Forecasting Economic Indicators by Applied Technologies of Data Processing
https://doi.org/10.21686/2413-2829-2021-1-124-132
Abstract
Today economic forecasts are necessary to identify possible goals of business and society development in general. Available models of information processes of economic system functioning differ by methods of mathematic tools usage. The degree of algorithm complexity with regard to their advantages and drawbacks cannot give an opportunity to choose one method without certain changes. For example, when the trend line is plotted usually only one economic indicator is used, however practice shows that in forecasting it is necessary to take into account several interdependent factors. Standard applied software products are rather expensive and to resolve the set tasks some additional instrumental means, tuning and users’ training are needed. The article proposes a method of plotting the trend line for several economic indicators simultaneously by using tools of widely spread software. The results of using the method for finding the common trend line for the rate of Euro to Russian ruble and the cost of oil barrel (3D) were shown. Apart from that the mathematic instrument for n-dimension case, which later could allow us to assess the possibility of changing several economic indicators at once and analyze their mutual impact.
About the Authors
R. R. Veynberg
Plekhanov Russian University of Economics
Russian Federation
Russian Federation
Roman R. Veynberg PhD, Assistant Professor, Assistant Professor of the Department for Informatics
36 Stremyanny Lane, Moscow, 117997
A. S. Skotchenko
Russian State University of Justice
Russian Federation
Russian Federation
Andrey S. Skotchenko PhD, Assistant Professor, Assistant Professor of the Department for Information Law, Computer Science and Mathematics
69 Novocheremushkinskaya Str., 117418, Moscow
A. A. Grigorev
HSE University
Russian Federation
Russian Federation
Anatoly A. Grigorev PhD, Assistant Professor of the Department for Information Systems and Digital Infrastructure Management
20 Myasnitskaya Str., 101000, Moscow
A. B. Yaroshyk
State University of Management
Russian Federation
Russian Federation
Anatoly B. Yaroshyk Doctor of Economics, Professor, Member of the Academy of Military Sciences, Professor of the Department for Finance and Credit
99 Ryazansky Prospekt, 109542, Moscow
References
1. Borisov R. S., Efimenko A. A. Optimizatsiya razmerov blokov elementarnykh zadaniy v zadachakh planirovaniya parallelnykh vychisleniy [Optimizing Sizes of Elementary Buildings in Tasks of Parallel Calculation Planning]. Prikladnaya informatika [Applied Information Science], 2018, Vol. 13, No. 3 (75), pp. 77–82. (In Russ.).
2. Ivanitskiy V. P., Tatyannikov V. A. Informatsionnaya asimmetriya na finansovykh rynkakh: vyzovy i ugrozy [Information Asymmetry on Finance Markets: Challenges and Threats]. Ekonomika regiona [Economy of Region], 2018, No. 14 (4), pp. 1156–1167. (In Russ.).
3. Kuklina O. K., Mikhaylova E. A., Yakhina A. S. Programmno-matematicheskoe obespechenie prognozirovaniya lokalnykh pokazateley [Software and Mathematic Support for Forecasting Local Indicators]. Vestnik Zabaykalskogo gosudarstvennogo universiteta [Bulletin of the Zabaykal’skiy State University], 2015, No. 11 (126), pp. 97–104. (In Russ.).
4. Litvishko O. V., Golikova O. M. Effektivnaya biznes-model kak osnova prirosta investitsionnoy stoimosti professionalnogo sportivnogo kluba [Efficient Business-Model as a Basis of Growing Investment Value of the Professional Sport Club]. Vestnik universiteta [Bulletin of the University], 2019, No. 5, pp. 159–166. (In Russ.).
5. Litvishko O. V., Keshchyan V. G. Finansovo-pravovye osnovy upravleniya investitsionnymi riskami professionalnogo sporta [Finance and Legal Basis of Managing Investment Risks in Professional Sport]. Vestnik Rossiyskogo ekonomicheskogo universiteta imeni G. V. Plekhanova [Vestnik of the Plekhanov Russian University of Economics], 2019, No. 1 (103), pp. 179–186. (In Russ.).
6. Khubulava N. M., Skotchenko A. S. Integrirovannaya model strategicheskogo rosta primenitelno k rossiyskim usloviyam (versiya 1) [Integrated Model of Strategic Growth as Applied to Russian Situation (Variant 1)]. Vestnik Universiteta Rossiyskoy akademii obrazovaniya [Bulletin of the University of the Russian Academy of Education], 2015, No. 4 (77), pp. 218–226. (In Russ.).
7. Das S. R., Ostrov D. N., Radhakrishnan A., Srivastav D. A New Approach to Goals-Based Wealth Management. SSRN Electronic Journal, 2018. DOI: 10.2139/ssrn.3117765
8. De Giorgi E. G. Loss Aversion with Multiple Investment Goals. Mathematics and Financial Economics, 2011, No. 5 (3), pp. 203–227. DOI: 10.1007/s11579–011–0057-y
9. De Giorgi E. G., Hens T. Prospect Theory and Mean-Variance Analysis: Does it Make a Difference in Wealth Management? Investment Management and Financial Innovations, 2009, No. 6 (1), pp. 122–129. DOI: 10.5167/uzh-50736
10. De Giorgi E. G., Mahmoud O. Naive Diversification Preferences and their Representation. SSRN Electronic Journal, 2016. DOI: 10.2139/ssrn.2864231
Review
For citations:
Veynberg R.R., Skotchenko A.S., Grigorev A.A., Yaroshyk A.B. Forecasting Economic Indicators by Applied Technologies of Data Processing. Vestnik of the Plekhanov Russian University of Economics. 2021;18(1):124-132. (In Russ.) https://doi.org/10.21686/2413-2829-2021-1-124-132
Views:
520
Email this article 