Application of Management based on Mathematical Models to Solve
Investment Strategy Problems
NIYAZI HASANOV
Department of Business Administration, Azerbaijan State University of Economics (UNEC)
AZERBAIJAN
TOKHTAPOSHA AKBULAEVA
International University named after K. Sh. Toktomamatov
Jalal-Abad, KYRGYZSTAN
KAMAL AHMADOV
Director of Mingachevir Tourism College
AZERBAIJAN
AKRAM HASANZADEH
Ministry of Agriculture. Agrarian Science and Information Advisory Center,
Chief Specialist of "Information and Consulting Department"
AZERBAIJAN
Abstract: - This article analyzes the emergence of large investment opportunities for the development of
different areas of the economy in the context of political and economic changes in a competitive environment
of the market economy and its relevance shows itself in the underdevelopment of risk analysis and its
experimental methodology with the need to improve quality of investment activity, as well as project decision
making, the contradictions between the possibility and impossibility of achieving the planned outcome and
application of management based on mathematical models to solve investment strategy problems of firms and
companies in this field. Application of management based on mathematical models to solve investment strategy
problems, development and intensification of risk analysis theory and specification of strategy for purpose, the
introduction of practice to the process of making investment decisions and efficient recommendations were
developed and ways to reach the goals were designated for all the activities and measures taken in this
direction. The action process is established based on the solution of made decisions and proved its
compatibility with the pre-defined trajectory based on strategic opinions and occurrence time of the existing
and principally indefinite, mentioned relevant events, the efficiency of application of management based on
mathematical models to solve investment strategy problems. Analysis methods have been establishetod to apply
management based on mathematical models to solve relevant problems in the market economy and suggestions
and recommendations for its practical usage in investment-project activity have proved that economic-
mathematical models are efficient tools.
Key-Words: - financial category of risk, risk capital investment, management strategy, risk management
strategy, classification of mathematical models of forecasting.
Received: August 17, 2021. Revised: March 15, 2022. Accepted: April 18, 2022. Published: May 6, 2022.
1 Introduction
It should be taken into account in the investment
strategy that the presence of risk assumes the
accuracy of one of the possible options, therefore,
all the possible alternatives should be analyzed in
their acceptance process, so that it is possible to
choose the most efficient and risk-free one.
Application of management based on mathematical
models to solve investment strategy problems, the
specific content of risk situations, and related
alternatives have a different range of challenges and
are solved in different ways and with the help of
different tools. Based on investment and relevant
experience, the application of management based on
mathematical models is possible to solve investment
strategy problems. The optimal volume of demand
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should be considered in any and other complex
production issues, for example, investment requires
the usage of special tools and risk analysis methods.
In this regard, the operation of the complex
probability analysis investigated with the model, i.e.
notwithstanding negative outcome or loss, any and
other risks are considered the last special (engine of
development) outcome of the project and possible
profitable source in the end.
Calculation within the framework of the project
is made with economic-mathematical methods and
models develop performing application of
management based on mathematical models to solve
investment strategy problems. it can be defined as
an important tool in making decisions under
uncertain conditions. The main attention should be
directed to the application factors of management
based on mathematical models to solve investment
strategy problems while conducting relevant applied
research based on analysis. Furthermore, all of their
sets are structured by conditionally controlled and
uncontrolled sets.
Thus, the main point at this stage is the plication
of management based mathematical models to solve
investment strategy problems, choosing a special
tool based on objective criteria and as we can see it
plays an important role in rational decision making
under uncertain conditions.
2 Using Relevant Management Based
on Mathematical Models to Solve
Strategic Problems
The financial category of risk has already been
proved and therefore, the level and scale of risk can
be impacted by financial mechanis. We cannot
forget that the process is conducted with the help of
financial management and key strategy methods.
Strategy and tactics together create special
management mechanism of risk and it is risk
management.
Management strategy comprises of usage
methods and direction of means in reaching the set
target and tactics is specific method and way to
achieve the goal set in the specific conditions.
Choosing the most relevant and optimal solution
among management methods and ways in specific
economic situation is the goal of the management
tactics. [1. P. 396].
In this regard, the following rules are used in risk
management strategy:
appropriation limit;
probability of optimal outcome;
optimal variable of the outcome;
optimal compatibility of efficiency and risk
magnitude.
The essence of appropriation limit rule is for
investor to choose an option that ensures efficient
outcome (revenue) with minimum risk among
possible options of risk capital investment. Optimal
compatibility rule of the magnitude of appropriation
and risk is that the manager evaluates the projected
volume of benefit and risk (damage, loss) and can
make a decision about making the capital
investment to the firm or company which enables
him to get the projected benefit and at the same time
to avoid big risks. [2. P. 103].
The rule for making risk capital investment
decision is completed with the selection methods of
solution options:
Method 1. Selection of solution, in case the
probability of possible economic situations is
known;
Method 2. Selection of the relevant solution option,
on the condition that the probability of possible
economic situations is not known, but they have
relative prices;
Method 3. Selection of the solution option, on the
condition that the probability of possible economic
situations is not known, but the main directions of
the result rates of capital investment are known;
The permanent management system can happen
in the condition of circulation of relevant
information through management channels and it
consists of the acquisition, development, and usage
of the information [7.p.129]. In this regard,
information acquisition that is accurate, reliable, and
sufficient in the given conditions plays an important
role in the specific decision-making process in risk
management.
We need to consider that risk management function
has two types:
functions of management facility;
functions of management entity.
In this regard, functions of the management facility
shall comprise of the following:
risk solution;
risk capital investment;
works on risk level reduction;
risk insurance process;
economic relationships and connections
with the processes between economic
entities.
Entity functions shall include:
forecasting;
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organization;
management;
coordination;
stimulation;
control;
It should be remembered that forecasting is the
ability to foresee the event within the certain time
frame and is one of the main functions of risk
management. [8.p. 63]. At the same time,
forecasting features having alternative in the
establishment of financial indicators and parameters.
It defines different development options of
management facility’s financial position according
to pre-established tendencies.
Several mathematical methods, which are based
on the analysis of the relevant series (in this case, it
means the set of discrete observations) and fixed in
a time-consistent manner are widely used in
forecasting process. The main purpose of statistical
analysis of time series is to learn about compatibility
between regularities and details in formation of
series’ values and in forecasting with the extension
of the credible interval. [3. P.239].
According to regularities that explain the
dynamics of the previous indicators, it is used in
forecasting of the future value; coincident relevance
of accounting allows to define the probability of
deviation from the regularity and its possible
volume. The formation of series’ values should be
determined by 3 main types of regularity:
inertia of tendencies;
inertia of the interconnection between
consecutive series’ values;
under research;
inertia of the connection between the
indicator and the factors that affect it.
Based on this situation, there are following issues in
analysis and modelling:
trends;
interconnectivity between consecutive
series’ values;
causal connection between the indicator
under study and the factor indicators.
It was proposed that the first problem be solved with
modelling of growth curves, the second with the
adaptive methods and the third with econometric
methods.
Fig. 1: Classification of mathematical models of
forecasting
Growth curves are mathematical functions used in
in the analytical equation of time series. Growth
curves are frequently used in practical works and
processes; there are three main types of growth
curves, these are unlimited growth, limited growth
with bend point, and limited growth with bend point
[9. p.69]. It should be remembered that there are
functions that describe unlimited growth processes.
These are linear, quadratic, power, exponential,
genetic curve, first and second degree linear-
logarithmic functions.
The foundation of this type of development
processes comprises of linear-logarithmic functions
of volume indicators. The foundation of this type of
development processes is characteristic to volume
indicators but they also frequently comply with the
development of the relative indicators.
It is rather rational to use Johnson graph and
modified practice in order to describe limited
growth indicators which characterize many relative
indicators. Rational curves characteristic to the
demand of some new commodities can be used to
describe limited growth procedures with bend point.
Parameters of growth curves are evaluated with
least-squares method, i.e. they are chosen in a way
that graph of growth curve function is located from
the given points in a minimum distance. According
to least-squares method, all observations are
considered in the same value in the evaluation
process of model parameters, i.e. their information
value is considered the same but development
tendencies remain the same in all observation areas
[10. P. 130].
Changes in growth curve tendencies can be
recorded, therefore, outdated tendencies are
frequently used as applicable during forecasting.
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Unlike growth curve models, while evaluating the
parameters of adaptive models they are given
different values depending on the impact of
observations to the current rates. This is observed in
all changes and regularities occurring in tendencies
and allows to consider any changes.
All adaptive models are based on two layouts:
moving average;
auto regression.
According to the layout of moving average, rate
of the current observation is the center of all the
previous observations’ values, and it should be
noted that the values are getting lower as they get
far away from the last observation, i.e. the value of
observational information increases with their
proximity to the last interval of observations [11. P.
193]. The main models of moving average are
considered Holt’s and Brown’s models (zero, first
and second order), the latter is the special scenario
case of the former. First-order Holt model is as
follows:
ktAAktYP)()( 101
Where
A t
0( )
- current growth rate;
Yp(t1k) – current observation rate;
A t
1( )
- current growth;
k
- number of steps-ahead.
Then, their separation level is defined and
model parameters are modified as applicable:
A t A t A t t
0 0 1 1
1 1( ) ( ) ( ) ( ),
),()1()( 2111 tetAtA
Where:
1 2
- adaptation coefficient that
varies between 0 and 1.
Parameters are calculated consecutively and
their last observation value defines the final outline
of the model. Starting values of the parameters are
evaluated with the least-squares method based on
several primary observations of series. [12. p.66].
Moving average models reflect the changes in
tendencies more accurately and at the same time
they can reflect the waves.
Current observation value in autoregressive
layout is the sum of previous observations’ values,
however, weight ratios are not reflected.
Information value of the observations is not defined
with their proximity to the value, but the connection
between them. Autoregressive models are not
designated for the depiction of procedures with the
tendency but they reflect the waves well and this is
important in reflecting the development of unstable
processes.
In order to make the application of
autoregressive models possible, switch should be
made from
)(tY
time series to
Z t( )
time
series:
Taking this into account the outline of
autoregressive model will be as follows:
( ) ( ) .... ( ),t A A Z t A Z t p
p
0 1 1
where,
p
- model sequence.
A Ap1,....
model parameters are
calculated with the least-squares method. The
difference based on the structure model should be
calculated with forecast value in
k
steps-ahead
series.
For the difference sequence
d1
:
1for );1()()1( knZNYNY
,
2for ),2()1()2( knZNYNY
One of the most successful (therefore,
famous) adaptive models for making short-term
forecasts is autoregressive integrated moving
average model (ARIMA). It reflects positive
features of both moving average and autoregressive
models, therefore can forecast on any series.
Autoregressive integrated moving average
model allows to describe time series with less
parameters and taking into account latest changes in
tendency, we can forecast on that. ARIMA model is
frequently called as Box-Jenkins model by the
authors and it is suggested that this model should be
used in analysis and description of time series of
observations on process parameters in economy,
nature and technics.
Model in seasonal variation is described
through 6 whole-number structural parameters:
sum of autoregressive terms p;
difference sequence d;
sum of moving average terms q;
sum of seasonal autoregressive terms P;
seasonal difference sequence D;
sum of seasonal moving average terms Q;
seasonal S and these ARIMA are written
like
S1111 Q)D(Pxq)d(p
.
Formally, ARIMA model should be written
like this:
;)()()()()( t
SS aBBtBB
)),(()( tYt DD
S
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P
PBBB
.....1)( 1
where;
q
qBBB
....1)( 1
sp
pBBB ....1)( 1
SQ
Q
SS BBB
....1)( 1
Where:
- differencing operator;
S
- seasonal differencing operator;
s
BttB ),1()(
- is such a
differencing operator that
1
),()( aSttBS
- is
considered discrete white scale factor.
i
parameters-autoregressive
parameters;
i
- moving average parameters;
i
- seasonal autoregressive parameters;
i
- seasonal moving average parameters.
The model must be stationary and rotating
and must meet the requirements of the conditions,
therefore, there are some limitations to the model
and those limitations include that the roots of the
denominations must be within a single circle
[13.p.191]. In that condition,
a
error is the one
step-ahead error of relevant decision. It is
impossible to analyze the model with
implementation of stationary and rotating
conditions.
Non-seasonal ARIMA model is considered
special when it is seasonal. In case of absence of
seasonal components, model is described with three
whole-number structural parameters -
qdp ,,
, and
seasonal vectors of the model get worse. Non-
seasonal ARIMA model’s special case is
autoregressive model
)(pAP
and moving
average model -
)(qCO
B, they have been
reviewed in the economic literature before. ARIMA
model has the ability to implement in several levels
in computer program case scenario.
In risk management, the regulatory process
has the ability to influence the management facility
and thus, the stability of this facility can be ensured
in the conditions of deviation from the given
parameters. As a management system, risk
management system should consist of risk purpose
development and risk capital investment,
determination of probability of event, designation of
risk level and volume, analysis of environment,
selection of risk management strategy and selection
of risk management tricks based on this strategy and
risk level reduction, implementation of purposeful
impact.
In the following layout, we described
organization of risk management directed to rational
concentration of all elements of the risk
management process in a single technology.
Thus, we cannot forget about the unpleasant
circumstances related to the aspects which lead to
the utilization of the project in risk condition during
the investigation of investment project management
problems in risk and uncertainty conditions and
arise from inaccurate and incomplete information
about project implementation and operation. These
risks include the ones detected during their research
process, as well as the ones suddenly emerged, and
this can be required in the duration of risk analysis
and their implementation and operation phase.
In that case, management of project risks
which embody themselves in practice can be
implemented in an efficient way. Implementation of
risk solution concept, which may occur in execution
of investment projects has to happen through project
risk analysis, evaluation and management according
to the integration of the procedural complex.
3 Introduction of Risk Management
in Solution of Strategic Problems of
Investment Project
Two case scenarios are possible in the process of
project management:
good understanding of project risks;
understanding of occurrence of new unexplored
project risks.
In the first case, relevant risk concept can be used
and in the second case, development of negative
scenarios. The success of the effective
implementation of relevant project is defined with
the professionalism of its manager in the first place
and its organizational structure (which comprises of
organizational forms, and organizational forms of
project management and organizational structure of
the project management) in the second place.
The concept of organizational structure of
project management is established by multi-level
hierarchical system of interconnected management
bodies, and it should be remembered that the
organizational form is a matter of organization of
mutual activity and relationships between all the
participants of investment process. [4. p. 190].
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Fig. 2: Layout of risk management organization
The important tool for project management is its
business plan and it is rational and also allowed to
have consistency of work organization proposed for
risk analysis and management and to include a
special section to business plan of the project. This
section may describe risks, their interaction
mechanism and general efficiency, risk protection
measures, interest of all parties in removing risk
danger, assessment of risk analysis experiments and
utilization of original offers they used, description
of the risk distribution structure between project
participants, special measure regarding insurance
policy or guarantees on risk aspects that need certain
conditions. [5. p. 131].
Project risk management methods are dynamic and
constantly evolving organisms in all hierarchical
levels, regional, local and mainly chain management
phases throughout implementation of the project,
their life cycles are meant to implement the
following six functions:
project selection;
planning;
implementation;
evaluation;
control;
outcome.
As risk is considered a financial category, it is
possible to impact risk level and volume by
financial mechanism, and this process can be
executed by means of financial management and
specially developed strategy. In that case, strategy
and tactics together create risk management, which
is specific risk management mechanism [6. p. 116].
From this point of view, it is important to define the
exact purpose of using risk management as a
management system, and removing or reducing the
risk. It largely depends on risk capital investment
procedures, determining the probability of event,
designation of risk level and scale, environmental
analysis, and accurate selection of risk management
strategy. It is possible to have a purposeful impact
on risk for especially this strategy based on the
selection of important risk management methods
and level reduction methods.
It should be noted that forecasting should be
accepted as one of the main functions of risk
management because it is possible to foresee a
certain event in this case. Alternative feature of
forecasting includes the establishment of financial
indicators and parameters that helps us identify
different development options for main financial
position according to pre-established tendencies of
the management facility.
3.1 Back Payment Time method
Investments in 4 projects with varying incomes over
the years amounts and annual incomes are given in
the table below. Project payback period by please
sort and suitable Specify the project.
Table 1. Four investment projects and 10 yearly
income
Years
Project A
Project
B
Project
C
Project
D
Investment in the
amount of (PB)
2000
2000
4000
4000
one
500
one
thousand
4000
800
2nd
500
one
thousand
200
800
3
500
200
200
800
4
500
200
200
800
5
500
200
0
800
6
500
one
hundred
0
800
7
500
one
hundred
0
800
8
500
one
hundred
0
800
9
500
one
hundred
0
800
10
500
one
hundred
0
800
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Solution
Back payment of the duration determination for the
purpose, yearly revenues investment to the amount
equal It is collected until it is collected, that is,
cumulative addition (CA) is done. Total annual
revenues investment equal to the amount is year
paid back gives the time.
Table 2: Four investment projects and 10 yearly
income
Years
Proj
ect
A
IT
(A)
Proj
ect
B
IT
(B)
Proj
ect
C
IT
(C)
Pro
ject
D
IT
(D)
Investm
ent the
amount
of (PB)
2000
0
2000
0
4000
0
4000
0
One
500
500
one
thous
and
one
thous
and
4000
4000
800
800
2
nd
500
one
thous
and
one
thous
and
2000
200
0
800
1600
3
500
1500
200
0
200
0
800
2400
4
500
2000
200
0
200
0
800
3200
5
500
0
200
0
0
0
800
4000
6
500
0
one
hundr
ed
0
0
0
800
0
7
500
0
one
hundr
ed
0
0
0
800
0
8
500
0
one
hundr
ed
0
0
0
800
0
9
500
0
one
hundr
ed
0
0
0
800
0
10
500
0
one
hundr
ed
0
0
0
800
0
Back
Payment
Time
( Year )
4
2nd
one
5
Priority
Sequenc
e
3
2nd
one
4
According to the results of the evaluation, the C
project with the lowest payback period of 1-year
alternative is chosen first. However, the business
will continue to invest in investments that will pay
for itself in 2 years. fund allocation if it will B
project also investment alternatives between should
think.
Back payment time of the method Some beneficial
the sides are:
Implementation and understanding simple a
is the method.
allows the selection of projects that pay
back the invested capital as soon as
possible, for risk and less uncertainty.
To ensure that the funds of the enterprises
with insufficient funds are returned in a
short time provides provided.
well short in time themselves paying to projects
priority by giving your active for necessary your
funds allow it to be created.
Back payment of the duration beneficial next to
them opposite objectionable the sides in has. This
drawback below by example is explained.
Static a method the one which... back payment time
method of money time its value consideration does
not take.
3.2 Net Present Value and IRR method
Net present value (NPV) is the present value of a
series of cash flows condensed into a single number.
If the net result is negative, the investment project
cannot be done, if it gives a non-negative result, it
can be done.
Sample Problem -1: A B C Company cost 350,000
$ the one which... a casting workshop will set up.
The company expects annual returns of $74,000
after establishing the foundry. To be established the
facility’s economic lifespan is 10 years. facility
economic lifespan finally scrap has no value. If the
expected profitability is 20%, according to NPV and
IRR methods of the company the implemented and
will not apply Please evaluate.
Solution
NPV Method:
Cash of entries the current value is found.
() = 310.282 $
NPV = P(A) − C
NPV = 310.282 – 350.000
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NPV = −39,718 $
NPV = −39718 <since it is 0 investment make
significant is not.
IRR method
In this method, the internal rate of return is found,
which equates revenues with expenses. try for it
make no mistake method applied; with revenues
your expenses net current values each other is
synchronized.
r 1 = For 0.21:
() = 299.996 $
PV = 299.996 – 350.000 = −50.004$
() = 357.642 $
PV = 357.642 – 350,000 = $ 7,642
PV = 7.642> 0 because r value should be
increased.
interpolation makes:
IRR(r) = 0.1666
IRR(r) < Since 0.20 investment make is not
meaningful.
Sample Problem-2: An investment of the project a
yearly establishment in the period of investment
expenditure is 22000 PB. The useful life of the
project is 4 years. 7500 per year during the
operational period The salvage value of this project
with a fixed income of 12500 PB at the end of its
useful life is 12500 PB. Production and care
activities within business yearly 2500 PB business
spending is done. The project management is in a
position to accept the 12% discount rate. This data is
according to the project NPV and IRR methods
Please evaluate.
Solution
NPV Method:
Cash entry:
A
A=614.428 PB
Cash output:
C
C=59.186 PB
NPV = A − C
NPV = 61448 – 59186 NPV = 2.262 PB
NPV = 2.262 > 0
because project acceptance can be done.
IRR Method:
1 = 0.10 inside :
A
A=32312 PB
C
C=59.850 PB
A − C = 64624 – 59.850 = 4774 PB
NPV ( r 1 ) = 4774 > 0 because r value should be
increased.
2 = 0.15 inside :
A
A=57118 PB
C
C=58274 PB
A − C = 57.118 – 58.274 = −1.156 PB
NPV ( r 1 ) = −1156 < 0 because r value should be
reduced.
Interpolation:
IRR(r)=0,14
IRR(r)=0,14 > 0.12
IRR(r)=0,14 > since it 's 0.12 project acceptance can
be done.
Excel from the program directly attempts to make
no mistake with IRR can be found.
Sample Problem-3: An investment of the project a
yearly establishment in the period of investment
expenditure is 22000 PB. The useful life of the
project is 4 years. 7500 per year during the
operational period The salvage value of this project
with a fixed income of 12500 PB at the end of its
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useful life is 12500 PB. Production and care
activities within business yearly 2500 PB business
spending is done. The project management is in a
position to accept the 12% discount rate. This data is
according to the project NPV and IRR methods
Please evaluate.
NPV Method:
Cash entry:
A
A=614.428 PB
Cash output:
C
C=59.186 PB
NPV = A − C
NPV = 61448 – 59186 NPV = 2.262 PB
NPV = 2.262 > 0
because project acceptance can be done.
IRR Method:
1
= 0.10 inside
:
A
A=32312 PB
C
C=59.850 PB
A − C = 64624 – 59.850 = 4774 PB
NPV ( r 1 ) = 4774 > 0 because r value should be
increased.
2
= 0.15 inside
:
A
A=57118 PB
C
C=58274 PB
A − C = 57.118 – 58.274 = −1.156 PB
NPV ( r 1 ) = −1156 < 0 because r value should be
reduced.
Interpolation:
IRR(r)=0,14
IRR(r)=0,14 > 0.12
IRR(r)=0,14 > since it 's 0.12 project acceptance can
be done.
Excel from the program directly attempts to make
no mistake with IRR can be found.
4 Conclusion
Main component charts of project risk management:
Risk identification
Development of relevant risk concepts;
Development of management system;
Risk monitoring;
Enhancement of management system;
Identification of risk possibility;
Making management decisions;
In the case of well-studied risks, the layout of
project management comprises of the following
sequence: project risk investigation; project audit
(this proves that risk is well studied); deciding on
either implementation of the project or refusal
thereof; development of relevant risk concept;
identification of unclear risk assumptions;
development of necessary control methods and risk
insurance happen if the assumption is right; then
efficient control is implemented in project
management process if control is possible.
In case of poorly studied risks, layout of project
management includes the following stages: project
risk investigation; project audit that proves that the
project is poorly studied; deciding on either
implementation of the project or refusal thereof;
development of worst-case scenarios and if the
worst scenario is unacceptable, then project is
rejected, otherwise, it is important to acquire
additional information for the development of
necessary control methods; if accurate information
is available, efficient control is implemented in the
project management process, and if not, the project
is rejected.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2022.19.99
Niyazi Hasanov, Tokhtaposha Akbulaeva,
Kamal Ahmadov, Akram Hasanzadeh
E-ISSN: 2224-2899
1138
Volume 19, 2022
The sequence of organization of works for presented
risk management and analysis includes:
Selection of an experienced team of experts;
Development of special question book and
expert reviews, and selection of risk
analysis techniques;
Identification of risk factors and their
importance.
Establishing a risk action mechanism model
and identification of the interconnection of
individual risks and the joint effect of their
influence;
Distribution of risks among project
participants;
Review of the results of the risk analysis in
the form of a specially-prepared report
(paper).
The rational approach principle is based on the
following methodological concepts.
Use of planned (forecasted) features that are
clearly expressed, understood, and
measured in quantity and quality.
Unambiguousness of designation of
responsibilities of project manager and all
participants in the process of solution of
raised issues.
Identification of interaction of main and
lead project elements to construct them and
facilitate project analysis and evaluation of
its implementation.
Enhancing the role of the person who makes
decisions in the selection of more efficient
measures to compensate and minimize
project risks in the project implementation
process.
As a management system, risk management
includes;
the process of determining risk and the
purpose of risk capital investments;
clarification of the probability of the event;
Identification of risk level and scale;
Environmental research;
Selection of risk management strategy;
Selection of risk management and reduction
methods, which are important for this
strategy;
Implementation of purposeful impact on
risk.
Mathematical models of forecasting applied in risk
analysis and management includes:
Growth curves (unlimited growth, limited
growth with bend point, limited growth
without bend point);
Aligned models (autoregressive models,
autoregressive integrated moving average
models, moving average models);
Econometric models (one-factor, linear-
factor).
References:
[1] N.A. Hasanov “Strategic business management”
Textbook. Baku. 2019. P.396.
[2] Shakaraliyev A.Sh. “International currency loan
system”. Baku. 2011. P.103.
[3] Bayramov A.I "History of World Economy",
Textbook. Baku-2010. p.239.
[4] Basayevayev RA "Urbanization: “Urbanization, city
economy and food problems" Baku, Azerbaijan.
2007. p. 190.
[5] Alirzayev A.G. "Concept and program of economic
development of Azerbaijan ". "The Land of Fire"
Baku. 2013. P.131
[6] Hasanov. "International business" Textbook. Baku-
2011. p. 116.
[7] Hasanov N.A "Management of ownership activity".
The Scientific and Methodological Council of the
Ministry of Education of the Republic of Azerbaijan
has been approved by Protocol No.11 dated
22.04.1997 of the "Economy and Management"
section. Baku, 1997 (course materials). p.129.
[8] Hasanov N.A Securities are the state's investment
source. Baku city, journal "Finance and
Accounting" 2010, No 1-2. p.63
[9] Hasanov N.A "Managing supervision and
coordination in management". Approved by Order
No. 922 dated 06.10.2000 of the Ministry of
Education of the Republic of Azerbaijan. Baku city,
2000 (textbook). p.69.
[10] Hasanov NA Problems of development and
modernization of investment strategy. (monograph)
Baku, 2013. p.103.
[11] Markowith. H. «Risk-Return Analysis» Moscow.
2016. p. 193 .
[12] Kleiner G.B., Tombovtsev V.L., Kochalov R.M.
"Enterprise in an unstable economic environment:
risk strategies, security” Publishing hause
«Economics». Moscow. 2010. p. 66.
[13] Pollak Yu.G., Filimonov V.A. "Strategic machine
modeling of communications" Moscow. From
Radio and communication. 2009. p.191.
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WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2022.19.99
Niyazi Hasanov, Tokhtaposha Akbulaeva,
Kamal Ahmadov, Akram Hasanzadeh
E-ISSN: 2224-2899
1139
Volume 19, 2022
