Journal of Environmental Treatment Techniques  
2019, Special Issue on Environment, Management and Economy, Pages: 960-965  
J. Environ. Treat. Tech.  
ISSN: 2309-1185  
Journal web link: http://www.jett.dormaj.com  
Modeling the Process of Portfolio Investment of  
Innovative Projects of High-Tech Products  
Igor L. Beilin, Vadim V. Khomenko, Ekaterina I. Kadochnikova, Nailya M. Yakupova  
Institute of Management, Economics and Finance, Kazan Federal University, Kazan, Russia  
Received: 13/09/2019  
Accepted: 22/11/2019  
Published: 20/12/2019  
Abstract  
The article proposes for the first time the use of the analytical segmentation method for the formation of investment portfolios of  
small innovative enterprises (MIP), based on a wide range of their own research projects. This is the MIP according to No. 209-ФЗ,  
which are supported, first of all, by the programs of the Foundation for the Promotion of the Development of Small Enterprises in the  
Scientific and Technical Sphere (Foundation for the Promotion of Innovations). These include the program "START",  
"Development", "Commercialization». The digital model realizes the possibilities in the on-line segmentation correction mode and  
the share of investment of portfolio projects depending on changes in the financial, technical, economic, technological and  
operational characteristics of an innovative product. On the basis of network planning methods, a model of the production program of  
a small innovative enterprise has been formed; a critical path and value of investment along a critical path have been determined. The  
new economy is based on knowledge and relies primarily on intellectual capabilities, reducing dependence on natural resources.  
Nowadays, knowledge, skills and experience are just as important in an increasingly interconnected world economy as any other  
economic resources for success. This globalization is based on technological innovations that change business models in all sectors of  
the economy through the virtualization of operating systems. Many innovative enterprises actively use servers, the latest data storage  
devices and network resources involved in digitizing key business processes, such as marketing, trading, manufacturing, customer  
service, communications, and much more.  
Keywords: Economics, Econometrics, Economic and Mathematical Modeling, Economic Theory, Regional Economy, Innovation  
Management  
1
modern economic system, there is the observation that  
1
Introduction  
technological changes that compete with newly created  
innovative enterprises are usually not fundamentally new or  
complex from a technological point of view. Successful  
implementation of a viable start-up in the digital economy  
requires taking into account the continuous transformation of  
the rules of competition and the development of new  
proposals.  
Technological innovations, especially digital technologies  
such as social networks, mobile, analytical and cloud  
applications have become the driving force of innovative  
industries and the development of the majority of both large  
and small companies. All sectors of the economy received  
rapid transformation and integration of technologies in order  
to increase economic efficiency, reduce costs, create new  
incomes, as well as improve cooperation and innovation  
infrastructure (6-9, 11, 12). At the enterprise level, the digital  
economy is not limited to the IT department, but shows its  
value throughout the organization, in all aspects of business  
processes. For example, the Internet of Things has become a  
key driver of productivity, competitiveness and growth.  
Among information and communication technologies in  
innovative enterprises, services dominate (74%), mobile  
In an increasingly closely connected global environment,  
digital technologies have become important factors in  
productivity, innovation and competitiveness in every sector  
of the economy. In the next 3-5 years, the adoption of  
“
smart” and Internet technologies, such as the Internet of  
Things, will constantly change all aspects of the economy.  
These are manufacturing, financial services, healthcare,  
transportation, basic services and cities, as well as the media  
and creative industries (1-5). Additionally, entrepreneurial  
potential creates new digital innovations and industries that,  
in turn, contribute to economic growth and social  
development.  
Computational  
and  
communications  
technologies, which are the technological foundations of the  
Internet, have developed gradually over the past few decades.  
Despite the fact that this development is strategic and  
fundamental, basic digital technologies do not differ much  
from the innovations that modern start-ups rely on. In the  
Corresponding author: Igor L. Beilin, Institute of  
Management, Economics and Finance, Kazan Federal  
University, Kazan, Russia, E-mail: i.beilin@rambler.ru.  
9
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Journal of Environmental Treatment Techniques  
2019, Special Issue on Environment, Management and Economy, Pages: 960-965  
technologies and data analysis tools (70%), social media  
number of potential buyers is calculated, and, accordingly,  
the required production capacity;- channels through which  
the distribution and sale of goods or services will be carried  
out, and which provide information on how to form a sales  
network; - market resilience, with which you can determine  
to what extent it is advisable to load the company's capacity;-  
profitability, reflecting the degree of profitability of the  
organization on a specific market segment;- compatibility of  
the market segment with the market of the main competitors,  
on the basis of which it is possible to make an assessment of  
the high or low potential of competitors, and also to decide  
whether to incur additional costs, focusing on this segment;-  
Assessment of the working experience of certain personnel of  
the organization (sales, production or engineering) in a  
specific market segment, as well as the adoption of  
appropriate measures;- competitiveness of the selected  
segment (22, 23, 35).  
(
(
62%) and customer relationship management tools (CRM)  
60%). Digital technologies such as e-learning tools,  
enterprise  
resource  
planning  
(ERP),  
e-commerce  
applications, and virtualization of economic systems are also  
becoming increasingly common in business operations.  
They increase enterprise productivity, allow employees to  
work more efficiently and use time, help reduce production  
costs and increase market share in a competitive environment  
(
i.e., increase sales and / or profits), help to stimulate  
innovative processes in MIP (10, 13, 14, 34). High-tech small  
innovative enterprises (MIP) of the polymer profile create  
twice as many new jobs and increase their income 15% faster  
than enterprises of other directions. n addition, their use of  
digital technologies can create additional non-ICT jobs  
through cost savings and increased profits (15-19). As the  
world economy becomes more and more computerized, and  
access to the latest technologies is expanding, the digital  
skills of scientific and technical staff and entrepreneurs can  
be a decisive factor for success in the new knowledge  
economy. In order to optimize the benefits from the  
introduction of digital technologies and to expand the scale of  
production activities, both at the regional level and at the  
national and international levels, organizations need  
employees with a wide range of diverse skills.  
3
Results and Discussion  
In the context of the relevance of finding economic  
models for managing the sustainable development of a  
regional petrochemical cluster, we will produce a practical  
implementation of analytical segmentation using the example  
of a small innovative polymer enterprise Plastic + based on  
the results of our own scientific and technical research (25-  
2
8, 30). The company develops ten innovative projects X =  
2
Methods  
(x1 - x10), each of which is characterized by six signs: Y =  
(y1 - y6): y1 is the valuation of the innovative product; y2 is  
the net present value (NPV); y3 is the return on assets; у4–  
modified internal rate of return (MIRR); y5 is the amount of  
investment (IC), y6 is the discounted payback period (DPP).  
Five investors are interested in the development of projects: Z  
= (z1 - z5). The degree of manifestation of the characteristic  
"y" in the innovation project "x" is expressed by the matrix  
"R" (Table 1) and investor's preference "z" in one or another  
characteristic "y" by the matrix "Q" (Table 2). On the basis of  
the Leung algorithm, in a probabilistic form, we express the  
interest of investors “z” in innovative projects “x”, which is  
presented in the form of a matrix “A” (Table 3). Calculations  
are made in MS Excel software when importing data with  
“update every time you open a file”.  
Analysis of the segmentation criteria is a mandatory  
procedure before the firm enters the segment. To this end,  
marketers conduct preliminary marketing research, and then  
examine the information obtained by consistently considering  
the segmentation criteria. If at least one criterion has received  
an unambiguously negative answer, access to this segment  
will be fraught with significant risk and one must either  
refuse this segment or take preventive measures to  
compensate for the weakness identified. As a rule, several  
prospective segments are considered simultaneously and the  
segment that received the best expert assessment for  
compliance with the segmentation criteria becomes the target  
segment of the firm (20, 21, 24, 33). The main segmentation  
criteria include:- segment capacity, on the basis of which the  
Table 1: Degree of manifestation of the characteristic "y" in the innovation project "x"  
y1 y2 y3 y4 y5  
0,7 0,4 0,8 0,5  
y6  
1
x1  
x2  
x3  
x4  
x5  
x6  
x7  
x8  
x9  
x10  
1
0,3  
0,4  
0,2  
1
0,35  
0
0,2  
1
0,2  
0,1  
0,7  
1
0
0
0
0,9  
1
0,1  
1
0,3  
0,1  
0,4  
0
0,5  
0,4  
0,9  
0,3  
0,9  
0,5  
1
R=  
1
0,2  
0,5  
0,5  
0,1  
0,5  
0,7  
0
0,3  
0,5  
0,3  
0,4  
0,5  
0,5  
1
0,4  
0,6  
0,8  
0,6  
0,7  
0
0,1  
0,7  
1
9
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Journal of Environmental Treatment Techniques  
2019, Special Issue on Environment, Management and Economy, Pages: 960-965  
To determine the separation threshold "L", projects from  
the set of "" for various portfolios "" on the basis of pairwise  
conjunction of columns, we build an auxiliary matrix "W"  
threshold for dividing “L” innovative projects by investment  
portfolios (Table 5). Such projects, the significance of which  
for investors is above a certain threshold, constitute the  
corresponding investment portfolios "M" (Table 6).  
(
Table 4). Through the operation “min max min” we find the  
Table 2: Degree of manifestation of investor preference "z" in the characteristics of the innovative project "y"  
z1  
1
z2  
0,7  
0,3  
0,1  
0,5  
0,9  
1
z3  
0,1  
1
z4  
0,5  
0,2  
0,36  
0,5  
0,3  
1
z5  
0,5  
0,1  
0,7  
0,9  
1
y1  
y2  
y3  
y4  
y5  
y6  
0,4  
0
Q=  
0,8  
0,9  
0,4  
0,4  
1
1
0
0,5  
Table 3: Investor interest "z" in innovation projects "x"  
z1  
z2  
z3  
z4  
z5  
x1  
x2  
x3  
x4  
x5  
x6  
x7  
x8  
x9  
x10  
0,586  
0,610  
0,208  
0,514  
0,622  
0,560  
0,565  
0,664  
0,413  
0,611  
0,636  
0,414  
0,554  
0,714  
0,638  
0,617  
0,813  
0,568  
0,557  
0,721  
0,555  
0,686  
0,538  
0,568  
0,587  
0,623  
0,443  
0,571  
0,670  
0,553  
0,530  
0,373  
0,629  
0,617  
0,524  
0,435  
0,691  
0,381  
0,491  
0,516  
0,584  
0,481  
0,600  
0,696  
0,549  
0,623  
0,652  
0,689  
0,603  
0,600  
A=  
Table 4: Auxiliary matrix “W” based on pairwise column conjunction  
0
0
0
0
0
0
0
0
0
0
,586  
,414  
,208  
,514  
,622  
,560  
,565  
,568  
,413  
,611  
0,555  
0,414  
0,538  
0,568  
0,587  
0,617  
0,443  
0,568  
0,557  
0,553  
0,530  
0,373  
0,538  
0,568  
0,524  
0,435  
0,443  
0,381  
0,491  
0,516  
0,530  
0,373  
0,600  
0,617  
0,524  
0,435  
0,652  
0,381  
0,491  
0,516  
0,584  
0,481  
0,208  
0,514  
0,549  
0,560  
0,565  
0,664  
0,413  
0,600  
W=  
Table 5: Threshold “L” of innovation projects on investment portfolios  
L
z1  
z2  
z3  
z4  
z5  
0
,568  
0,622  
0,617  
0,568  
0,652  
0,664  
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Journal of Environmental Treatment Techniques  
2019, Special Issue on Environment, Management and Economy, Pages: 960-965  
To address the issue of the size and sequence of financing  
of formed investment portfolios, it is possible to use the  
network planning method. In this context, it is proposed  
instead of the duration of the work to indicate the amount of  
funding in projects. As an example, let us present the network  
schedule of the largest investment in terms of value: from the  
Plastic + enterprise of the M5 portfolio (Fig. 1) of eight  
projects. With this method, the circle of the network,  
indicating the project number, is divided into four sectors. In  
the upper sector, the project number is fixed, in the left - the  
least possible financing for its implementation, in the right -  
the largest financing under the development option with  
maximum costs (29, 31, 32). In the lower sector indicates the  
reserve investment of this innovative project. In brackets are  
indicated the full and free reserve of financing.  
According to calculations, the results of which are  
presented on this network graph, the critical path is: (1.3)  
(3.4) (4.5) (5.8). The value of investment on the critical path  
amounted to 19.16 million rubles. Figures 2 in parentheses  
indicate the number of the innovative project through which  
the project is most heavily funded from the stage of the  
finished complex of target products of the enterprise. The  
calculation starts with the final project, since its potential is  
equal to 0. In the lower sector of the last item, a dash is  
indicated in brackets, 0 is written to the right and a transition  
to the next event is made.  
Table 6: Formation of investment portfolios "M"  
М1  
x1  
x2  
-
М2  
x1  
-
М3  
-
М4  
-
М5  
x1  
-
x2  
-
-
-
x3  
x4  
-
x3  
x4  
-
-
x4  
x5  
x6  
x7  
x8  
-
x4  
x5  
x6  
-
x5  
-
-
x6  
x7  
x8  
x9  
x10  
-
x7  
-
x8  
-
x8  
x9  
-
-
x10  
x10  
-
Figure 1: Solution graphically (sectoral method) of network investment planning of the M5 portfolio of the innovative enterprise Plastic +  
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Journal of Environmental Treatment Techniques  
2019, Special Issue on Environment, Management and Economy, Pages: 960-965  
Figure 2: Solution by the method of network investment planning potentials of the M5 portfolio of the innovative enterprise Plastic +  
their own scientific and technological research and allows the  
Summary  
Information and communication analytics improve the  
4
use of a large set of modern, yet easily accessible software.  
quality of customer service by assessing the capacity of a  
potential market through demographic and transactional  
evaluation. It helps in determining the success rates of  
marketing activities and new products, product design and  
marketing communications. Social media creates additional  
opportunities for enterprises to maximize their presence in the  
market while optimizing marketing expenses. Banks are  
increasingly using differentiated industry formats to better  
target different customer segments using analytical network  
management. The market requires multichannel interaction  
penetrating into all areas. Consequently, simultaneous  
transactions will become commonplace in the future, when a  
consumer can begin transactions on one channel and continue  
with the next steps on others.  
6 Acknowledgements  
The work is performed according to the Russian  
Government Program of Competitive Growth of Kazan  
Federal University.  
References  
1. Isard W, Han SH. Mediation Analysis: The Use of Regional  
Science Methods to Resolve Conflicts, Peace Economics. Peace  
Science and Public Policy.2008;13(2):1–13.  
2
.
Isard W. Industry-Services Complex Analysis for Poverty  
Region Development (Realistic Development Theory for  
Regions Emerging from Poverty), Peace Economics. Peace  
Science and Public Policy.2005;11(1): ISSN (Online) 1554-  
8
597.  
3
4
.
.
Isard W. A Jordan/West Bank Development Proposal. Peace  
Economics. Peace Science and Public Policy.2004;10(2).  
Isard W, Hara T. The Old City of Jerusalem as a Tourist  
International Magnet: An Initial Proposal for a First Step  
Cooperation in the Middle East. Peace Economics, Peace  
Science and Public Policy.2002;8(4):46-59.  
5
Conclusions  
Digital technology is at the center of excellence in  
organizational operations, especially in the current global  
market. They are rapidly changing traditional economic  
models, the transition to the "figure" is a significant  
transformation experienced by all production sectors today,  
and especially with the innovation component. “Adapt or die”  
5
.
Nilesh N, Karnik Jerry M. Mendel. Operations on type-2 fuzzy  
sets. 2000;4.  
6. Zadeh LA. Toward a perception-based theory of probabilistic  
reasoning with imprecise probabilities. InSoft Methods in  
Probability, Statistics and Data Analysis 2002 (pp. 27-61).  
Physica, Heidelberg.  
-
can be considered the motto of the digital economy, since a  
paradigm shift in consumer behavior also contributes to the  
growth of competitive threats and heightened risks in the  
market. Opportunities for growth in investment in innovative  
enterprises are endless and enormous, because the results can  
be products with a unique set of properties and completely  
new categories of services. The economic model of analytical  
segmentation presented and tested in this article considers  
projects from the point of view of investing in the transfer  
and commercialization of high-tech innovations based on  
7
.
Beilin IL. Economic-mathematical modeling of the total costs of  
innovative chemical enterprise methods of fuzzy set theory.  
Journal of Engineering and Applied Sciences.2017;12(19):4865-  
4
869.  
8
.
Beilin IL, Khomenko VV. Theoretical bases of project  
management in conditions of innovative economy based on  
fuzzy modeling. InJournal of Physics: Conference Series 2018  
May (Vol. 1015, No. 3, p. 032013). IOP Publishing.  
9
64  
Journal of Environmental Treatment Techniques  
2019, Special Issue on Environment, Management and Economy, Pages: 960-965  
9
1
1
.
Beilin IL. Economic optimization in chemical enterprises.  
International Journal of Economic Perspectives. 2017;11(4):670-  
32. Muyambiri B, Chabaefe NN. The Finance–Growth Nexus in  
Botswana: A Multivariate Causal Linkage. Dutch Journal of  
Finance and Management.2018;2(2):03.  
33. Barreto DM, Alturas B. Quality-in-use app evaluation: case of a  
recruitment app for Portuguese SMEs. Quality-in-use app  
evaluation: case of a recruitment app for Portuguese SMEs.  
2018;1.  
34. Radhy ZH. Application of Multiply Regression Linear Model  
and New Technology Method in Estimating Learning and  
Education of Students. International Electronic Journal of  
6
77.  
0. Inuiguchi M, Tanino T. Portfolio selection under independent  
possibilistic information. Fuzzy sets and systems. 2000 Oct  
1
;115(1):83-92.  
1. ErdalKayacan PhD. Candidate. Contributions to Type-2 Fuzzy  
Sets. Theory and Applications in Control Engineering and  
Robotics. 2009;10.  
1
1
2. Tardy Y. Petrology of laterites and tropical soils. AA Balkema;  
2
010.  
Mathematics  
Education.2019;14(1):87-90.  
3. Dimova L, Sevastjanov P, Sevastianov D. Fuzzy Capital  
Budgeting: Investment Project Valuation and Optimization.  
Chenstohova Tech. Univercity Proceedings. 2001.  
4. Zhou SM, John R, Chiclana F, Garibaldi JM. New type-2 rule  
ranking indices for designing parsimonious interval type-2 fuzzy  
logic systems. In2007 IEEE International Fuzzy Systems  
Conference 2007 Jul 23 (pp. 1-6). IEEE.  
https://doi.org/10.12973/iejme/3978.  
35. Merkibayev T, Seisenbayeva Z, Bekkozhanova G, Koblanova A,  
Alikhankyzy G. Oppositions in the conceptual and linguistic  
category of time. Opción.2018;34(85-2):116-148.  
1
1
1
1
1
5. Zopounidis C, Pardalos PM, Baourakis G. Fuzzy sets in  
management, economics, and marketing. world Scientific; 2001.  
6. Hua Wang. Fuzzy Control Systems Design and Analysis: A  
Linear Matrix Inequality Approach. 2011.  
7. Zimmerman H-J. Fuzzy Sets Theory -and Its Applications. -  
Kluwer Academic Publishers, 2001.  
8. Zobel CW, Khansa L. Quantifying Cyberinfrastructure  
Resilience  
against  
Multi-Event  
Attacks.  
Decision  
Sciences.2012;43(4).  
1
2
2
2
2
2
2
2
9. Rose PR. Risk Analysis and Management of Petroleum  
Exploration Ventures American Association of Petroleum  
Geologists. Methods in Exploration Series.2001;12.  
0. Sandler U, Tsitolovsky L. Neural Cell Behavior and Fuzzy  
Logic: The Being of Neural Cells and Mathematics of Feeling.  
Springer Science & Business Media; 2008 Sep 23.  
1. Takafumi N, Fminori T, Kamran MN, Bernardino MC,  
Alessandro PF. Practical Equations for the Elatic Modulus of  
Concrete. ACI Structural Journal. 2009 Sep;5:106-108.  
2. EFFATI S, SADOGHI H, SABERI Z. First Joint Congress on  
Fuzzy and Intelligent Systems Ferdowsi University of Mashhad,  
Iran 29-31 Aug 2007.  
3. LeSage JP, Fischer MM. Spatial growth regressions: model  
specification, estimation and interpretation. Spatial Economic  
Analysis. 2008 Nov 1;3(3):275-304.  
4. LeSage JP, Parent O. Bayesian model averaging for spatial  
econometric  
models.  
Geographical  
Analysis.  
2007  
Jul;39(3):241-267.  
5. Kleibergen F, Paap R. Generalized reduced rank tests using the  
singular value decomposition. Journal of econometrics. 2006 Jul  
1
;133(1):97-126.  
6. Nwogugu M. Decision-making, risk and corporate governance:  
A critique of methodological issues in bankruptcy/recovery  
prediction models. Applied Mathematics and Computation. 2007  
Feb 1;185(1):178-196.  
2
7. Balcaen S, Ooghe H. 35 years of studies on business failure: an  
overview of the classic statistical methodologies and their related  
problems. The British Accounting Review. 2006 Mar  
1
;38(1):63-93.  
2
2
3
3
8. Østergaard CR, Park E. What makes clusters decline? A study  
on disruption and evolution of a high-tech cluster in Denmark.  
Regional Studies. 2015 May 4;49(5):834-849.  
9. Thomas LC.  
A survey of credit and behavioural scoring:  
forecasting financial risk of lending to consumers. International  
journal of forecasting. 2000 Apr 1;16(2):149-172.  
0. Tingting J. Consumer credit delinquency and bankruptcy  
forecasting using advanced econometric modeling. MPRA  
Paper. 2006;3187.  
1. Mcleay S, Omar A. The sensitivity of prediction models to the  
non-normality of bounded and unbounded financial ratios.  
British Accounting Review. 2000;32:213-230.  
9
65