Published at : 27 Dec 2022
Volume : IJtech
Vol 13, No 7 (2022)
DOI : https://doi.org/10.14716/ijtech.v13i7.6185
Nikolay Lomakin | Volgograd State Technical University, ave. V.I. Lenina, 28, 400005, Russia |
Maxim Maramygin | Ural State University of Economics, 8 Marta st., 62, Yekaterinburg, 620144, Russia |
Alexander Kataev | Volgograd State Technical University, ave. V.I. Lenina, 28, 400005, Russia |
Sergey Kraschenko | Volgograd branch of the PRUE G.V. Plekhanov, st. Volgodonskaya, 11, Volgograd, 400066, Russia |
Olga Yurova | Volgograd State Technical University, ave. V.I. Lenina, 28, 400005, Russia |
Ivan Lomakin | Volgograd State Technical University, ave. V.I. Lenina, 28, 400005, Russia |
The
study's relevance lies in the fact that the important problem is ensuring the
sustainability of the development of the Russian economy. Its financial system
is influenced by many factors, among which are increased risks and market uncertainty,
aggravation of the global military-political confrontation between the largest
world powers, and technological innovations associated with the emergence of a
new technological order "Industry 4.0". The purpose of the work is to
study the financial stability of the state as a complex system based on a
cognitive model that involves the use of an artificial intelligence system and
a VaR model. The novelty of the
study lies in the proposed approach, which involves the formation of a
cognitive model of economic processes, which includes the Decision Tree
artificial intelligence system and the VaR model, both generate GDP forecasts,
then their forecast values are compared. A hypothesis has been put forward and
proved that with the help of a cognitive model that includes an A.I. system and
a VaR model, it is possible to obtain a forecast of the volume of Russia's GDP,
the dynamics of which allows us to assess the sustainability of the development
of the country's economy.
Cognitive model; Financial stability; Market uncertainty; Risk; Decision tree
The purpose of the work is to study the state's financial
stability as a complex system based on a cognitive model that involves the use
of an artificial intelligence system and a VaR model. Calculate the forecast
values of the volume of the gross domestic product of the Russian Federation
using artificial intelligence systems and the VaR method.
To achieve this goal,
the following tasks were set and solved. 1) The theoretical foundations of the
financial stability of the country's economy, the formation of GDP and the
value of exports were studied. 2) The factors that determine the sustainability
of economic development are identified. 3) The forecast values of the GDP
volume were calculated in two different ways: both using the Decision Tree
neural network and using the VaR model. 4) The accuracy of forecasts obtained
by different methods is compared.
The relevance of the study comes from a problem of ensuring stability
of the development of the Russian economy in the face of increasing market
uncertainty and the action of a number of factors due to the consequences of
the COVID-19 pandemic, increased economic sanctions from the United States and
the eurozone countries, the aggravation of the global military-political
confrontation, the changing economic landscape, the emergence of a new
technological way of "Industry 4.0" and others. The study's novelty is determined by the fact
that an approach is proposed to fill the gap, which concerns the problem of the
lack of a reliable approach to using a cognitive model to form an accurate
forecast of GDP to achieve sustainable development of the domestic economy.
Getting a correct forecast of the volume of gross domestic product in the face
of market uncertainty is essential.
The paper attempts to put forward and prove the hypothesis that in
the face of uncertainty and intensification
of all types of risk, based on
the application of a cognitive. Model , using the artificial intelligence
system "Decision Tree" and the VAR model, it is possible to obtain
predictive GDP values to support managerial decision-making to ensure the
sustainable development of the economy.
The practical significance of the study lies in the fact that in
the course of the study the prerequisites for solving an important national
economic problem were formed - forecasting the value of GDP and ensuring the
sustainable development of the country's economy. The action of the above
factors requires an assessment of global risks, and the capabilities of A.I. systems
as elements of cognitive modeling.
As you know, the economic development of countries largely depends
on exports. The export of goods and services gives impetus to the growth of
national production, income and employment of the population, contributing to the growth of the economy and GDP. The
coronavirus pandemic resulted in an 8% trade in goods and a 21% year-on-year
decline in trade in commercial services
in 2020. Thus, global exports of manufactured goods decreased by 5.2% in 2020,
while total exports of goods decreased by 7.7% overall.
Russia's trade turnover for 2021 amounted to $784.4 billion (of
which exports - $491.2 billion, imports - $293.1 billion), an increase of
+38.1% compared to the same period last year.
Exports from Russia in 2021 amounted to $491.2 billion, an increase of
+46% compared to last year’s period. The dynamics of Russia's exports are
presented below (Figure 1).
Figure 1 Dynamics of Russian
exports, billion$
Studies have shown that the stability of the Russian economy is
largely affected by partner countries. The economies of different countries
have shown different economic stability. Russia’s merchandise exports remained
below their level of two years ago (-8 percent) while those of China were up
sharply (+31 percent). The standard deviation calculated from the results of
fluctuations in quarterly parameters of the gross domestic product of countries
for 2020 reflects the amount of financial risk, which can be used in assessing
the country's stability.
Studies show that this "economic sustainability"
category is a complex and multifaceted concept. Many works of Russian and
foreign scientists are devoted to the study of the problem of stability of
economic systems. These problems have long been reflected in the works of
economists, for example, Gurvich, Prilepsky, Bobylev, M.A. Konishchev, others. (Abdrakhmanova et al.,
2019).
Kleiner proposed a normative model for the distribution of the
role functions of subsystems over the stages of the crisis cycle of the
economy. The problem of developing a cognitive model of the national financial
market, considering the peculiarities of its construction and the possibility
of using it to assess the security of its functioning, was studied by
Loktionova (Loktionova,
2022).
The works are devoted to the study of issues of ensuring financial market stability based on cognitive
modeling (Badvan
et al., 2018).
Cognitive modeling of financial market stability factors, and constructing
cognitive maps was considered in their
works by Emelianenko and Kolesnik (Emelianenko & Kolesnik, 2019).
Mohammed Ali Berawi has established that many industrial sectors
are in the middle of a digital transformation that has emerged from the
advancement of information and data technology, enhancing the use of computers
and automation with smart and autonomous systems powered by data and machine
learning. This revolution has been broadly adopted in the industry by using digital technologies, sensor
systems, intelligent machines, and smart material s in its processes. (Berawi, 2020)
In modern conditions, it becomes relevant to study the issues of
using artificial intelligence to ensure the sustainable development of the
economy, and reduce financial risks in the face of increasing market
uncertainty. Abdalmuttaleb and Al-Sartawi reviewed the latest research in the
application of artificial intelligence for stable financing and sustainable
technologies (Abdalmuttaleb
& Al-Sartawi, 2021)
Burova’s paper suggests a mechanism for managing the costs of I.P.
of an industrial enterprise, which: (1) considers the high level of volatility
of the external environment common to the digital economy and the effects
exerted by risks on cost management; (2) can be used for controlling the level
of target costs and introducing corrections made to the costs in due time
according to the changing external and internal conditions so that the target
profitability can be ensured; and (3) is based on using up-to-date and
high-precision tools and methods for assessing risks and their effects on the
costs and profitability of the IP (Burova et al., 2021)
The materials are presented at the International Conference
"Global Economic Revolutions: The Era of the Digital Economy". The
Lomakin et al. developed a neural network model that makes it possible to
forecast the profit of enterprises in the real sector of the economy that is at
risk. The analysis showed that the risk of financial income of enterprises
(sigma) in chronological sequence increased unsustainably from the level of 0.4
in the second quarter of 2015 to a maximum of 3.1 with subsequent consolidation
to 2.8 billion rubles, while its average value was 2.09 billion rubles (Lomakin et al., 2019).
The study of Nadezhina aims to evaluate the risks of integration
processes in the EU. Two indicators were used to quantify the degree of convergence:
1) convergence and 2) convergence. This is very important in today's
environment (Nadezhina
et al., 2021)
Certain aspects of the use of neural networks in the financial
sector intersect with issues of economic analysis in the financial management system.
Morozova et al. note that in the conditions of the development of the modern
economy, for the effective operation of an enterprise in the face of
ever-increasing competition, it is necessary to respond in a timely manner to
various kinds of changes in all factors affecting the enterprise. (Morozova et al., 2022).
An important factor in the financial stability of the economy is
the reliable operation of the banking sector, and preventing the growth of
overdue debt is one of the most pressing issues for ensuring reliability. To
prevent the development of outstanding loans in the credit sector, it is
important to assess the creditworthiness and financial stability of the
enterprise. Rybyantseva et al. considered separate approaches for assessing the
financial stability of an enterprise (Rybyantseva et al., 2017).
In the deep risk model proposed by Lin et al., a deep learning
solution is proposed to analyze latent risk factors while improving the ovariance matrix
estimation. Experiments were carried out on stock market data and demonstrated
the effectiveness of the proposed solution. The method allows you to get 1.9 %
higher than the identified variance and reduce
the risk of the global minimum variance portfolio (Lin et al., 2021).
The risk is identified by estimating the standard deviation based
on the biased estimate of the variance, which can be calculated using the
formula:
Of practical interest are the studies of Zhan et al. in the
development of graphical models for financial time series and portfolio
selection. The authors explored various graphical models for building optimal
portfolios. Graphical models such as PCA-KMeans, auto-encoders, dynamic
clustering, and structural learning can capture time-varying patterns in a
covariance matrix and allow you to create an optimal and robust portfolio. When
comparing derived portfolios from different models with the underlying methods,
charting strategies produced steadily increasing returns at low risk and
outperformed the S&P 500 index. This work suggests that charting models can
effectively learn time dependencies in time series data (Zhan et al., 2021).
Financial risk assessment using the VaR model provides high performance
to support managerial decision-making in the financial sector. A team of
scientists consisting of Kei Nakagawa, Shuhei Noma, and Masaya Abe proposed an
approach based on the use of the RM-CVaR model. It is known that dispersion is
the most fundamental measure of risk that investors seek to minimize, but it
has several drawbacks. Notional Value at Risk (CVaR) is a relatively new risk
measure that overcomes some shortcomings of well-known variance risk measures
and has gained popularity due to its computational efficiency (Nakagawa et al., 2020).
In the presented work, such research methods were used as monographic,
analytical, statistical, and cognitive models, including the artificial
intelligence system "Decision Tree" and the VaR model. The neural
network model includes indicators that reflect the dynamics of the development
of the domestic economy. The studies reflected in this article relied on the
research methodology followed by the authors. This study's methodology involves
using the main research method - a cognitive model. Modeling financial and
economic stability based on a cognitive model allows us to develop a new model
for the task of supporting managerial decision-making in terms of the financial
and economic stability of the Russian financial system, the most important
prognostic parameter of which is the value of GDP. The cognitive model acts as
a kind of trigger, which in turn launches methods as independent model
programs: Decision Tree VAR-model, which allows you to get the forecast value
of GDP and compare the results. The Decision Tree model dataset is shown below
(Table 1).
Table 1 The dataset of the AI-model Decision
Tree (fragment)
Year |
Key rate |
Growth of bank assets, % |
Share of overdue loans, % |
GDP, billion rubles |
RTS Index |
Dollar exchange rate |
2021 |
8.5 |
16 |
23.5 |
131015 |
1608 |
73.7 |
2020 |
4.25 |
16.8 |
17.8 |
1073015 |
1376 |
73.8 |
2019 |
7.25 |
10.4 |
5.9 |
109241 |
1549 |
61.9 |
2018 |
7.75 |
6.4 |
7.5 |
103861 |
1157 |
69.8 |
2017 |
8.25 |
-3.5 |
9.3 |
91843 |
1154 |
57.6 |
Continuation
of table 2
Investments in assets in
GDP, % |
Share of robots on
exchange,% |
The capital outflow of
billion rubles |
Risk (VaR) banking system,
billion rubles |
Bank assets, trln. rub. |
GDP forecast, billion
rubles |
21.2 |
58 |
72 |
-108.5 |
120 |
130015.0 |
16.5 |
55 |
53 |
-72.7 |
103,7 |
107315.5 |
20.6 |
55 |
25,2 |
-77.5 |
92,6 |
109241.5 |
20.6 |
51 |
60 |
-77.1 |
92,1 |
103861.7 |
21.4 |
51 |
33,3 |
-58.8 |
85,2 |
91843.2 |
The neural network
dataset includes data for the period 2010-2021. The statistics are given in
Table 1 to use these data to train the neural network model.
Decision trees (D.T.) are based on a
non-parametric supervised learning method that is used for classification and
regression. The goal of the method is to create a model that predicts the value
of the target variable based on the study of simple decision rules derived from
the data's characteristics. The tree can be viewed as a piecewise constant approximation.
The deeper the tree, the more complex the decision rules, and the fitter the model. Decision trees are used for both classification and regression problems. Understanding the Importance of Variables in the forests of random trees is presented in many works, including Louppe et al. (Louppe et al., 2020).
A binary classification (resp. regression) tree (Breiman et al., 2022) is an input-output model represented by a tree structure, T, from a random input vector (X1...XP) taking its values in (X1*....*XP)=X to a random output variable A tree is built from a learning sample of size N drawn from P(X1....XP,Y) using a recursive procedure that identifies at each node t the split st= s*. For which the partition of the Nt node samples into tL and tRmaximizes the decrease
of some impurity measure i(t) (e.g., the Gini index, the Shannon entropy, or the variance of Y ), and where The tree construction stops, e.g., when nodes become pure in terms of Y or when all variables Xi is locally constant.
As a result of the study, forecast values of Russian GDP were obtained
based on a cognitive model that includes the Decision Tree neural network and
the VaR model. The artificial intelligence system "Decision Tree,"
and the VaR model were formed based on financial and macroeconomic indicators
obtained at the World Trade Statistics Review.
3.1. Cognitive
model
Results should be clear
and concise. Show only the most significant or main findings of the research.
Discussion must explore the significance of the results of the work. Adequate
discussion or comparison of the current results to the previous similar
published articles should be provided to show the positioning of the present
research (if available).
To form a cognitive map
and conduct a scenario analysis, it is necessary to select criteria for
evaluating the effectiveness of the Russian financial market, which should act
as the peaks of the map is created. The solution to this problem will require a
search for different approaches to the very concept of financial market
efficiency and indicators of its assessment.
According to Paul Trejo (M.A. from California State University Dominguez
Hills) semantics is the study of meaning and relationships between the world,
and the human mind. (Trejo, 2021). There are
several classes of relationships: ancestral relationships; relationship
"whole-part"; synonymy and antonymy; logical relationships;
functional relationships; attribute relationships; quantitative relationships;
spatial relationships; temporal relationships; linguistic relations. Much
knowledge can be represented in the form of hierarchical structures.
It seems appropriate to
use an approach that involves the use of a semantic model of knowledge
representation regarding the sustainability of the economy in order to forecast
GDP based on the A.I. system and the VaR model. The developed cognitive model
of economic sustainability is based on the approach proposed by Matokhina with
a team of authors (Matokhina et al., 2022).
Graphviz is a package of utilities developed by AT&T labs for automatically visualizing graphs given as textual descriptions. The package is distributed with open-source files and works on all operating systems, including Windows, Linux/Unix, and Mac OS. The code script in the Dot language is shown in Figure 2.
Figure 2 Code script in the Dot language of the
semantic model of knowledge representation
The package's main program is
"dot", an automatic visualizer of directed graphs, which takes a text
file with the graph structure as input, and generates a graph as a graphic,
vector, or text file as output. The main program of the package is
"dot", an automatic visualizer of directed graphs, which takes a text
file with the graph structure as input, and generates a graph as a graphic,
vector or text file as output. The archive with the program contains the file
bin/gvedit.exe. As a result of execution, a dialog box appears with the ability
to edit the dot file and view the resulting semantic network. DOT is a graph
description language. A graph described in the DOT language is a text file with
a.gv or .dot extension in a format that is understandable to a person and a
processing program. Below is a visualization of forming a semantic model for
representing knowledge about GDP neuroprotection using graphs (Figure 3).
Figure 3 ?ognitive model of Russian GDP
The cognitive
model allows you to optimize the factors, and the architecture of the Decision
Tree and compare the forecast values of GDP with the results of the forecast of
the VaR model.
3.2. Development
of the AI-system "Decision Tree"
The following factors were included in
the Decision Tree neural network model: key rate; Growth of bank assets, %;
Share of overdue loans, %; GDP, billion rubles; RTS Index; dollar exchange
rate; Investments in fixed assets in GDP, %; Share of robots on the exchange,
%; Capital outflow, billion dollars; Risk (VaR); banking system, billion
rubles; Bank assets, trln. rub.; GDP forecast, billion rubles.
The AI-model Decision Tree was successfully formed on the Deductor platform. The neural network graph is shown below (Figure 4).
Figure 4 The structure of the Neural network
“Decision Tree”
Using the "What-if" function,
the forecast value of Russia's GDP for the next year was obtained. The forecast
value of Russia's GDP will be from 109241.5 billion rubles by 2023. With an
actual value of 131,015 billion rubles, the neural network predicts a decrease
in GDP by $21,773.5 billion, or 16.6%.
3.3 VAR
– Model
VAR models are often used to predict
interrelated time series systems and analyze the dynamic impact of disturbances
(shocks) on a system of selected indicators. The initial parameters of the
various series of the value of Russian GDP for forecasting using the VaR model
are presented below (Table 2).
Table 2
The initial parameters of the variation series of the value of Russian GDP
Year |
GDP, billion rubles |
Change, % |
Year |
GDP, billion rubles |
Change, % |
Year |
GDP, billion rubles |
Change, % |
2021 |
131015 |
0,220842 |
2017 |
91843.2 |
0.07273 |
2013 |
72985.7 |
0.07169 |
2020 |
107315.3 |
-0.01763 |
2016 |
85616.1 |
0.03043 |
2012 |
68103.4 |
0.132904 |
2019 |
109241.5 |
0.051798 |
2015 |
83087.4 |
0.05134 |
2011 |
60114 |
0.351138 |
2018 |
103861.7 |
0.130859 |
2014 |
79030 |
0.08281 |
2010 |
44491.4 |
0 |
The calculation of
financial risk by the VaR model was performed using the Data Analysis package
in XL tables. Risk assessment is essential in predicting financial parameters.
Risk assessment is essential in all areas of activity. Fauzi N. and colleagues
concluded risk management is a constant effort that must be carried out throughout
the life of a project.
Due to work involved, the administration
of each risk management stage is important for construction and property
development projects (Fauzi & Jahidi, 2022)
According to the VaR model, with a
probability of 99%, the absolute value of the financial risk of a reduction in
Russia's GDP may amount to 17,093.03 billion rubles in 2022 or 13.0 %, and the
forecast value of GDP may amount to 113,921.97 trillion rubs. Using the VaR
model, a table chart was formed with the frequency of deviation (percentage of
GDP change) falling into one or another interval.
After substituting the parameters into
the equation, the forecast value of Russian GDP for the next period (year) was
obtained based on the VaR model: Pt+1=(-0.130466179+1)*131015 = 13921.97
billion rubles. An objective assessment of the quality of the forecast will be
obtained at the end of the 2022 year. The calculations show that both models
predict a decline in GDP, so the forecast value of GDP for the next 2022,
calculated by the A.I. system, was 83.38% relative to the actual one, while
that calculated by the VaR model was 86.95%.
Further research into the problem of
sustainable development of the domestic economy can be continued in the
following areas. Firstly, the use of cognitive models to assess the state and
forecast the future sustainability of the domestic economy as a complex system.
Secondly, monitoring and improving the capital structure to reduce the share of
bad loans. Thirdly, it is important to study and take into account cognitive
intellectual models, global challenges, and trends in world politics - how
strong their impact on economic processes, world trade, and production.
The conducted research allows obtaining
an increment of knowledge, allowing us to close the scientific gap regarding
the influence of factors influencing the complex formation processes of export
earnings in modern conditions. In this case, two approaches were used: an
artificial intelligence system - a perceptron and a VaR model.
The study's novelty is in a proposed approach that involves the use of
the cognitive modeling of the processes in the interaction of elements of the
economy as a complex economic system using the artificial intelligence system
"Decision Tree" and the VaR model to obtain a forecast of domestic
GDP. A forecast of the GDP value was formed both with the help of the neural
network software on the Deductor platform and with the help of the VaR model
with a given probability level. A hypothesis has been put forward and proved
that using the A.I. system and the VaR model; it is possible to obtain a
forecast of the volume of Russian GDP, the dynamics of which make it possible
to assess the sustainability of the development of the country's economy.
According to the VaR model, with a probability of 99%, the absolute value of
the financial risk of a reduction in Russia's GDP may amount to 17,093.03
billion rubles in 2022, or 13.0 %, and the forecast value of GDP may amount to
113,921.97 trillion rubs. This study provides an increase in scientific
knowledge, which allows closing the scientific gap in terms of identifying and
assessing the influence of factors that determine the formation of domestic GDP
and the financial risks of this process in modern conditions.
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