Volume 11 - Issue 57
/ September 2022
189
https://www.amazoniainvestiga.info ISSN 2322- 6307
DOI: https://doi.org/10.34069/AI/2022.57.08.20
How to Cite:
Baboyan, K. (2022). New solutions for assessing insolvency risk in comercial organizations. Amazonia Investiga, 11(57), 189-197.
https://doi.org/10.34069/AI/2022.57.08.20
New solutions for assessing insolvency risk in comercial organizations
Новые решения для оценки риска неплатежеспособности в коммерческих
организациях
Received: October 1, 2022 Accepted: November 2, 2022
Written by:
Khachatur Baboyan63
https://orcid.org/0000-0002-3672-732X
Abstract
In the context of continuous crises that have
occurred in the last decade, effective solutions to
reduce risks and introduce effective controls into
the financial management process in commercial
organizations are extremely important. In order
to give a new impetus to sustainable economic
growth, it is necessary to prioritize the
implementation of large-scale reforms, the
difficult fiscal situation in a large number of
countries, the problems associated with a
decrease in the solvency of commercial
organizations in the real sector of the economy
also require a comprehensive regulatory solution.
The main purpose of this article is to offer
solutions for controlling financial risks, in
particular, for predicting the risk of insolvency,
in the context of new ideas of financial
management. Alternative methods for assessing
the financial condition of commercial
organizations, which also include the assessment
of solvency, are based on more complex
calculations, algorithms and the principle of joint
application of a number of methods. From this
point of view, a number of researchers in modern
conditions prefer cluster analysis. A new
approach to assessing and predicting insolvency
risks, proposed as a scientific innovation,
provides an opportunity to implement new
progressive ideas of financial management in
commercial organizations.
Keywords: risk management, bankruptcy risk,
business risks, financial indicators, solvency,
model, forecast, range, variable.
63
Candidate of Economics, PhD at the Institute of Economics after M. Qotanyan of the National Academy of Sciences of the
Republic of Armenia, Yerevan, Armenia.
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Introduction
In the period of 2010-2020, the development of
financial management, from the point of view of
handling the problems faced, is characterized by
the development of information technologies and
the use of new innovative approaches related to
it. In this phase, methods of expert analysis of
asset price changes were created. At the same
time, the new financial technologies in the digital
economy, such as blockchain (MacKendrick,
2016), cryptocurrencies (Korechkov &
Tselishchev, 2017), which are considered as a
type of digital currency, began to be widely used.
Without undermining the significant advantages
of the financial technology of the digital
economy, it should not be ignored the fact that,
in particular, the intangible nature of
cryptocurrencies leads to significant deviations
in their value assessment, which can lead to a
number of problems in the assessment of the
value of Internet organizations. We do not also
exclude the fact that at a certain stage of
development of the world economy, the
inadequate overvaluation of digital currencies
may cause a new global financial crisis.
According to predictions (Tebekin, 2019),
handling of the global economic crisis caused by
2020-2021 COVID-19 will be accompanied by
an increase in the role of human capital in the
economy. Therefore, during the financial
management processes, improvement in the
accounting tools of the value of the human
capital and increase in the efficiency of capital
management within the framework of capital
management of the organization are envisaged.
Currently, in the conditions of the crisis caused
by the Russian-Ukrainian war, finding new
solutions for sustainable development based on
financial risk management and corporate social
responsibility is considered one of the key
priorities of the financial management
philosophy.
Manifestations of specific types of risks are
related to time and probability. The major
criticism of quantitative risks by the high-level
specialists is that the scope of risk results is not
represented by probability distribution. (Ashley,
2020)
In the conditions of market economy, solvency is
one of the most important standards for
strengthening relations between organizations
connected with each other by economic ties.
According to different approaches to solvency
assessment, it is interpreted as the ability of a
business to pay off its current liabilities on time
with the liquid current assets.
An insolvent organization is attractive to neither
suppliers nor investors, as it creates a threat of
losing both its resources and the resources
involved. Effective management of
organization's solvency enables to quickly
address the problem of survival in a competitive
environment, and furthermore, to be able to
receive and pay off the borrowed funds on time
and in the necesssary amount.
The analysis of the practice of conducting
bankruptcy procedures shows that their
rehabilitation potential is not used effeciently
enough, and the bankruptcy procedures are, in
many cases, considered as a means of liquidating
organizations (Skripichnikov, 2009).
The recent developments in the world economy
have affected the solvency of commercial
organizations, and it is the imperative of the time
to implement new solutions aimed at the
restoration thereof. Unstable international
markets, economic restrictions, changes in tax
policy and gradual digitization in the business
cause certain problems in the process of
managing the solvency of organizations and
require new solutions.
Literature Review
The literature review shows that financial risk
management in the conditions of a crisis has been
highly pivotal in various studies. (Yankovskaya
et al., 2022) have proved that investments and
corporate social responsibility separately do not
contribute positively to sustainable development
and they linked the philosophy of financial risk
management to corporate social responsibility.
(Van Staveren, 2009) has proposed five stages
for making the risk management process more
efficient, they are: goal determination, risk
identification, risk assessment, consideration of
alternative options and risk diagnostics.
The development of financial stability
assessment approaches requires the use of
financial and operational risk assessment
coefficients. The financial coefficients, which the
analysts use to determine the uncertainty of the
organization's income formation process, are
included in the group of coefficients
characterizing the risk. They, in turn, are divided
into 2 groups:
• operational risk assessment coefficients;
• financial risk assessment coefficients.
Baboyan, K. / Volume 11 - Issue 57: 189-197 / September, 2022
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Operational risk assessment coefficients reflect
the quantitative measurement of the uncertainty
related to the receipt of operating income of the
organization.
Operational risk management (ORM) is crucial
to any organization, and in the era of big data,
analytical tools of operational risk management
are evolving faster than ever (Araz et al. 2020).
As a rule, the magnitude of the operational risk is
related to the state of the organization's scope of
activity, which makes it necessary to carry out
scope analysis in the process of operational risk
assessment. The maximum operational risk
occurs at the beginning of the organization's life
cycle or at the innovative stage of its
development, in which case the volume of
uncertainty in terms of income guarantees
reaches its maximum size. A high level of
operational risk is also observed during the
growth and development of the organization’s
life cycle. If the organization experiences a crisis
and decline during its life cycle, there occurs a
decrease in the operational risk.
There are two ways of measuring the operational
risk. In the first case, it is measured as the ratio
of the standard deviation of operating profit to its
mean value, by representing the coefficient of
covariance of operating profit, and in the second
case, the operational risk is measured as the ratio
of the standard deviation of net revenue from
sales and its mean value, by representing the
coefficient of sales covariance.
Determination of the share of the borrowed
capital in the total capital structure is one of
common approaches to determination of
financial risks; the higher the share of the
borrowed capital, the higher the financial risk.
The operational and financial risks are inversely
related to each other; hence financial risks occur
at the upper limit of operational risks. Based on
this, we can note that maximum operational risks
are accompanied by minimum financial risks, an
argument for this is currently the venture
financing, which is used at the beginning of the
life cycle of the organization.
Around the world, the pandemic has exacerbated
the risks posed by the increase of debt levels. At
the current stage, the containment of the spread
of the virus, provision of assistance to the
vulnerable groups of population and solving the
problems related to vaccines are the priority tasks
to be addressed.
Solvency is one of the most important standards
for strengthening the relations between
organizations connected with each other by
economic ties. Therefore, according to different
approaches to solvency risk assessment, it is
interpreted as the ability of a business to pay off
its current liabilities on time with the liquid
current assets.
In order to evaluate the long-term solvency of
organizations, (Van Horne, 1996) proposed four
ratios.
(Savitskaya, 2015) highlights the unreasonable
and non-targeted management of the current
assets of the organization as one of the reasons
for the decrease in solvency, noting, in particular,
the accumulation of unsubstantiated receivables,
the high share of their overdues and the large
volume of unsubstantiated inventory reserve
balances.
(Dantsova, 2015) states in her viewpoint that the
insolvency of the organization can be
significantly dependent on the non-payment of
tax liabilities within the specified periods, which
leads to additional costs for the payment of
penalties and fines.
According to (Smirnov, 2015), the solvency of
the organization is quite variable. For example,
in the case of occurrence of a maturity date of
payables and the lack of funds in the
organization's bank accounts, the organization is
assessed as insolvent, which is a result of the
financial indiscipline of accounts receivable
payers, even if the organization has a liquid
balance sheet and opportunities to attract new
borrowed funds.
(Kudryavtsev, 2015) defines the solvency of the
organization as the ability of the corporate debtor
to pay off the liabilities within the defined
periods.
It should be noted that chronic insolvency is one
of the most fundamental impulses of
manifestation of bankruptcy risk in commercial
organizations. At the current stage, the prediction
of the probability of insolvency and bankruptcy
risks is considered to be a very important
economic problem for commercial organizations
that requirs effective solutions, because the
sooner the negative trends are identified, the
greater will be the opportunities for the
organization to restore solvency. The first
attempts to assess the financial position of
organizations were made at the beginning of the
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19th century. Creditworthiness was the first
indicator used for this purpose.
However, only in the 20th century, financial and
economic indicators began to be widely used to
predict not only insolvency, but also various
financial problems. (Altman, 1968; Beaver,
1966) have made a great contribution to this
process.
Canadian researcher (Springate, 1978) proposed
its approach to predicting the probability of
bankruptcy risk based on discriminant analysis.
Discriminant analysis based on multiplicative
relationship is one of the crucial directions of
improvement of regression analysis methods in
the approaches to evaluating the solvency of
organizations.
Using the latter, evaluating the solvency of
organizations has gained special importance
when discussing bankruptcy issues. Such a
complete system was proposed by (Bastensi, Van
Den Berg, & Woody, 1997), In Neural Networks
Based Conjoint View.
Among the discriminant models, which are also
important in predicting the potential risk of
bankruptcy, special importance is given to the
approach proposed by (Zaitseva, 1998).
The approaches based on regression formulae for
assessing the potential risk of solvency and
bankruptcy of commercial organizations, mainly
characterize the situation with high accuracy and
neutralize many drawbacks of analytical
methods. However, it should be noted that the
approaches to predicting the probable risk of
bankruptcy are mainly focused on the assessment
of long-term solvency or financial stability.
The logistic regression analysis is considered to
be the most effective regression method for
predicting the probable risk of insolvency and
bankruptcy, which is an extension of the
multivariate regression analysis methodology
and is applied to situations where the predicted
parameter accepts a true or false value. Among
the solutions to logistic regression analysis
proposed by the western researchers, let us note
the model proposed by (Ohlson, 1980) and the
joint approach by (Begley et al., 1996).
One of the researchers, (Voiko, 2019), studying
the mechanisms of predicting the probability of
bankruptcy based on the use of logit models,
proposed a mathematical model for calculating
such probability for small and medium-sized
construction organizations.
(Dahiyat et al., 2021) in the coauthored article
assessed the performance of companies listed on
the Amman Stock Exchange in 2010-2019.
liquidity and solvency with data. Return on assets
(ROA) and earnings per share (EPS) were
highlighted in the developed model. Current
liabilities and total debt to total assets were
considered by these researchers as indicators of
liquidity and solvency. Within the framework of
the developed approach, correlation and multiple
regression analyzes were used for data analysis,
the results of which proved a statistically
significant relationship between liquidity and
solvency management and company size.
Using data from 244 out of 323 companies listed
on the Dhaka Stock Exchange, Mohammad
(Abdullah, 2021) developed a solvency
prediction model using artificial intelligence to
help banks effectively classify their customers
based on their solvency.
The development and practical application of a
new approach to forecasting solvency risk for
alcoholic beverage companies will be quite
useful for financial management professionals of
commercial organizations. Therefore, it was
considered as the main objective in this paper.
Methodology
Analytical approaches require assessment of the
correlation of the financial stability with
financial risks comparing their assessments and
making conclusions on the feasibility of the
policy of attracting funds.
The research was carried out by RA NAS at the
M. Kotanyan Institute of Economics. During the
research, commercial organizations of the RA real
sector were studied, the total number of
observations of which was 32. The methods of
matrices, least squares, correlation analysis,
combining financial ratios and logit analysis were
applied. The LOGIT-probit models for assessing
the insolvency and bankruptcy risk of
organizations are statistical predictive models by
their nature, which make it possible to estimate the
occurrence of bankruptcy of the organization for a
period of 1-3 years. When building similar
models, 2 groups of organizations are selected: the
first group includes organizations declared
bankrupt by the court's decision, and the second
group includes financially stable organizations. In
the process of building the model, financial
coefficients are calculated for those 2 groups of
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organizations, after which, using them, a
regression model is built with the help of the
logistic regression toolkit, which more accurately
describes the 2 groups of the sample of
organizations.
In the first step for this purpose, we offer the
following set of financial risk assessment
indicators:
• long-term debt /equity;
• long-term debt /total liabilities;
• gross assets/equity;
• total debt/equity;
• (total debt on loans+finance costs)/equity;
• profit before tax/(total debt on loans+finance
costs).
In the second step.
- In the context of correlation of the proposed
coefficients and the following
macroeconomic indicators: the AMD/USD
exchange rate (T AMD/USD), the money
multiplier (TMM), and the M1 money supply
aggregate (TM1), and the impact of the
relative growth rates on the selected
variables, we offer a logit regression
analysis approach. Development of the
mathematical model pursues 2 important
goals:
• prediction of the value of the result indicator
for the new values of the predicted variables;
• determination of the degree of impact of
each predicted variable included in the
model on the the basis of determination of
the result indicator.
The most popular methods for addressing this
problem are multivariate linear regression
(Nikonov, 2021), discriminant analysis
(Borovsky et al., 2018) and logit regression
(Luchinin & Lyanguzov, 2022).
The multivariate linear regression is mostly used
in situations where the dependent (result)
variable is considered to be a continuous
parameter and it coincides with the predicted
variables by size. In this case, the main condition
for the effectiveness of this method is the
theoretically very close linear dependence of the
result indicator and the predicted variables.
The discriminant analysis is effective to use in
situations where it is necessary to classify the
relevant subject into a specific group or class.
- As a rule, the logit regression is used in
situations where the dependent (outcome)
variable has a binary value: one true or zero
false. In this case, the result indicator can be
both discrete and continuous.
- of developing the proposed model,
adjustment of selected variables with
macroeconomic indicators is performed.:
- a table of standardized coefficients is built
using the matrix method (Goldman &
Schmalz, 2004).
- the table of squares is built based on the data
of the table of standardized coefficients.
- we develop the regression formula for
determining Y*, the prediction of the
bankruptcy risk.
In the third step.
- applying the formula 𝑃 = 1/(1 + 𝑒−𝑦), we
calculate the value of P according to the
observations made.
- we determine the ranges of P, the model (1)
of bankruptcy risk assessment of
commercial organizations.
In the fourth step, we determine the bankruptcy
risk assessments of randomly tested commercial
organizations of the Republic of Armenia.
Results and Discussion
Step 1. The analysis made for “Yerevan
Champagne Wines Factory” OJSC for 2010-
2020 shows the following trends concerning the
proposed indicators characterizing the financial
risk:
• in terms of long-term debt/equity ratio, the
mean value for 2010-2020 was 0.945, the
maximum value was 1.428 in 2010, and the
minimum value for the studied period was in
2019 – 0.523.
• in terms of long-term debt/total debt ratio,
the mean value for 2010-2020 was 0.725, the
maximum value was 0.798 in 2010, and the
minimum value was 0.511 in 2019.
• in terms of gross assets/equity ratio, the
mean value for 2010-2020 was 2.291, the
maximum value was 2.789 in 2010, and the
minimum value was 1.831 in 2017.
• in terms of total debt/equity ratio, the mean
value for 2010-2020 was 1.291, the
maximum value was 1.789 in 2010, and the
minimum value was 0.831 in 2017.
• in terms of (total debt on loans+finance
costs)/equity ratio, the mean value for 2010-
2020 was 0.372, the maximum value was 0.8
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in 2020, and the minimum value was 0.230
in 2016.
• in terms of profit before tax/ (total debt on
loans+finance costs) ratio, the mean value
for 2010-2020 was 0.185, the maximum
value was 0.680 in 2017, and the minimum
value was -0.181 in 2020.
The analysis made for “Plant of Pure Iron” OJSC
for 2010-2020 shows the following trends
concerning the proposed indicators
characterizing the financial risk:
• in terms of long-term debt/equity ratio, the
mean value for 2010-2020 was 0.018, the
maximum value was 0.06 in 2018, and the
minimum value for the studied period was in
2019 – 0.
• in terms of long-term debt/total debt ratio,
the mean value for 2010-2020 was 0.345, the
maximum value was 0.896 in 2016, and the
minimum value was 0 in 2019.
• in terms of gross assets/equity ratio, the
mean value for 2010-2020 was 1.077, the
maximum value was 1.196 in 2020, and the
minimum value was 1.010 in 2015.
• in terms of total debt/equity ratio, the mean
value for 2010-2020 was 0.077, the
maximum value was 0.196 in 2020, and the
minimum value was 0.010 in 2015.
• in terms of (total debt on loans+finance
costs)/equity ratio, the mean value for 2010-
2020 was 0.031, the maximum value was
0.156 in 2014, and the minimum value was
0 in 2016-2017 and 2019-2020.
• in terms of profit before tax/ (total debt on
loans+finance costs) ratio, the mean value
for 2010-2020 was 6819.255, the maximum
value was 51519,47 in 2020, and the
minimum value was 0.903 in 2014.
The results of the developed bankruptcy risk
assessment model are as follows:
Step 2. We have created the regression formula
for determining Y*, prediction of bankruptcy risk
which is as follows:
Y*= 0.29*(long-term debt /equity)
+0.064*(long-term debt /total liabilities) -
0.007*(gross assets/equity) +0.075*(total
debt/equity) +0.031*((total debt on
loans+finance costs)/equity) +0.028*(profit
before tax/ (total debt on loans+finance
costs)), (1).
The developed regression formula reveals that
total debt / equity coefficient has made a negative
impact on Y1, which will require use of effective
mechanisms of internal control in the financial
management process in respect of equity.
Step 3, We propose the following ranges of P,
the model (1) of bankruptcy risk assessment of
commercial organizations:
• If 0.869<P<1, the solvency of the
organization has a chronic nature;
• If 0.566<P<0.869, the organization has a
problem of restoring current solvency;
• If 0.222<P<0.566, the solvency of the
organization is assessed as normal;
• If 0<P<0.222, , the solvency of the
organization is assessed as very good.
Step 4. We have below presented the brunruptcy
risk assessments of randomly tested commercial
organizations of the Republic of Armenia:
• “Armenian Mining Contractor" LLC
- 2019: the company needs to restore current
solvency;
- 2020: which shows the solvency of the
company is assessed as very good.
“Gazprom Armenia” CJSC
- 2019: the solvency of the company is
assessed as normal;
- 2020: which shows the solvency of the
company has a chronic nature.
• “Bacon Product” LLC
- 2019: which shows the solvency of the
company is assessed as very good;
- 2020: which shows the solvency of the
company has a chronic nature.
• “TEX” CJSC
- 2019: which shows the solvency of the
company has a chronic nature;
- 2020: which shows the solvency of the
company is assessed as very good.
• “Chaarat Kapan” CJSC
- 2019: which shows the solvency of the
company is assessed as normal;
- 2020: which shows the solvency of the
company is assessed as very good.
• “Vedi Alco” CJSC
- 2019: the company needs to restore current
solvency;
- 2020: which shows the solvency of the
company is assessed as very good.
• “Beer of Yerevan” CJSC
- 2019: the company needs to restore current
solvency;
- 2020: which shows the solvency of the
company is assessed as very good.
• “Alex Textile” LLC
- 2019: which shows the solvency of the
company has a chronic nature;
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- 2020: which shows the solvency of the
company is assessed as very good.
• “MAP” CJSC
- 2019: which shows the solvency of the
company has a chronic nature;
- 2020: which shows the solvency of the
company is assessed as very good.
• “AMP Holding” LLC
- 2019: which shows the solvency of the
organization is assessed as normal;
- 2020: which shows the solvency of the
company has a chronic nature.
Conclusions
1. The impact of financial risks on “Yerevan
Champagne Wines Factory” OJSC in
respect of the maximum and minimum
values of the calculated coefficients makes it
possible to distinguish the period of 2010-
2011 as mainly a stable period of activity in
the organization, and the year of 2019 – as
unstable, which is directly conditioned by
the COVID-19 crisis.
2. The impact of financial risks on “Plant of
Pure Iron” OJSC in respect of the maximum
and minimum values of the calculated
coefficients makes it possible to distinguish
the years of 2014, 2018, 2020 as a stable
period of activity in this company and the
years of 2015 and 2019 as unstable, due to
the negative impact of devaluation of the
Armenian dram in 2014 and the COVID-19
crisis in 2019.
3. The study of the practical situation shows
that the external environment and the
renderred financial decisions are highly
important in establishing sufficient stock of
financial stability and necessary conditions
for economic development of commercial
organizations, which become rather
essential within the framework of anti-crisis
management.
4. In the conditions of increasing competition
in commercial organizations, there regularly
occurs a need to attract borrowed funds both
to finance current activities and to
implement new investment programs. On
the one hand, the borrowed funds are very
necessary, but on the other hand, their excess
amount beyond the permissible limits leads
to the loss of solvency and financial stability
of the organization.
5. Based on the study of current bankruptcy
risk prediction methods, an assessment of
the potential risk of bankruptcy of the
studied and randomly selected commercial
organizations of the Republic of Armenia
has been made, which has served as a ground
for proposing a logit regression analysis
model for predicting bankruptcy risk based
on the financial risk assessment indicators
and marginal ranges for determining its
value.
6. Based on the results of the testing, we
discovered that manifestations of insolvency
risk were observed in the randomly selected
commercial organizations of the Republic of
Armenia in 2019, which was conditioned by
the global crisis caused by COVID-19. It
should be noted that among the randomly
selected organizations, “Armenian Mining
Contractor" LLC, “TEX” CJSC, Chaarat
Kapan” CJSC, ‘Vedi Alco” CJSC, “Beer of
Yerevan” CJSC, “Alex Textile” LLC and
“MAP” CJSC have brought the solvency to
the required level due to the measures taken,
which is not the case in "Gazprom Armenia"
CJSC and "Bacon Product" LLC. As for
“AMP Holding” LLC, there was a decline in
the level of solvency in this company in
2020 comparing to 2019, which received the
maximum insolvency risk assessment
according to the logit regression analysis
model (1).
7. In order to mitigate bankruptcy risk in
commercial organizations, any program for
restoration of solvency should
simultaneously take into account both the
legal and economic aspects, which is due to
the fact that solvency has not only economic
but also legal grounds. In this regard, when
developing solvency restoration programs in
practice, in addition to the financial and
economic grounds, it is necessary to take
into account the existing legal grounds as
well. If only the economic aspects are taken
into account when developing solvency
programs, the process will, in fact, lead to
business planning. Without undermining the
importance of business planning, in our
opinion, in order to obtain more complete
solutions, it is always necessary to correlate
the legal grounds for the restoration of
solvency as well. In practice, the steps of
business planning are appropriate to carry
out in accordance with the following
algorithm:
• grouping of the most significant analytical
results for justifying the opportunities of
solvency restoration;
• identification of the main causes of
insolvency, which is very important for
developing specific measures to restore
normal solvency;
• analysis of the resources and assessment of
restrictions on their acquisition, which is due
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to the fact that in a crisis situation there is
almost always a problem of resource
limitation, which should necessarily be
taken into account when developing a
solvency restoration program;
• the conditions and procedure for
implementing solvency restoration
measures, planning and prediction of the
financial and economic results of the
organization, which is important for
justifying the opportunities of solvency
restoration;
• determination of the solvency restoration
period, which is essential during the
bankruptcy procedure, so that these periods
can be fitted within the framework of the
recovery plan;
• feasibility and coherence of the
opportunities of solvency restoration within
the framework of the program developed.
Bibliographic references
Abdullah, M. (2021). The implication of machine
learning for financial solvency prediction: an
empirical analysis on public listed companies
of Bangladesh. Journal of Asian Business and
Economic Studies, 28(4), pp. 303-320.
https://doi.org/10.1108/JABES-11-2020-
0128
Altman, E.I. (1968). Financial Ratios,
Discriminant Analysis and the Prediction of
Corporate Bankruptcy. Journal of Finance,
23(4), pp. 189-209.
Araz, O. M., Choi, T. M., Olson, D. L.,
& Salman, F. S. (2020). Role of analytics for
operational risk management in the era of big
data. Decision Sciences, 51(6), 1320–
1346. https://doi.org/10.1111/deci.12451
Ashley, M. (2020). Risk heat maps have failed
us... now what? Linkedin.
https://www.linkedin.com/pulse/risk-heat-
maps-have-failed-us-now-what-mike-ashley/
Bestens, D. E., van der Berth, V. M., & Wood,
D. (1997). Wood. Neural networks and
financial markets: decision making in trading
operations. Moscow: TVP, 236 p.
Beaver, W.H. (1966) Financial Ratios as
Predictors of Failure/Empirical Research in
Accounting Selected Studies. Journal of
Accounting Researchruen, 4, pp. 71-111.
Begley, J., Ming, J., & Watts, S. (1996)
Bankruptcy Classification Errors in the
1980s: An Empirical Analysis of Altman’s
and Ohlson’s Models. Review of Accounting
Studies, pp. 25-38.
Borovsky, V., Rakovskaya, E. E., & Bisikalo,
A. L. (2018). Discriminant analysis of
technical short texts, Vestn. Astrakhan. state
tech. university Ser. control, computing tech.
inform., 2, pp. 53–60.
Dahiyat, A. A., Weshah, S. R., & Aldahiyat, M.
(2021). Liquidity and Solvency Management
and its Impact on Financial Performance:
Empirical Evidence from Jordan. Journal of
Asian Finance, Economics and Business,
8(5), pp. 135-141.
Dantsova, L.V. (2015) Analysis of financial
statements: textbook. M.: Business and
Service, p. 147. [In Russian]
Goldman, K.D., & Schmalz, K.J. (2004). The
Matrix Method of literature reviews. Health
Promot Pract, 5(1), 5-7. doi:
10.1177/1524839903258885
Korechkov, Yu. V., & Tselishchev, P. B. (2017).
Features of cryptocurrency exchanges and
cryptocurrency volatility. Economics and
society, 35, pp.760–761. [In Russian]
Kudryavtsev, D.S. (2015). Methods of analysis
and assessment of the risk of insolvency of
small businesses (dissertation for the degree
of candidate of economic sciences). Financial
University under the Government of the
Russian Federation, Russian, p. 64.
Luchinin, A.S., & Lyanguzov, A.V. A (2022)
logistic regression-based model to predict
ICU mortality: problems and
solutions. Kachestvennaya Klinicheskaya
Praktika = Good Clinical Practice, 2, 13-20.
(In Russ.) https://doi.org/10.37489/2588-
0519-2022-2-13-20
Mackendrick, D. (2016). Six Reasons to Love
Blockchain. M.: PCWeek, 17.05. - Access
mode:
https://www.itweek.ru/idea/article/detail.php
?ID=185642&param=blk
Nikonov, M. V. (2021). Multiple linear
regression in the assessment of the UK
currency market / M. V. Nikonov. Text:
direct. Young scientist, 392(50), S. 7-10.
URL: https://moluch.ru/archive/392/86492/
(date of access: 12/12/2022).
Ohlson, J. (1980). Financial Ratios and the
Probabilistic Prediction of Bankruptcy.
Journal of Accounting Research, 6, pp. 12-45.
Savitskaya, G.V. (2015). Comprehensive
analysis of the economic activity of the
enterprise: textbook. M.: INFRA-M, p. 548.
[In Russian]
Skripichnikov, D.V. (2009). Rehabilitation
procedures used in bankruptcy cases in
foreign countries and ways to improve them
in the Russian Federation. M.: Bridges and
cultures, 208 p. 7–135. [In Russian]
Smirnov, P.A. (2015). Analysis of the solvency
of the enterprise. European research, 5(4), p.
15–16. [In Russian]
Volume 11 - Issue 57
/ September 2022
197
https://www.amazoniainvestiga.info ISSN 2322- 6307
Springate, G. L. V. (1978). Predicting the
Possibility of Failure in a Canadian Firm.
Unpublished M.B.A. Research Project,
Simon Fraser University, January. 200 p.
Tebekin, A.V. (2019). To the question of the
formation of the concept of management in
the 2030s. Bulletin of the Moscow Finance
and Law Academy, 2, p.168-176. [In
Russian]
Vaganova, O., Ilyashenko, L., Smirnova, Z.,
Bystrova, N., & Kaznacheeva, S. (2019).
Students’ creative abilities development in
higher educational institution. Amazonia
Investiga, 8(22), 701-710. Retrieved from
https://amazoniainvestiga.info/index.php/am
azonia/article/view/822
Van Horne, J.C. (1996) Osnovy upravleniya
finansami [Fundamentals of Financial
Management]. Moscow: Finance and
Statistics Public., 800 p.
Van Staveren, M.T. (2009). Design propositions
for implementing risk management in
organizations. (Doctoral dissertation),
University of Twente, Groningen, The
Netherlands.
Voiko, A.V. (2019). Modeling the probability of
bankruptcy of construction companies in the
Russian Federation. Finance: theory and
practice. Finance: Theory and Practice, 23(5),
p. 62-74. https://doi.org/10.26794/2587-
5671-2019-23-5-62-74
Yankovskaya, V.V., Mustafin, T.A., Endovitsky,
D.A., & Krivosheev, A.V. (2022). Corporate
Social Responsibility as an Alternative
Approach to Financial Risk Management:
Advantages for Sustainable Development,
Risks, 10(5), 106.
Zaitseva, O.P. (1998). Anti-crisis management in
a Russian company. Aval Siberian Financial
School, 11, S. 66-73.