=Paper=
{{Paper
|id=Vol-2649/paper4
|storemode=property
|title=Model Tools of Credit Risks Assessment of Agricultural Enterprises in International Trade
|pdfUrl=https://ceur-ws.org/Vol-2649/paper4.pdf
|volume=Vol-2649
|authors=Olga Nashchekina,Olena Sergienko,Igor Sosnov,Maryna Tatar,Evgeniy Shapran
}}
==Model Tools of Credit Risks Assessment of Agricultural Enterprises in International Trade==
https://ceur-ws.org/Vol-2649/paper4.pdf
34
Model Tools of Credit Risks Assessment of Agricultural
Enterprises in International Trade
Nashchekina Olga1 [0000-0003-2578-1109], Sergienko Olena1 [0000-0002-9796-9218],
Sosnov Igor1 [0000-0003-0027-5488], Tatar Maryna2 [0000-0002-1111-7103],
Shapran Evgeniy1 [0000-0002-9236-0905]
1
National Technical University «Kharkiv Polytechnic Institute», Ukraine
2
National Aerospase University «Kharkiv Aviation Institutе», Ukraine
onashchekina@gmail.com, serhelenka@gmail.com,
igor.i.sosnov@gmail.com,
marina.sergeevna.tatar@gmail.com, evgen.shapran1948@gmail.com
Abstract. The paper aim is improving the methodological tools for agricultural enterprise's
credit risks assessment and classification as participant of international trade market.
The proposed approach differs from the existing approaches by complexity and systematic-
ness, on the bases of usage of multilevel factor system of the borrower's assessment by local
and aggregate components. The object of research is set of credit risks affecting crediting pro-
cesses in agricultural sector of Ukrainian economy.
The following economic and mathematical methods of scientific research were used: factor
and comparative analysis (to highlight classification specific features of agricultural sector
crediting), methodology of integrated rating (for rating of local components of risk), methods of
factor analysis (to confirm the hypothesis of grouping of agricultural enterprises credit risks by
components), hierarchical and iterative methods of cluster analysis (to distinguish agricultural
enterprises classes by risk level).
The proposed methodology is tested on a sample set of observations for 14 agricultural en-
terprises of Ukraine for 2018 year for the selected 38 credit risk indicators.
On the bases of economic and mathematical tools for estimating and analyzing the aggre-
gate system of agricultural enterprises credit risk indicators, namely the methodology of factor
analysis, the hypothesis regarding the grouping and formation of agricultural enterprises credit
risk classes has been improved. Five major systemic groups of external and internal risks which
are specific to agrarian enterprises are identified: mortgage risks, system providing risks, sys-
tem forming risks, natural and climatic risks, production risks. The obtained classes by risk
level by the studied components on the basis of cluster analysis methods make possible to de-
termine the set of critical and safe states in general and by local components and to choose
effective behavior trajectory for agricultural enterprise creditworthiness increasing.
The assessment results using proposed methodology laid in the bases of scenarios devel-
opment of agricultural sector lending, make possible to develop a set of measures for strategic
and tactical management of agricultural enterprise's creditworthiness and adjust their behavior
in international markets.
Keywords: agricultural enterprises, credit risks, economic and mathematical tools, assess-
ment, classification, indicators, external environment, internal risks
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
35
I Introduction
Effective lending to the agrarian sector in modern conditions is one of the most
important tasks of the Ukrainian banking system. Lending to the agricultural sector in
our country remains one of the main problems that hinder agriculture development,
which is one of the main economic sectors. The problem of financial support for the
agricultural sector of the economy is the neglect of the specificity of agricultural pro-
duction and features of the agricultural sector of the economy during lending.
Special attention should be given to increasing the enterprises’ creditworthiness of
the branches which are budget-forming for Ukraine and which are focused on interna-
tional trade, in particular the agricultural sector enterprises. Since a lot of agricultural
enterprises are export-oriented and provide significant portion of foreign currency
earnings to the country the primary government tasks are realization of measures to
resume lending to the agricultural sector, as well as to find ways for credit mechanism
improvement in order to increase the volume of lending to agricultural sector
enterprises and reduce the lending risks in this sector.
One of the areas that take enterprises to substantially new level in relation to
competing companies is economic entities lending improvement, especially in the
agricultural sector of Ukraine economy, which is currently the leader of national
economy and significant subject of international trade market.
Specificity of lending to an agrarian sector of the economy has been the subject of
research in papers of such Ukrainian authors as L. Berezina, V. Gubenko, G.
Yevtushenko, N. Tymkov and A. Sheshenya, O. Kovalenko, O. Nepochatenko and V.
Yudin, M. Sayenko, I. Yatsiv, as well as foreign scientists S. Adidto, K. Gan and G.
Nartea, H. Aimin, J. Deunink, K. Kerels, L. Bass and D. Van Gizgegem, N.
Yankelova, D. Masar and S. Morykova, M. Janowicz-Lomott, K. Liskava, S.
Riethong, P. Schreinmacher, K. Groverman and T. Berger etc.
Berezina (2013) emphasizes that the results of this branch work of economy
depend substantially on factors beyond the control of the person, for example,
weather conditions, which not only significantly increases the riskiness of enterprises
activity, but also threatens macroeconomic stability, since the share of agroproducts in
state total export was more than 40%.
Hutorov, Lupenko, Yermolenko, Dorokhov (2018) define the features of the credit
facility, due to various factors influencing the activities of agricultural enterprises on
the basis of their classification according to classification factors, namely: natural-
climatic, economic, social, legal and institutional factors.
Gubenko (2010) carries out an analysis of the institutional principles of lending to
the agricultural sector which makes possible to determine the components of the
institutional environment of bank lending to economic entities in agricultural sector of
economy and their functional purpose.
The principles and functions of crediting of agrarian sector were specified in paper
Gubenko (2010). Among the functions of crediting the author emphasizes the role of
innovation-investment function, the implementation of which contributes to attracting
financial resources and accumulation of additional resources by credit institutions for
the agricultural sector of economy, which will eventually lead to increase in economic
performance of agricultural enterprises.
36
Yatsiv (2013) affirmed that agricultural risks are not independent, but rather
related to each other and as part of system that incorporates all available risk
management tools, strategies and policies.
Jankelova, Masar, Moricova (2017) explored approaches to risk management and
risk mitigation tools which are the most commonly used. They used a scale to assess
the importance of different strategies which influence companies' indicators.
At the same time, the issues of improving the directions of complex evaluation of
agribusinesses lending efficiency, as well as methodological approaches to the as-
sessment and risks aggregation of agricultural sector enterprises remain underdevel-
oped.
Thus, agricultural enterprises activity efficiency, as well as their creditworthiness
level as a resultant indicator of financial and economic activity effectiveness, largely
depends on the risks level, which is determined by a large number of internal and
external environmental factors, which are directly influenced on enterprises goals,
tactics and strategies and the negative effects of which are threatening to creditwor-
thiness loss.
Significance and logical incompleteness of the mentioned problems are deter-
mined their relevance. Therefore, in order to determine the leading directions of the
enterprises' creditworthiness management policy as an indicator of development level
in the future, it is necessary to improve the tool of external and internal risk assess-
ment, which provides:
– continuous monitoring of the financial situation, i.e. the potential assessment
(both internal and external) for neutralization and overall localization the financial
and economic crisis threat;
– defining measures for all risks types minimization to ensure appropriate credit-
worthiness level;
– evaluating the effectiveness of measures of localization and overcoming the
negative impact of various factors, as well as evaluating these measures, the timing of
their implementation and the results that can be expected.
II Methodology and Data
The main purpose of the system of agricultural enterprises creditworthiness ensur-
ing is achievement stable and maximally effective functioning in a non-stationary and
aggressive environment, taking into account the perspective dynamics of develop-
ment, which is achieved when solving the relevant tasks and implementation of func-
tions on the basis of proper organizational and legal support. For adequate assess-
ment, analysis, forecasting, and management of agricultural enterprise's creditworthi-
ness in terms of external and internal factors, the pressing issue is the usage of ap-
proaches, methods, and models existing nowadays, as well as their improvement
through aggregation and integrated usage.
One way for agricultural sector lending improvement is credit risk reduction as a
way of minimization of its negative impact (Bezrodna, Ivanova, Onyshchenko, Lyp-
chanskyi, Rymar (2019). On the bases of research results, we consider that agricultur-
al enterprise's credit risk is partial or total loss of income by the creditor due to the
37
default of the borrower, and the probability of collateral loss by debtor.
The following empirical hypotheses are are the basis of the research:
Hypothesis 1. The state of external and internal factors influences the uneven de-
velopment of economic entities in agricultural sector of economy and determines their
strengths and weaknesses of activity, risk stability in lending.
Hypothesis 2. The existence of systematic general and local classification groups
of external and internal credit risks specific to agraricultural enterprises. But there
may be differences specific to agrarian enterprises, with certain differences depending
on certain conditions and factors.
Hypothesis 3. The presence of critical and safe states of influence of credit risk
factors in general and by local components, the analysis of which makes possible to
choose an effective behavior trajectory in different risk conditions for agricultural
enterprise creditworthiness increasing.
Credit risk research involves the following tasks of assessing and classifying cred-
it risks:
1) formation of factors system for credit risk assessment;
2) integrated assessment of credit risks level by local components;
3) factor analysis of credit risk indicators, their classification, and aggregation;
4) classification of enterprises by the risk level according to the allocated aggre-
gate components.
For these problems solving, we propose a methodological approach with modern
economic and mathematical tools used for assessing and classifying agricultural en-
terprise's credit risks to develop adequate management measures for economic enti-
ties. The improved methodological approach containing four main steps for the im-
plementation of the selected research objectives concerning agricultural enterprise
credit risk assessment and analysis is presented in Fig. 1 and include the stages that
provide solutions tasks mentioned above.
During the research the following methods were used: scientific abstraction, logi-
cal generalization (in the analysis of categorical apparatus), analysis and synthesis
(for data structuring and agricultural enterprises risk assessment), induction and de-
duction (for research algorithm development), factor and comparative analysis (to
distinguish specific features of agrarian sector lending classification), methodology of
integrated rating assessment (for rating of risk local components), methods of factor
analysis (to confirm the hypothesis of grouping agroenterprises credit risks by com-
ponents), iterative and hierarchical cluster analysis methods (for agricultural enter-
prises classes selection by risk level).
The research information base is legislative and regulatory acts concerning agri-
cultural complex crediting, official statistical information of the National Bank of
Ukraine and the State Statistics Service of Ukraine, analytical reviews, financial re-
ports of enterprises available on the following information resources: National Bank
of Ukraine (https://bank.gov.ua), Association "Ukrainian Agrarian Business Club"
(http://www.ucab.ua/en), Ministry of Agrarian Policy and Food of Ukraine
(https://agro.me.gov.ua/ua), State Statistics Service of Ukraine
(http://www.ukrstat.gov.ua/), Stock Market Infrastructure Development Agency of
Ukraine (https://smida.gov.ua) and the authors’ calculations.
The sample set of observations for 14 Ukraine agricultural enterprises for 2018 for
38
38 credit risk indicators was checked for robustness and representativeness and the
following statistical limitations were taken into account:
– validity and expediency of the presence of indicators in the research;
– minimizing time spent and costs for obtaining information about indicator value
or its calculation;
– sufficient limited number of indicators necessary for economic interpretation of
research results without content loss;
– compliance of the indicators with the existing statistical reporting system.
III Results and analysis
Let us consider in more detail the essence of credit risk assessment and their clas-
sification for agricultural enterprises.
Stage. 1. Formation of factors system for credit risk assessment. For assess-
ment the borrower's creditworthiness, the main tasks are aggregation and analyzation
the set of credit risks that are important for creditworthiness monitoring from the per-
spective of the borrower (agricultural enterprise). Therefore, during the study of ex-
ternal and internal risks it is necessary to identify both positive and negative sides of
the factors influence on creditworthiness level and take into account the degree of its
impact on the activity. This fact is confirmed in papers Berezina, L. (2013), Martynchyk,
O. (2014), Sergienko, O., Tatar, M. (2015), Vitlinskyy, V., Velykoivanenko, G. (2004), Yatsiv,
I. (2013), Yepifanov, A., Vasylyeva, T., Kozmenko, S. (2012).
The basic principles that should be followed when research organizing are the
principles of objectivity, development, systematicity, flexibility, regularity and rele-
vance.
Therefore, when forming factor system of indicators for assessment, the following
features of the external and internal environment of agricultural enterprises existence
and their associated risks should be taken into account:
– agricultural enterprise external environment is a complex of factors, conditions,
legislation, communications systems, regulatory systems and common business prac-
tices, conditioned by the level of development of financial, economic, moral and legal
relations within which the enterprise interacts with clients, creditors, other counterpar-
ties, regulators and fiscal bodies (it is stated in paper Gubenko (2010));
– external environment state is characterized by instability, dynamism, integration,
uncertainty and sometimes aggressive influence on agricultural enterprises activity
(Klebanova, Kyzym, Chernyak (2010), Shtal, Buriak, Amirbekuly, Ukubassova,
Kaskin, Toiboldinova (2018));
– the main characteristics of agricultural enterprises external environment are en-
vironmental factors interrelation, complexity, uncertainty, mobility of external envi-
ronment (Smovzhenko, Azarenkova (2014));
– internal risks are factors that are directly generated by the enterprise itself or part
of its internal environment, such as financial resources availability level, unsatisfacto-
ry structure of assets and liabilities, personnel incompetence, equipment wear (Ser-
gienko, Tatar (2011);
– the influence of internal environment factors is unfavorable, so internal risks can
39
lead to creditworthiness and liquidity levels decrease, required reserves amount in-
crease, inefficient management decisions adoption, assets profitability decrease, large
number of production loss (Yatsiv (2013));
Stage 1. Formation of factors system for credit risk assessment
Result:
Task: Tools:
multilevel factor
- selection and aggregation of - analysis of the
system of credit risk
indicators for credit risk literature;
assessment of agricultural
assessment by local and - applied statistical
enterprises external and
aggregate components analysis;
internal environment
Stage 2. Integral assessment of risk level by local and aggregate risk
indicators
Task: Tools: Result:
- objective - integral integral
complex assessment of rating assessment by indicators by local
agricultural enterprises development level and aggregate risk
risk level indicator components
Stage 3. Confirmation of the hypothesis of classification and aggregation of
risks by local and aggregate groups
Result:
Task: Tools:
- substantiation - methods and improved risk
system by local and
and formation of models of factor
aggregate components
agricultural enterprises analysis and for agricultural
credit risk system principal components enterprises
Stage 4. Enterprises classification by aggregate risk indicators and risk degree
assessment
Task: Result:
Tools:
- formation of selection of agricultural
agricultural enterprises - hierarchical
and iterative methods enterprises classes by
classes by the risk level risk level: high,
using local aggregate of cluster analysis
medium, low
components
Fig. 1. The main stages of agricultural enterprises credit risk assessment and classification
– internal threats, as a rule, are conditioned by the presence of prerequisites for
negative, unlawful personnel actions, uncontrolled usage of production means and
40
violation of enterprise activity regime; internal credit risks are persistent and inde-
pendent of enterprise reputation, location, business value or the presence of external
threats (Levkina, Kravchuk, Sakhno, Kramarenko, Shevchenko (2019)).
Highlighted features make it necessary to assess and analyze the agricultural en-
terprises external environment as accurately as possible, as well as to determine the
nature and strength of possible risk actions for adequate management behavior in each
situation and to formulate an aggregate indicators system for borrower risks assessing.
The paper proposes the initial structure of the borrower's credit risk, which consists of
the following components:
– system risks: currency risk (R_vr), interest rate risk (R_рr), inflation risk (R_ir),
price risk (R_zr), government regulation risk (R_dr);
– mortgage risks: liquidity decrease risk (R_zl), risk of collateral loss (R_vz);
– natural and climatic risks: temperature fluctuations (R_tk), precipitation (R_о),
wind ((R_v);
– production risks: risk of crop or it part loss (R_vtv), risk of productivity decrease
(R_zрr), technological risk (R_tесh).
The developed indicators system for borrower risk assessing is presented in Tab.
1. It was compiled by the results of the analysis of scientific and methodological liter-
ature [14; 29; 30; 31], the selection of indicators was carried out on the basis of expert
evaluation.
Table 1. Aggregate indicators system for borrower risk assessing
№ Risk Indicators
System risks
1 Currency risk - exchange rate;
(R_vr): - number of currency contracts;
- export of goods and services;
- import of goods and services
2 Interest rate risk - banks interest rates on credits in national currency;
(R_рr): - interest rates of banks on deposits in national currency;
- National Bank of Ukraine discount rate
3 Inflation risk - inflation rate;
(R_ir): - industrial products producers price index;
- agricultural producers price index
4 Price risk - current transfers;
(R_zr): - taxes
5 Government regulation risk - the rate of increase / decrease in real wages;
(R_dr): - relations between economic and political structures;
- private capital protection;
- business legislation change
Mortgage risks
6 Liquidity decrease risk - liquidity ratio;
(R_zl): - solvency ratio;
- ratio of liquid assets to total assets;
41
7 Risk of collateral loss - ratio of inactive loans to total gross loans;
(R_vz): - failure to fulfill contractual obligations of counterparties
Natural and climatic risks
8 Temperature fluctuations - average temperature for seasons;
(R_tk): - deviation from the normal temperature regime
9 Precipitation - rainfall;
(R_о): - intensity of extreme weather events (storms, hurricanes, floods, droughts);
10 Wind - wind speed;
(R_v): - wind direction
Production risks
11 Risk of crop or it part loss - insurance contracts number;
(R_vtv): - deterioration of soil quality;
- unforeseen weather conditions;
- environmental pollution level;
- theft at work
12 Risk of productivity de- - labor productivity;
crease - staff turnover;
(R_zрr): - efficiency of wages;
- low-skilled workers usage
13 Technological risk (R_tесh): - fixed assets depreciation degree;
- investments in fixed assets
Stage 2. Integral assessment of risk level by local and aggregate risk in-
dicators.
For assessment of integral risk level by the selected components, the inte-
grate rating method on the bases of taxonomic indicator of development level was
used (this method was proposed by Z. Helwig and was presented in Azarenkova,
Belenkova (2011) and Ayvazyan, Bukhshtaber, Yenyukov (1989).
The integral index is normalized and varies from 0 to 1, which makes possible to
rank the investigated objects according to credit risks level. This indicator is easy to
interpret: its values close to 1 correspond to smaller values of the general credit risks
level, which positively affect the creditworthiness of the analyzed objects, and values
close to 0, to larger values of credit risk indicators, which negatively affect the cre-
ditworthiness level. The integral index calculation is proposed by Pluta (1980).
On the bases of calculations results, we can conclude that none of the surveyed en-
terprises can be uniquely classified and assigned to the corresponding risk group,
since for some components we have high risks level and for others – medium or low
risk level. However, it confirms the hypothesis of the strengths and weaknesses of the
agricultural enterprise and provides a list of opportunities and threats in the direction
of which the company should move to improve creditworthiness.
Stage 3. Confirmation of the hypothesis of classification and aggregation of
risks by local and aggregate groups.
For confirmation the hypothesis of the proposed classification and aggregation of
risks by local and aggregate groups according to the studied indicators on the bases of
literature sources analysis and improvement of evaluation for agricultural enterprises
42
in the paper we use the methodology of multivariate factor analysis and the principal
components method as a procedure for factor analysis of grouping of similar features
into a homogeneous set of factors. Plot of Eigenvalues calculated in Statistica for
determining the number of principal components of the local credit risk groups is
presented in Fig. 3.
Pl ot of Ei genval ues
4,0
3,5
3,0
2,5
Value
2,0
1,5
1,0
0,5
0,0
1 2 3 4 5 6 7 8 9 10 11 12 13
Number of Ei genval ues
Fig. 3. Plot of Eigenvalues for determining the number of principal compo-
nents of the local credit risk groups
The assessment results of informativeness level of the main factors by credit risks
(the value of eigenvalues and explanatory variance) is shown in Fig. 4. So we can see
that all five components have a root value greater than 1 (one) and overall explanatory
variance of 80,63%, which confirms the hypothesis of risk aggregation across the five
major groups.
Ei genval ues (S preadsheet47)
Extracti on: Pri nci pal com ponents
Ei genval ue % T otal Cum ul ati ve Cum ul ati ve
Val ue vari ance Ei genval ue %
1 3,548068 27,29283 3,54807 27,29283
2 2,396805 18,43696 5,94487 45,72979
3 1,909533 14,68871 7,85441 60,41851
4 1,368375 10,52596 9,22278 70,94447
5 1,258525 9,68096 10,48131 80,62543
Fig. 4. The assessment results of informativeness level of the main factors by
credit risks for agricultural enterprises
The values of the factor loadings of the initial characteristics (local components of
risk) with the selected 5 main factors are shown in Fig. 5.
The task of recognizing the principal components is explained by the desire for
simple structure of principal component, which is always easier to interpret. As a
result of factor analysis methodology implementation by the method of principal
components on the basis of the significance of factor loadings, the following aggre-
gated groups are distinguished by credit risks indicators:
43
Factor Loadi ngs (E quam ax raw) (Spreadsheet47)
Extracti on: Pri nci pal com ponents
(M arked l oa di ngs are >,500000)
Factor Factor Factor Factor Factor
Vari abl e 1 2 3 4 5
R_vr -0,153651 0,864394 0,472794 0,190939 -0,118283
R_pr -0,360618 0,041800 0,124535 0,700091 0,134883
R_i r 0,104455 0,102440 0,085457 0,815257 0,355441
R_zr 0,149147 -0,073014 -0,071324 -0,780434 0,472794
R_dr 0,069314 0,936192 -0,122858 0,131168 0,047974
R_zl -0,748146 0,234034 -0,311667 0,155869 0,032680
R_vz -0,694888 -0,462374 0,000116 -0,213859 0,145168
R_tk 0,150437 -0,097037 0,899128 -0,042760 -0,098495
R_o 0,198911 -0,014662 0,503888 0,135008 0,263095
R_v 0,269866 0,417122 0,610714 0,070768 -0,182060
R_vtv -0,417930 -0,110774 -0,268672 -0,242489 0,730845
R_zpr -0,085053 -0,022818 -0,050175 -0,018504 0,946020
R_tech 0,422042 0,338855 -0,447392 0,149036 -0,604374
Expl .Var 1,815983 2,138925 2,361373 2,739072 1,425953
Prp.T otl 0,139691 0,164533 0,181644 0,210698 0,109689
Fig. 5. The values of the factor loadings of the initial characteristics
for agricultural enterprises
1) F1 – mortgage risks: liquidity decrease risk (R_zl), risk of collateral loss (R_vz);
2) F2 – system providing risks: currency risk (R_vr), government regulation risk
(R_dr);
3) F3 – natural and climatic risks: temperature fluctuations (R_tk), precipitation
(R_о), wind (R_v);
4) F4 – system forming risks: interest rate risk (R_рr), inflation risk (R_ir), price
risk (R_zr);
5) F5 – production risks: risk of crop or it part loss (R_vtv), risk of productivity
decrease (R_zрr), technological risk (R_tесh).
The sample scatterplots of the principal factors in the two- and three-dimensional
factor spaces are presented in Fig. 6.
Confirmation of the importance of input indicators (Xj) which are involved in the
formation of main components names can be obtained by calculation when informa-
tiveness coefficient determining (Soshnikova, Tamashevich (1999):
a (W W )
2
jr 2 3
Ku j
m
a
j 1
2
jr
where: W1 – subset of insignificant weights; W2 – subset of significant weights;
W3 – subset of significant weights that do not participate in the formation of the main
components name; W2 –W3 – subset of significant weights involved in the name for-
mation; a 2jr – value of factor loading.
44
Scatterplot of F5 against F3 3D Scatterplot of F4 against F1 and F2
Spreadsheet1.sta 16v*14c Spreadsheet65 10v*13c
1,0 R_zpr
0,8 R_tech
R_vtv
R_ir
0,6 R_zr
R_pr
R_ir R_zr R_dr
R_vr
0,4 R_pr
R_o
F5
0,2
R_zl
R_zl
R_vz R_dr R_vr
0,0
R_tk
-0,2 R_v R_vz
-0,4
-0,6 -0,4 -0,2 0,0 0,2 0,4 0,6 0,8 1,0
F3
Fig. 6. The sample scatterplots of the principal factors in the two- and three-
dimensional factor spaces for agricultural enterprises
A set of explanatory aggregate principal components is considered satisfactory if
Ки values lie within the range of 0,75–0,95.
The results of factor analysis of the agricultural enterprises by the formed local
credit risk indicators and their components are presented in Tab. 2.
Table 2. Results of factor analysis of credit risk classes for agricultural enterprises
Components of Designation Designation of Informative- Name of local credit risk components
aggregate credit of factors / local risk ness coeffi- / (factor loading)
risk classes (variance groups cient
percentage)
System providing F2 (R_vr) 0,87 currency risk (0,86)
risks (18,44 %) (R_dr) government regulation risk (0,93)
F4 (R_рr) 0,79 interest rate risk (0,7)
System forming
(10,53 %) (R_ir) inflation risk (0,82)
risks
(R_zr) price risk (0,78)
F1 (R_zl) 0,8 liquidity decrease risk (0,75
Mortgage risks
(37,29 %) (R_vz) risk of collateral loss (0,69)
F3 (R_tk) 0,78 temperature fluctuations (0,9)
Natural and (14,69 %)
climatic risks (R_о) precipitation (0,5)
(R_v) wind (0,61)
F5 (R_vtv) 0,81 risk of crop or it part loss (0,73)
Production risks (9,68 %) (R_zрr) risk of productivity decrease (0,95)
(R_tесh) technological risk (0,60)
45
Thus, the implemented methodology of factor analysis makes possible to improve
the system of risk classification and to identify two significant subgroups of risks in
the group of systemic risks by initial classification and to take this into account in
further research, because this is agricultural enterprises feature.
On the bases of obtained results, it can be concluded that agricultural enterprises
are characterized by rather large deviations from the mean values and significant dis-
parities in the aggregated risk groups, which gives the basis for further classification
and formation of enterprises classes with more homogeneous states for the develop-
ment of more acceptable and specific solutions for each situation. The graph of the
main statistical characteristics (average, minimum and maximum values) for 14 agri-
cultural enterprises by local and aggregate risk indicators is presented in Fig. 7.
MEAN MEAN
MIN MIN
MAX MAX
Fig. 7. Graph of the main statistical characteristics by local and aggregate
risks indicators for agricultural enterprises
Risk grouping by variation and mean for the studied enterprises and variation
by aggregate risk groups is shown in Fig. 8.
The obtained distribution shows that set by both local components in the
groups and aggregate risk factors are not sufficiently homogeneous. The variation
coefficient is less than 33% for systemic risk factors (currency and government regu-
lation risks) and natural and climatic risk factors; the most heterogeneous are mort-
gage risks, which reflect the different credit status of agricultural enterprises.
Stage 4. Enterprises classification by aggregate risk indicators and risk
degree assessment.
As the results of the previous research it was revealed that agricultural enter-
prises are very different from each other in terms of the risks in each specific area, the
cluster analysis was used for objectification the results and formation of enterprises
classes which are characterized by common risks (Soshnikova, Tamashevich (1999).
46
VARIATION
VARIATION
Mean
Fig. 8. Credit risk grouping by variation and mean value and by variation and
aggregate risk groups for agricultural enterprises
The enterprises classification by credit risk level on the basis of cluster anal-
ysis methods is presented in Tab. 3. It shows enterprises distribution into three
groups:
L – low risk level, M – medium risk level, H – high risk level.
Table 3. Classification of agricultural enterprises by aggregate components
of credit risk on the bases of cluster analysis methods
№ Name of agricultural enterprise R1 R2 R3 R4 R5
1 PJSC "Gunivskaya Agro Firm" M L M M H
2 PJSC "Agro Firm "Verbivske" H L L H M
3 PJSC «Оhoche» H M H M L
4 PJSC "Agro Firm “Provesyn”» H M H H L
5 PJSC "Agro Firm “Rosia”» L H M H H
6 OJSC Malovyskivska Agro Firm "Agrotech- L L H L H
service"
7 PJSC "Agro Firm named after G. S. Skovoroda H M L H M
8 JSC "Ukraina" H H L H L
9 PJSC "Agro Firm “Yatran” M M L L M
10 OJSC "Agro Firm “Globyvska” L M M M H
11 OJSC "Agro Firm “Zorya Novobuzya” H H M H H
12 CJSC «Kolos» M L L H L
13 CJSC «Agro Firm “Sumy-Nasinna” H L H L H
14 CJSC 14 «Tsukrove» L M M H L
According to the implemented methodology, the main characteristics of the inter-
nal and external environment of agricultural enterprises are identified, which form
certain risks of creditworthiness loss. The risks system is formed on the bases of ex-
ternal and internal environment factors affecting the creditworthiness level, among
47
which is a group of systemic risks, and a group of natural and climatic risks, which is
the basis for assessing the external competitive environment, economic, natural and
climatic situation in the country as a whole. Also, mortgage risks and production risk
groups are selected, using which comparative analysis makes possible to identify
internal competitive advantages and risk prevention options for each company.
IV Conclusions
On the bases of economic and mathematical tools for assessment and analysis of
the aggregate system of agricultural enterprises credit risk indicators are obtained
such results:
1) local and aggregate integrated indicators of internal and external environmental
risks were calculated using integrated rating assessment;
2) their main statistical characteristics were determined and investigated.
3) five main systemic groups of external and internal risks, which are specific to
Ukraine agricultural nowadays, are selected:
– mortgage risks: liquidity decrease risk, risk of collateral loss;
– system providing risks: currency risk, government regulation risk;
– system forming risks: interest rate, inflation and price risks;
– natural and climatic risks: temperature fluctuations, precipitation, wind;
– production risks: risk of crop or it part loss, risk of productivity decrease and
technological risk.
4) the hypothesis of grouping and formation of credit risk classes for agricultural
enterprises is improved on the bases of integral and factor analysis methodology;
5) using methods of cluster analysis the classes of risk levels such as low, medi-
um, high were identified for each group of risks.
So estimation of classes of agricultural enterprises by the risk level by the studied
components makes it possible to determine the set of critical and safe states in general
and by local components and to choose an effective trajectory of behavior for agricul-
tural enterprise creditworthiness increasing and competitiveness ensuring in global
competitive markets.
The results implementation of credit risk assessment, analysis and classification
make possible to increase the management decision validity and agricultural enter-
prises efficiency and to improve the policy of credit activity in the agricultural sector
of the economy.
The proposed methodological tools for agricultural enterprise's credit risks as-
sessment and classification can be applied in modified version in other countries tak-
ing into account the specifics of these countries, their level of development, banking
system development level, competition level in the agricultural sector in these coun-
tries, etc.
The study can be expanded by identification the specific risks inherent in each en-
tity, as well as the country and at the international level. The main areas of further
research are creation of mechanism for crediting agricultural enterprises during inter-
national trade, which provides a set of measures by the state and banking system
aimed at developing an effective integration mechanism for interaction with the agri-
48
cultural sector; increasing the volume of financial support for agricultural enterprises
through the mechanism of cheaper loans; development and use of all possible sources
for raising capital in lending to agriculture sector.
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