Assessment of Investment Attractiveness of RF Entities Using Artificial Intelligence
Difficult geopolitical situation in the world has made the issue of attracting private investment into the economy at the meso-management Level relevant for Russia Currently, the solution of this problem is impossible without monitoring the investment attractiveness of the subjects of the Russian Fe...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | Russian |
| Published: |
Government of the Russian Federation, Financial University
2025-05-01
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| Series: | Финансы: теория и практика |
| Subjects: | |
| Online Access: | https://financetp.fa.ru/jour/article/view/3603 |
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| Summary: | Difficult geopolitical situation in the world has made the issue of attracting private investment into the economy at the meso-management Level relevant for Russia Currently, the solution of this problem is impossible without monitoring the investment attractiveness of the subjects of the Russian Federation. The purpose of the study is to develop an adequate methodology for assessing the investment attractiveness of Russian regions. Based on competitive benchmarking techniques, the investment attractiveness of territories (within the country) is assessed in dynamics over a number of years. The results of a retrospective assessment conducted using the index method are deepened by cluster analysis. Also, the authors’ methodology allows us to assess not only the actual investment attractiveness of Russian regions, but also assumes the formation of a forecast. At the same time, the above tasks are solved with the help of artificial intelligence. The results of the retrospective assessment showed that in 2019-2022, Moscow and St. Petersburg were the pronounced leaders in the rating of investment attractiveness among the subjects of the Russian Federation. At the bottom of the rating (they did not rise above 71st place) on a regular basis were all the republics from the North Caucasus Federal District, as well as the Republic of Kalmykia and the Republic of Tyva, which are included, respectively, in the Southern Federal District and the Siberian Federal District. Based on the results of the cluster analysis, it can be seen that all Russian regions in 2019-2022 could be organized into three groups characterized by above-average, average and below-average levels of investment attractiveness. The quality of the formed cluster structure has improved over the entire analyzed period of time: the share of subjects of the Russian Federation with above-average investment attractiveness has almost doubled. The results of the (retro and prospective) assessment according to the authors’ methodology allow us to conclude that there are significant reserves for the growth of investment attractiveness of all Russian regions without exception. Based on the decomposition of its results, the leadership of the constituent entities of the Russian Federation will be able to develop measures to improve the effectiveness of the regional investment policy. |
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| ISSN: | 2587-5671 2587-7089 |