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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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Genomic epidemiology of the early stages of SARS-CoV-2 outbreak in Russia

2020.
Komissarov A. B., Safina K. R., Garushyants S. K., Fadeev A. V., Sergeeva M. V., Ivanova A. A., Danilenko D. M., Lioznov D., Shneider O. V., Shvyrev Nikita, Spirin V., Glyzin D., Shchur V., Bazykin G. A.
The ongoing pandemic of SARS-CoV-2 presents novel challenges and opportunities for the use of phylogenetics to understand and control its spread. Here, we analyze the emergence of SARS-CoV-2 in Russia in March and April 2020. Combining phylogeographic analysis with travel history data, we estimate that the sampled viral diversity has originated from 67 closely timed introductions into Russia, mostly in late February to early March. All but one of these introductions came from non-Chinese sources, suggesting that border closure with China has helped delay establishment of SARS-CoV-2 in Russia. These introductions resulted in at least 9 distinct Russian lineages corresponding to domestic transmission. A notable transmission cluster corresponded to a nosocomial outbreak at the Vreden hospital in Saint Petersburg; phylodynamic analysis of this cluster reveals multiple (2-4) introductions each giving rise to a large number of cases, with a high initial effective reproduction number of 3.7 (2.5-5.0).
Research target: Medical and Health Sciences Biology Computer Science Mathematics
Priority areas: IT and mathematics
Language: English
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DOI
Text on another site
Keywords: SARS-CoV-2SARS-CoV-2COVID-19COVID-19
Publication based on the results of:
Study of population structure from modern and ancient genomes, study of evolutionary mechanisms with population genetics methods (2020)
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