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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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Results and Impact of National Foresight-Studies

P. 31–41.
Meissner D.

This paper presents evidence suggesting that countries that continuously conduct Foresight studies, and that integrate the results systematically in policy making and the development of supporting measures and programs, perform clearly better in the national innovative performance than other countries. In retrospect, the Foresight studies are also generally considered as something positive in most countries, with evaluations of these studies shows them to have had a significant impact on the structure of innovation, technology and science policy. For instance, the results of Foresight studies are frequently used to establish research and development priorities and to design R&D programmes. The correlational analysis and the evaluation studies thus together suggest that Foresight studies do have a supportive function and role on the innovation culture and awareness for innovation in a country.

Language: English
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Keywords: форсайт-исследованияForesight stidies
Publication based on the results of:
Forming effective science, technology and innovation policy:methods and instruments (2013)

In book

Science, Technology and Innovation Policy for the Future — Potentials and Limits of Foresight Studies
Science, Technology and Innovation Policy for the Future — Potentials and Limits of Foresight Studies
Dordrecht, L., Heidelberg, NY: Springer, 2013.
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