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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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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.
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Региональные различия в образовательных результатах российских учащихся в PISA-2018: социопространственный подход

Образовательная политика. 2022. Т. 3. № 91. С. 49–61.
Adamovich K.

The results of the international study PISA (Programme for International Student Assessment) have long been in the focus of the educational policy of many countries and become the basis for recommendations to educational organizations. However, often these recommendations do not consider the within-country context and regional differences in the landscape of educational inequality.

This study is done in the framework of the sociospatial concept of Pierre Bourdieu. The aim of the article is to identify and describe the scale of regional differences in the educational results of Russian school students in PISA-2018 and their relationship with the cultural and social capital of students and those indicators of the social space of the school that are the focus of the PISA recommendations for educational policy. Data analysis was performed using a series of linear regressions with cluster standard error correction.

The results show that the landscapes of educational inequality in Russian regions vary. Regional differences in reading literacy of students were comparable with 2.4 years of study, in mathematical literacy - with 1.6 years of study. For indicators of the cultural and social capital of students, the largest regional differences are observed both in the distribution of these capitals and in their connection with the educational results of students. The indicators of social space vary less between regions, however, the patterns of their relationship with the results are also different. Finally, the practices of teaching reading that students from different subjects of the Russian Federation face are almost identical. But even in this case, their similar use may not show the expected increase in points in the case of a particular region.

The results obtained cast doubt on the effectiveness of the universal conclusions and recommendations of PISA, which do not consider local contexts, especially in the case of Russia as a country with a very extensive and diverse territory. This creates new challenges related to the reassessment of the validity of nationwide recommendations made based on the analysis of data from a small number of regions.

Research target: Education
Language: Russian
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Keywords: социальный капиталPISAчитательская грамотностьsocial capitalобразовательное неравенствокультурный капиталcultural capitalPISAregional differences educational inequalitynumeracyreading literacyрегиональные различияматематическая грамотностьschool social spaceсоциальное пространство школы
Publication based on the results of:
Experimental evaluation of practices for improving the online learning effectiveness (2022)
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