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June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
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Финансирование исследований и разработок в регионах: задачи, состояние, перспективы

Экономические и социальные перемены: факты, тенденции, прогноз. 2022. Т. 15. № 5. С. 135–152.
Климова Ю. О., Устинова К. А., Frolov I.

Financing the research and development sector is an important condition for growth of innovation activity in regions. However, the share of research and development (R&D) expenses in Russia has been decreasing over the last ten years. According to this indicator, Russia lags behind other countries – leaders of innovation development 3–4 times. The formation of funds to support research, scientific and technological, and innovation activities can contribute to the growth of the volume of R&D financing. The purpose of the study is to examine the organizational conditions for the formation of funds to support research, scientific and technological, and innovation activities. Using statistical data from Rosstat, we calculated the variants of growth of R&D costs provided that regional funds are created, the budget of which is formed by deductions from the revenues of organizations in the amount of up to 1.5%. We have identified groups of companies whose financial resources could be a source of formation of the funds budget. The article identifies the main directions and volume of use of finances accumulated in the funds. In the final part of the article we reviewed the support measures for companies that participate in filling the budget of the funds, and proposed adjustments to the main areas of incentives for organizations. The scientific novelty of the presented work lies in the implementation of a comprehensive study of the supporting funds for research, scientific and technological, and innovation activities in the RF constituent entities as a tool for the growth of R&D expenditures. The latter includes studying the issues of financial filling of the funds budget, determining the directions and calculating the possible volume of spending of accumulated funds on them, as well as adjusting the existing measures to encourage organizations to participate in financing the funds budget. The practical significance of the work lies in the fact that its results can serve as an economic justification and organizational support for the creation of regional funds by regional authorities. In addition, the materials of the study can be used to develop a separate federal law regulating the creation and operation of the funds in question.

Language: Russian
DOI
Text on another site
Keywords: инновациинаукаinnovationНИОКРregionинновационная активностьисследования и разработкиscienceresearch and developmentfundsфондыR&D Funding R&Dфинансирование НИОКР innovation activity регион
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