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News
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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Жизненные риски и низший класс в России

Вестник общественного мнения. Данные. Анализ. Дискуссии. 2019. Т. 128. № 1-2. С. 43–54.
Slobodenyuk E.

The paper addresses the issue of the low strata and their relation to the poor in contemporary Russia. To define low strata we employed the Weberian concept of life chances. These chances are realised or deprived in four major domains such as 1) economic conditions of life, 2) situation at work, 3) possibility of preserving and increasing human capital, 4) characteristics of consumption and leisure. Drawing on the RLMS-HSE data (2017) and the IS RAS monitoring data (2015), the paper concludes that low strata represent the low class. Despite this, the low strata and the poor overlap partly. This means that the area of social disadvantages in Russia is much wider than official estimates of poverty.

Research target: Social Sciences
Priority areas: economics sociology
Language: Russian
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Keywords: бедностьpovertysocial stratificationнизший классжизненные шансыlife chanceslow class социальная стратификация
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