The article is devoted to the formation and implementation of national goals as a stage of the strategizing process. Subject of the study: analysis of current problems in the implementation of national goals for the period up to 2030 and beyond. The possibility of expanding the planning horizon in conditions of high uncertainty is considered. It is shown that improving the effectiveness of national goals should be carried out on the basis of the use of modern forecasting methods, as well as ensuring realistic planning, continuity of goals and structural and logical harmonization of the planning system. Based on the use of factor analysis and ranking methods, an assessment of the degree of influence of problems on the effectiveness of plans to achieve national goals was carried out. The directions of solving the most significant problems are substantiated.
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
