIM—net percentage margin; and

Total number of errors 12 % of errors 17 First type of errors 5 Second type of errors 7

Input data—following financial indicators (other than in experiments 5 and 6):

The results of application of FNN TSK for forecasting with these input indicators

It should be noted that these indicators are used as input in the method of Euro

general reliability factor (own capital/assets); instant liquidity factor (liquid assets/liabilities); cross coefficient (total liabilities/working assets);

reserve fund/total liabilities); and

The results for FNN TSK are presented in Table 6.

Total number of errors 7 % of errors 10 First type of errors 1 Second type of errors 6

reports):

Experiment No. 7:

Results of FNN TSK forecasting.

Results

Table 6.

Number of rules = 5.

NI—net income.

are presented in Table 7.

Results of FNN TSK forecasting.

Money [1].

Results:

Table 7.

90

Training sample—120 Ukrainian banks and test sample—70 banks.

Number of rules = 5.

Input data—financial indicators (banks financial accountant reports):

general reliability factor (own capital/assets);

instant liquidity factor (liquid assets/liabilities);

cross coefficient (total liabilities/working assets);

general liquidity coefficient (liquid assets + defended capital + capitals in reserve fund/total liabilities);

coefficient of profit fund capitalization (own capital/charter fund); and

coefficient entity security (secured entity/own entity).

The results of FNN TSK application with these financial indicators are presented in Table 8.

The comparative analysis of forecasting results using different sets of financial indicators are presented in Table 9.

Next experiment was aimed on finding the influence of data collection period on the forecasting results. It was suggested to consider two periods: January of 2008 (about 1.5 year before the crisis) and July of 2009 (just before the start of crisis).

#### Experiment No. 9:

Training sample—120 Ukrainian banks and test sample—70 banks.

Number of rules = 10.


#### Table 8.

Results of FNN TSK forecasting.


#### Table 9.

The dependence of forecasting accuracy on sets of input financial indices.


Table 10.

Accuracy of forecasting in dependence on data collection period.

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

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

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

% of errors

ANFIS 7 10 1 6 TSK 5 7 0 5 GMDH 6 8.5 1 5 Kromonov's method 10 15 5 5 BBA method 10 15 2 8

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

First type of errors

Second type of errors

3.3 Application of rating system CAMEL for assessment of financial state of

rating estimates determination by rating system 'CAMELS'."

The most widely used approach of banks financial state analysis and bankruptcy risk forecasting is based on the application of rating systems. The determination of bank rating is one of the methods that enables to obtain complex financial assessment of bank financial state and compare them. There are various private and official banks rating systems. The most known of them are systems developed by world leaders in this sphere-rating companies Fitch, Standard & Poor's, Moody's, etc. Officially recognized banks rating system that is widely used in the world is system CAMELS. It's American rating system was developed and implemented by Federal reserve System (FRS) and Federal Deposit Insurance Corporation (FDIC) in 1978 [1]. Supervision over banks activity based on risk estimation by system CAMELS lies in determination of general bank state using the common criteria that defines all aspects and spheres of bank activity. This system is also widely used in Ukraine by National Bank of Ukraine (NBU) according to developed "Statement of order of

Rating system CAMELS allows NBU to estimate general financial state and stability of banking system of Ukraine. Such assessment enables to obtain information for priority determination in banking supervision activity and necessary materials and financial resources for performing adequate control over banking system. At the same time, system CAMELS envisages the detail supervision and analysis

of bank state. Such analysis may be performed only while complex inspecting checking of bank activity, which enables to determine how the top managers ana-

The base of rating system, CAMELS, is risk assessment and determination of rating estimates by each component of the system: capital adequacy, assets quality,

Due to rating system, each bank obtain digital rating by all six components, and integral (complex) rating estimate is determined on the base of rating estimates of all components. Components of rating system are estimated by 5 balls scale in which estimate 1 is the highest, and estimate 5 is the lowest one. Integral estimate is also determined by 5 balls scale. Banks that obtained integral rating estimate 1 or 2 are considered reliable by all the factors capable to overcome economic depression and

Banks that got integral estimate 3 have substantial drawbacks, which may lead to serious problems with liquidity and solvency if these drawbacks won't be corrected

presented in Table 12.

Table 12.

Method/period Total amount of

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

Comparative results analysis of various forecasting methods.

errors

Ukrainian banks

lyze and control bank risks.

93

management, liquidity, and sensitivity.

its management believed to be qualified.
