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size of Russian banks. Their weak financial capacity explains why the real sector turned to foreign funding and the level of debts to non-residents is currently comparable to domestic corporate lending.
However, a large group of local banks numbering several hundreds of organizations will not be able to augment equity capital neither to a level corresponding to the financial needs of the leading Russian companies nor to the level designated by the Government of the RF. At the same time this group of banks could help to reduce the impact of the above-mentioned negative effects of consolidation. The best way to save these banks is their isolation in a separate class that will be under special prudential supervision depending on the risk profile.
In most countries where the banking sector has hundreds of players, there are several categories of banks to which different requirements, depending on specificity of activities, are applied. Taking into consideration the available international experience, it is necessary not to force small banks to close or consolidate and let them choose their niche and continue to work in it.
As a result of this offer, multilevel banking system will be created in Russia. The Bank of Russia will be the first level of the national banking system, the second level will be federal banks with general license and a large capital of their own (e. g. from 100 million Euros). They will carry out the whole range of banking operations, operate throughout the country, and have access to foreign financial markets. The third level of the system will be represented by separate groups of banks working at the level of federal districts, federal subjects and cities. Their licenses will include restrictions on the minimum equity capital, the territory of operation (on which the
bank may open branches) and the list of banking operations.
In conclusion, it should be noted that, without structural reforms, it is difficult or almost impossible for the Russian financial sector to assist sustained development of the real economy and to resist external shocks. Only the appropriate modernization of the banking system of Russia is able to assist a more sustainable economic growth and strengthening of the competitive position of Russia in the global economy.
References
1. Sarkisyants A. Problems of Banking Supervision // Accounting and Banks. 2008. № 6 [Electronic resource]. URL: www .bankir.ru/publication/regulation/1379862.
2. Kreyndel V. Who will measure the systemic risk? // Institute for Financial Studies. 31.07.2009. [Electronic resource]. URL: www.ifs.ru/upload/systemicrisk_31july.pdf.
3. Vernikov A. Share of state interest in the banking system of Russia // Cash and credit. 2009. № 11.
4. The transition process and indicators of CIS countries and Mongolia in 2009. The transition process in the crisis : Report of the Economic Department of the EBRD under the general direction of E. Berglof. [Electronic resource]. URL: www.ebrd.com.
5. Moiseyev S. Prospects of privatization in the banking sector // Analytical Banking Magazine. 2010. № 1 (176).
6. State and banks: risks cycling // Help to business. 23.06.09. [Electronic resource]. URL: www.bishelp.ru / gde_dengi / detail.php? ID = 80372.
© Parkhomenko A. A., 2010
D. V. Parshukov, G. V. Mironov Krasnoyarsk State Agrarian University, Russia, Krasnoyarsk
A MODEL TO ASSESS THE RISK OF BANKRUPTCY FOR AGRICULTURAL FIRMS IN KRASNOYARSK REGION
In this paper we report on the algorithm of development of a bankruptcy risk assessment model to be applied to agricultural firms of Krasnoyarsk region, which involves factorial and discriminant analysis of relevant data.
Keywords: factors, discriminant functions, tree-like hierarchy, aggregation, membership functions.
The global financial crisis and as a consequence the instability in financial markets have caused a drastic increase in the number of firms going out of business on the background of the overall economic downturn. In this context, an early recognition of pending problems is important for ensuring continuity of one’s business. In connection to this there is a necessity to work out an effective model to assess the risk of bankruptcy, which would allow to predict potential distress situations in Russian companies. The purpose of the present work is to construct such a model of bankruptcy risk assessment for agricultural firms of Krasnoyarsk region.
The structure of the model consists of a number of consecutive steps:
Step 1. To select a set of significant financial ratios for further analysis, to define classes of financial condition, put together linguistic characteristics.
Step 2. To reduce the dimensionality of the selected set of factors by applying the method of principal component analysis and to construct factors hierarchy.
Step 3. To derive discriminant functions for the principal components having been identified in the second step mentioned above.
Step 4. To produce an aggregate matrix for level recognizing on a standard 01-qualifier.
Step 5. To perform hierarchy nodes convolution and assign the firm to one of the classes defined in Step 1 mentioned above.
During the first step, we define three groups of firms as follows: financially sound (Class 3), financially unstable (Class 2), and financially distressed (Class 1) firms. Next we create a set of data containing twenty three various financial indicators, such as profitability ratios, solvency indicators, and business activity indicators, which describe different aspects of financial standing of the agricultural firms [1]. The model was constructed according to data for 2006, 2007. Model check was made according to data for 2008.
Let us define the so-called linguistic variable, “Factor level” [2], with the term set of L to have the form:
L = {Low level (L), Average level (A),
High level (H)}, (1)
Next we introduce F0 as a criterion of financial soundness and solvency. The linguistic variable provides a qualitative description of the firm’s condition related to F0. We set the following meaning for the linguistic characteristics:
- low F0 means the firm is in a critical financial condition and falls into Class 1 enterprises;
- average F0 means the firm is financially unstable and fits into Class 2 enterprises;
- high F0 means the enterprise is financially sound and qualifies for Class 1 enterprises.
If the company relates to financially distressed enterprises it indicates a high risk for bankruptcy. Likewise. Financially unstable firms have an average risk for default and financially sound enterprises indicate a low risk for bankruptcy.
The second step is to identify the factors based on factor analysis (after a preliminary analysis for multicollinearity). These factors give the largest contribution into dispersion of the resultant indicator F0, which describes a probability of bankruptcy through linguistic characteristics. The algorithm of factor analysis can be found in [2].
The following financial ratios are incorporated in the factor analysis:
Factor 1 - k1 - inventory coverage ratio; k2 -circulating assets in fill rate; k3 - economic efficiency of operating assets; k4 - a part of working capital in circulating assets; k5 - a part of fill rate in operating assets.
The first factor includes indicators showing how effectively the firm manages its assets and inventories. Current assets coverage indicator can be adduced as a short characteristic; k7 - financial dependence index; k8 -equity flexibility ratio; k9 - payback term for equity of the investment.
Factor 2 includes indicators which characterize the use of the firm equity capital.
Factor 3 - k10 - equity capital ratio; k11 - loan capital ratio, k12 - return of assets pricing; k13 - common production profitability.
The third factor measures profitability of the firm derived from equity and loan capital indicators. It denotes a profitability factor.
Factor 4 - k14 - cash ratio; k15 - quick ratio; k16 -current ratio; and k17 - return on production assets.
The factor incorporates indicators of liquidity and the earning capacity of the production, therefore it can be called a solvency indicator.
To define the significance of the factors, let us consider tab. 1.
Table 1
Total Dispersion
Factors Initial Eigenvalues
Total Variance (n), % Cumulative, %
1 6.676 29.026 29.026
2 4.006 17.417 46.444
3 3.178 13.818 60.261
4 1.833 7.967 68.229
Let the sign «^» denotes the indifference between two factors, i.e. the two factors are equally significant, and the sign «\» denotes the preference of one factor to another,
i.e. one of the factors is more significant than the other for the root element of the hierarchy (F0). The set of symbols and factors forms a system of relative preference. In this case this system looks as follows:
® = {F1 F - F3 \ F4}. (2)
It is based on the results of factor analysis. Relative contribution of individual factors to the total dispersion of characteristics [3] is compared:
Fi } Fi+1 if n > ni+1 is more than on 10 % and Fi ~ Fi+1 if ni> ni+1 is less than 10 %,
where n is the percentage of dispersion due to a particular individual factor (i. e. the contribution of that factor to the total dispersion), the subscript i denoting the factor number (thus n1 is the percentage of dispersion due to the first factor).
Let us build a tree-like hierarchy of the given factors (fig. 1).
Fig. 1. Tree-like hierarchy of factors
In our case being based on system (2) and the Fishburn method [3] we have the following weights 3 111
(©,7,7,®) for factors F1, F2, F3, F4, accordingly.
8 4 4 8
Now we have to categorize the firm belonging to classes mentioned above using each factor
(Fj, F2, F3, F4). We attempt to assign the firm to one of the three groups of financial viability by using each factor, based on the indicators selected as described above. This will be the third step in developing the model. Using the indicators determining each factor we reciprocate the functions with the best predictive ability. In this work it is a linear discriminant function. To reciprocate this function (3)-(6), we perform a discriminant analysis (tab. 2). In accordance with its results we have the following functions:
F =-0.14 • k1 -1.055 • k2 + 0.441-k3 + + 1.534 • k4 -1.667 • k5 + 2.462,
F2 =-0.713 • k6 + 0.738 • k 7 -
- 0.88 • k8 +1.658 • k9 - 0.08,
(3)
(4)
F3 =-0.063 • k10 - 0.139 • k11 + + 0.912 • k12 + 2.044 • k13 + 0.802, F4 = 0.071 • k14 - 0.008 • k15 +
+ 0.462 • k16 + 3.339 • k17-1.014.
(5)
(6)
According to the correlation index (> 0.5 for all factors) we can that correlation is satisfactory.
Significance p is less than 0.001 for all functions, which implies that the mean values of each of the functions are significantly different for various classes. High eigenvalues (more than one) indicate a good (appropriate) choice of discriminant functions (tab. 3).
Let us calculate the average value between the centroids for the functions of factors and we can establish the intervals of the firms belonging to each class (tab. 4).
Table 2
Statistical calculations for discriminant functions
Function Eigen value Variance, % Cumulative, % Canonical Correlation Wilks’ Lambda Chi-square Sig. p
F\ 1.431 94.8 94.8 0.767 0.382 32.758 .000
F2 3.316 95.6 95.6 0.877 0.201 55.403 .000
F3 2.022 89.9 89.9 0.818 0.269 45.249 .000
F4 3.463 96.7 96.7 0.881 0.200 55.467 .000
Table 3
Group Centroids Functions
Group Function in Group Centroids
F1 f2 F3 F4
Class 3 1.409 2.324 1.856 2.641
Class 2 .333 -2.211 .014 -.725
Class 1 -1.286 -.223 -1.402 -1.482
Table 4
Intervals of belonging in accordance with discriminant functions of factors
Value range Parameter level Risk of bankruptcy by each of the factors
For Fi:
F1 < -0.4765 Class 1 High
-0.4765 < F < 0.871 Class 2 Average
0.871 < F1 Class 3 Low
For F2:
-1.217 < F2 < 1.0505 Class 1 High
F2 <-1.217 Class 2 Average
1.0505 < F2 Class 3 Low
For F3:
F3 < -0.694 Class 1 High
-0.694 < F3 < 0.935 Class 2 Average
0.935 < F3 Class 3 Low
For F4:
F4 <-1.1035 Class 1 High
-1,1035 < F4 < 0,958 Class 2 Average
0.958 < F Class 3 Low
Having established the intervals of firm belonging to each class and having calculated the functions for their quantitative estimation we can make a convolution using the hierarchy stages. For the linguistic variable “Factor level” with the L term-set given by (1) and the hierarchy of factors we use the conventional three-level 01-classifier (SFC) [3] which acts as a group of functions of the firm class belonging, where these functions are trapezoidal triangular numbers (fig. 2):
|Ui( x) =
|U2 (x) =
^3(x)=
'1.0 < x < 0.2
5(0.4 - x),0.2 < x < 0.4 (7)
1.0.4 < x < 1 0,0 < x < 0,2
5(x - 0.2), 0.2 < x < 0.4
1.0.4 < x < 0.6 (8)
5(0.8-x), 0.6 < x < 0.8
0,0.8 < x < 1
0,0 < x < 0,6
5(x - 0.6), 0.6 < x < 0.8 (9)
1,0.8 < x < 1
Let it be that F0 = x, and x = a 01-carrier in (6) (the [0,1] segment of real line).
Fig. 2. System of trapezoidal belonging functions on the 01-carrier
The standard classifier projects the fuzzy linguistic variable onto the 01-carrier, and does so in a consistent manner, producing a pattern of symmetrically distributed classification stages (0.1; 0.5; 0.9). In these stages, the value of one particular membership function is equal to 1 (one) while all other functions are zero. The analyst’s uncertainty about correctness of classification decreases or increases linearly with the distance from the stages, the sum of membership functions being equal to 1 (one) in all points across the carrier.
We find F0 by means of matrix convolution:
F, = PMV = ( p2... pn)
Y11 Y12 Y13
A ' 0.1 A
0.5 , (10)
0. 9 V
f the factors
(3,1,1,1); 8 4 4 8
f 0.1 A 0.5 0.9
is the vector of vertices of
trapezoidal numbers SFC; M is the belonging matrix, y is the belonging of the firm to one of the classes by means of each particular factor (3)-(6). For example if by F1 (3) the enterprise belongs to Class 1, the element y11 is equal to 1 while the rest of the elements in the first line are zero. The same principle applies to other lines of the matrix.
Let us compare the estimated value with the tabular data and evaluate a probability of bankruptcy in terms of linguistic characteristic (tab. 5).
Table 5
SFC-based classification of the level of FA
F0 limit Parameter level Degree of evaluation certainty (membership function), magnitude -100 %
0 < F0 < 0.2 Low 1
0.2 < F0 < 0.4 Low ^ = 5 • (0.4 - F0)
Average 1 -^1 =
0.4 < F0 < 0.6 Average 1
0.6 < F0 < 0.8 Average = 5 • (0.8 - F0)
High 1 -^2 =^3
0.8 < F0 < 1 High 1
The final result is a linguistic description of the degree of probability of bankruptcy as well as the degree of the analyst’s certainty as to the correctness of recognition. Therefore the conclusion about the risk degree not only has a linguistic form but also contains a characteristic of the assertions quality.
All in all we have finished the development of the model for assessing the risk of firm default. Let us illustrate the model by evaluating the factors for the agricultural firm GPKK “Borodinskoye”, Rybinsky Region. While this company was included into the group of financially unstable enterprises, its data were not used in the developing of the model. In our calculations we use formula (2) and the data from tab. 6.
According to (3)—(6) we have: F1 = -0,5 - Class 1; F2 = -1,6 - Class 2; F3 = -0,22 - Class 2; F4 = -0,32 -Class 2;
= 0.35.
According to tab. 5 the company belongs to Class 1 with the probability 5 (0.4-0.35) = 0,25 and to Class 2 with the probability 1-0.25 = 0.75. The company does not have sufficient coverage for floating assets and fill rate (by factor F1 = -. 05 it falls into Class 1), the company’s equity is inefficient, and it exhibits low profitability and solvency indicators (by factors F1, F3, and F4 it belongs to Class 2). The risk of bankruptcy in the nearest perspective is assessed as average.
O O i-H
' 0.11
1 1 11 0 M 0
0.5
4 4 8 ) 0 1 0
/ 0.9
, 0 1 0 v \ y
Table 6
Balance Sheet Data
Item Thousand roubles Item Thousand roubles Item Thousand roubles Item Thousand roubles
Earnings 16 028 Inventory 12 552 Balance value 36 937 Long-term liabilities 2 023
Cost price 17 686 Long-term receivables 1 967 Charter capital 100 Loans and credits 6298
Profit before tax -1 658 Short-term receivables 514 Added capital 45 606 Accounts payable 36 110
Fixed assets 20 132 Cash 100 Net profit -53 200 Current liabilities 42 408
Non- circulating assets 20 132 Floating assets 16 508 Capital and reserves -7 494 Liabilities 36 937
Based on financial information provided by Agricultural Agency of Krasnoyarsk region, we have calculated the value of F0 for ten different agricultural firms in each of the classes and compared the results with their initial classification. Full matching (i. e. when probability = 1) of our findings and conclusions with the original classification was 82.5 %. We observed any case when after the analysis a company originally classified as a financially distressed class 1was transferred to Class 3 (financially stable firms). It proves that the model is adequate and appropriate for assessing the risk of bankruptcy.
In addition to conventional methods, the proposed model of bankruptcy risk assessment can be an effective tool in evaluating financial position of a company, that can enable company’s management to continuously monitor the financial situation in the company for the risk of default. It is never late to mitigate the risks with the development of a package of measures particular important in the unstable conditions of economic environment [4-6].
References
1. Gilyarovskaya L. T. Economic analysis : Textbook for high schools / ed. by L. T. Gilyarovskaya. M. : UNITI-Dana, 2001.
2. Dubrov A. M., Troshin L. I., Mhitanyan V. S. Multivariate statistic methods : Textbook. M. : Finance and statistics, 2000.
3. Nedosekin A. O. Integrated assessment of the corporate bankruptcy risk the basis of fuzzy descriptions. SPb. : Sesam, 2002 [Electronic resource]. URL: http://sedok.narod.ru/sc_group.html.
4. Davydova G. V, Belikov A. Yu. A quantitave method to assess the bankruptcy risk of enterprises // Risk management. 1999. № 3. P. 13-20
5. Loyko V. I., Yefanova N. V. Quantitative models and techniques of assessment of risks in the agroindustrial integrated systems // The scientific journal of KSU. 2008. № 40 (6) [Electronic resource]. URL: http://ej.kubagro.ru/2008/06/pdf/12.pdf.
6. Fuzzy Logic: An Overview of the Latest Control Methodology. Application Report. SPRA028. Digital Signal Processing Products. 2000.
© Parshukov D. V., Mironov G. V., 2010
O. V. Patsuk
Siberian State Aerospace University named after academician M. F. Reshetnev, Russia, Krasnoyarsk DESIGN OF THE REGIONS’ ECONOMIC DEVELOPMET STRATEGY
At present Russian economy is undergoing the similar trends of the modern economic development to those taking place in developed countries, those are globalization, advanced development of the service industries, post-industrial society formation, intellectual component expansion in the outcomes of any industry, informatization of the society, exhaustion of traditional sources of social and economic growth. In such conditions search for new ways and factors of the regional self-development is critical.
Keywords: social and economic development of the territories, regional economy.
In recent years regions in Russia are becoming more ones, which are in the first place methods of regional
independent. They are more responsible now for the management. Economic reforms have shown that regions,
results of their economic development. Their social and using advanced methods of management are less
economic progress is determined by the objective factors influenced by crisis tendencies.
(macroeconomic conditions, region’s position in the Strategy of the state regional development is not social division of labor, production structure, uniform in different regions. This is caused by their
geographical location, natural resources) and subjective significant differences in natural resources, economic
