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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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Continuous and Distributed Systems II. Theory and Applications

Vol. 30: Topological Classification of Geodesic Flows on Revolution 2-Surfaces with Potential. Springer, 2015.
Kantonistova E., Фоменко А. Т.
Language: English
Keywords: Integrable Hamiltonian systemsSurfaces of revolutionFomenko-Zieschang invariant
Continuous and Distributed Systems II. Theory and Applications
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