Priorities of State Support of the Russian Agriculture on Federal and Regional Levels under Volatile Economic Growth

The state support of the agriculture is increasing with the globalization of Russia, the need to shape level and instruments in accordance with the WTO requirements. For Russia, rational state support of agriculture is topical according to the domestic economic problems： insufficient financing in Russia, in the regions; degradation of production facilities; turnover acreage reduction; reduction in livestock; and according to the external economic factors： the accession to the WTO, the transition to the Common Economic Space, and subsequently, the Eurasian Economic Union, global change conditions in the global food market, an increase in the world population, increasing demand for food resources, a significant increase in food prices on world markets, the increased activity of Russian agricultural producers in the world markets of grain products; ensuring of economic stability systems, critical analysis of the tools and the effectiveness of economic policy. According to the IMF, the economic has slowed down despite the state support for agriculture should stay a priority in the government＇s economic policy, in the regions.

DAViS:a unified solution for data collection, analyzation,and visualization in real‑time stock market prediction

The explosion of online information with the recent advent of digital technology in information processing,information storing,information sharing,natural language processing,and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content.For example,a typical stock market investor reads the news,explores market sentiment,and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock.However,capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market.Although existing studies have attempted to enhance stock prediction,few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making.To address the above challenge,we propose a unified solution for data collection,analysis,and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles,social media,and company technical information.We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices.Specifically,we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices.Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93.Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance.Finally,our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.