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This study reveals the approach to scaling socio-economic indicators to ensure economic security through regional budget expenditures to the GRP ratio example. Indicator choice is conditioned by the necessity to determine the degree of the federal center's rational influence on the regional strategic goals of sustainable development. The study aims to develop and test the system for assessing the dynamics of Russian socio-economic development based on the authors' interpretation of the scaling factor values. The main research method is scaling, which provides additional perspectives reflected by preserving proportions when changing the target parameters. The new method's effectiveness is confirmed by calculating the scaling factor. Its value interpretation gives a tool for assessing the effectiveness of the strategy development system and its economic security. The study's relevance is due to adaptation to global transformations based on the management system's capability to act under various crisis scenarios and make anti-crisis decisions important for the Russian economy. The findings improve the basis for implementing a sustainable strategic planning system and strengthening national security in the COVID-19 pandemic. The findings make it possible to predict the further evolution of the relationships between indicator groups in order to increase the role of per capita budgetary expenditures in GRP. Doi: 10.28991/esj-2022-SPER-08 Full Text: PDF

An effective and competitive investment policy requires improvements to the existing tools. The ongoing COVID-19 crisis requires understanding as to how the recovery processes should be implemented. This study aims to develop a model for determining the autonomous expenditure multiplier (AEM) values, considering the investment accelerator action. The scientific novelty consists of proving that the AEM is not only an effects enhancer of the government and private investment, but also a tool to specify on the regional industrial map of Russia where investment projects will allow significant economic growth. The work's practical significance consists of determining the possibility of applying the AEM as a tool to improve investment efficiency. The key research method was paired linear regression analysis. Based on the developed model, the AEM values for the economies of the five Central Federal District regions are calculated. Additionally, authors provide an explanation on how AEM values correlate to regional economic specialization. For example, atypically low AEM values for Moscow can be explained by high daily workforce movement among Moscow and the Moscow region. The information support difficulties of the proposed model are defined, and the directions to overcome them are proposed. Empirical results show significant differences in the AEM values of the researched regions, and that the AEM as a management tool for interregional investment distribution will help to invest the limited resources of both the state and private businesses more effectively. Additionally, authors establish that the achieved results fall in line with real macroeconomic situations within the regions, which proves that the proposed model reflects real world processes. Primary beneficiaries and end users of the study are government agencies, state-owned corporations, and members of broader scientific community.

The crisis phenomena irregularity due to the COVID-19 pandemic requires new, to a certain extent, non-routine tools for their settlement, namely the action effect enhancers of the state and private investors to develop the economy in conditions of shrinking volumes of available financial resources. The paper proposes the Investment Multiplier (IM) as such a tool. The author's model was generated based on a detailed description of developing multiplier processes to analyze its action. The study aims to estimate the IM values of the economies of Kurgan, Sverdlovsk, and Chelyabinsk, the most industrially developed Russian regions, and identify the main points of their economic growth. The key research method was regression analysis. The present study establishes interconnectivity between marginal propensity and savings, marginal propensity and imports, marginal tax rate, and investment accelerator, and their influence on the regional multiplier. From a position of investment multiplier, the authors try to narrow the research gap in how investment policies influence the economic growth of the Russian regions. The study results determine IM values for the analyzed regions, identifying their differences and reasons. The key points of economic growth in these regions were identified. The scientific novelty involves applying changes in regional multiplier processes' dynamics to determine the key development points. The practical significance is that the IM value is an analytical tool that will identify existing key points of regional economic development and, by managing the IM value, create new points for investments with a large-scale multiplier effect, thus developing the regional economy. Doi: 10.28991/ESJ-2022-06-02-05 Full Text: PDF

This paper identifies hidden patterns between trading volumes and the market value of an asset. Based on open market data, we try to improve the existing corpus of research using new, innovative neural network training methods. Dividing into two independent models, we conducted a comparative analysis between two methods of training Proximal Policy Optimization (PPO) models. The primary difference between the two PPO models is the data. To showcase the drastic differences the PPO model makes in market conditions, one model uses historical data from Binance trading history as a data sample and the trading pair BNB/USDT as a predicted asset. Another model, apart from purely price fluctuations, also draws data on trading volume. That way, we can clearly illustrate what the difference can be if we add additional markers for model training. Using PPO models, the authors conduct a comparative analysis of prediction accuracy, taking the sequence of BNB token values and trading volumes on 15-minute candles as variables. The main research question of this paper is to identify an increase in the accuracy of the PPO model when adding additional variables. The primary research gap that we explore is whether PPO models specifically trained on highly volatile assets can be improved by adding additional markers that are closely linked. In our study, we identified the closest marker, which is a trading volume. The study results show that including additional parameters in the form of trading volume significantly reduces the model's accuracy. The scientific contribution of this research is that it shows in practice that the PPO model does not require additional parameters to form accurately predicting models within the framework of market forecasting. Doi: 10.28991/ESJ-2023-07-04-012 Full Text: PDF