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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.
June 3, 2026
Pocket Money, Personal Interest, and Family Practices: What Shapes Students Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
June 3, 2026
Creative Work as a Remedy for Burnout
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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Качественный сетевой анализ на практике: сравнение способов построения сетевых карт

Социология: методология, методы, математическое моделирование. 2023. № 56. С. 113–142.
Kim A.

This article is devoted to the application of qualitative network analysis on the example of studying the social interactions of young parents in migration. A conceptual description of qualitative network analysis, network visualization features, design of qualitative network research, comparison of two approaches to building network maps and an example of their analysis are presented. Qualitative network analysis is aimed at studying relationships in the network and consists of interpretative and structural components, which are implemented through interviews and building a network map. The two approaches to building network maps differ in that in one case, network maps are built by the researcher based on an interview, and in the other case, the construction of the map is delegated to the informant himself. The first method is limited by the interview data and is their structural visualization. The second method of building a network map by the informant himself creates additional resources for interpretation – contrasting narratives and visual structuring of the informant's interactions.

Research target: Sociology (including Demography and Anthropology
Language: Russian
DOI
Text on another site
Keywords: миграциясетевой анализmigrationродительствокачественные методыRussian migrantsnetwork analysisParentingqualitative methodsqualitative social network analysisкачественный сетевой анализnetwork visualizationnetwork mapsвизуализация сетисетевые картыроссийские мигранты
Publication based on the results of:
Development of network analysis in Russia: adaptation of theoretical and methodological approaches and practical application (2024)
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