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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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The Heterogeneous Character of Russian Poverty Through the Prism of the “Deprivation” and the “Absolute” Approaches

Sociological Research. 2015. Vol. 54. No. 1. P. 20–40.
Tikhonova N. E., Slobodenyuk E.

The article proposes a new approach to the analysis of poverty. Along with the criterion of poverty that is linked to people’s incomes the authors propose to include criteria based on the analysis of people’s deprivations. Formally their incomes may be higher than the subsistence minimum, but their actual situation in life may put them in a difficult position. The new approach to the problem of poverty dictates the need to change the priorities of state social policy.

Research target: Political Science, International Relations, and Public Administration Economics and Management Sociology (including Demography and Anthropology
Priority areas: economics sociology state and public administration humanitarian
Language: English
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Keywords: бедностьpovertyабсолютная бедностьотносительная бедностьabsolut povertyrelative povertypoverty in Russiaбедность в России
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