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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 creative, supportive atmosphere and innovative methods at the Centre for Sociocultural Research make it appealing to early-career scholars. Over years of working at HSE University, they grow into researchers and lecturers recognised both in Russia and abroad. Chief Research Fellow Zarina Lepshokova and Leading Research Fellow Ekaterina Bushina spoke about their journey at the centre and at HSE, their research, and the role of mentors in their academic success.

 

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Ценностные ориентации и практики социальной активности иногородних студентов Казани и Санкт-Петербурга

Социологические исследования. 2023. Т. 6. С. 123–135.
Ефлова М. Ю., Максимова О. А., Озерова К. А.

The structure of value orientations and the specifics of the implementation social activity practices of nonresident students studying in the leading universities of the two largest cities in Russia – Kazan Federal University (Kazan) and the Higher School of Economics in St. Petersburg (St. Petersburg) are analyzed. Based on the results of mass representative surveys, a comparative analysis of the studied parameters was made, a correlation was revealed between different types of values in the system of value orientations of students of two universities. A factor analysis has been carried out and factors characterizing the structure of social activity of nonresident students in the cities under consideration have been identified. Factor analysis by preferred areas of activity made it possible to identify three specific groups of respondents among Kazan Federal University students and four groups at the Higher School of Economics-St. Petersburg.

Language: Russian
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Keywords: ценностные ориентацииvaluesценностисоциальная активностьvalue orientationssocial activityиногородние студенты nonresident students
Publication based on the results of:
Сценарии социального включения региональной молодежи в условиях образовательной миграции (2023)
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