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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.
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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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The Physical and Geometric Properties of Human Transposon Stem–Loop Structures under Natural Selection

Биофизика. 2017. Т. 62. № 6. С. 857–864.
Poptsova M., Гречишникова Д. А.

Secondary RNA structures play an important role in transposition, in particular, in RNA recog- nition by transposon proteins. Previously, we found a conserved structure at the 3'-end of human transposons and proposed a hypothesis about the role of this structure in transposition. Although there is no similarity at the sequence level, the conserved position of this structure points to the fact that structural properties occur that are under positive natural selection. In this paper, the physical and geometric properties of stem-loop structures at the 3'-end of human transposons are identified and compared with properties of the structures of other genome regions. Each stem-loop structure was characterized by a set of ten characteristics: the Gibbs free energy, enthalpy, entropy, hydrophilicity, Shift, Slide, Rise, Tilt, Roll, and Twist. A model has been built using machine-learning methods, which recognizes stem-loop structures according to their physical and geo- metric characteristics with 94% accuracy. The most important parameters in the recognition model are hydrophilicity, enthalpy, Rise, and Twist. These properties of transposon structure are thought to be under positive natural selection.

Research target: Biology Computer Science
Language: Russian
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Keywords: машинное обучениеentropyэнтропияmachine learningслучайный лесMechanisms of retrotranspositiontransposonstem-loopsdinucleotide characteristicsGibbs free energyтранспозонретротранспозицияструктура стебель-петлясвободная энергия Гиббсахарактеристики динуклеотидовrandom forest
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