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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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Dynamics of senses of new physics discourse: Co-keywords analysis

Journal of Informetrics. 2022. Vol. 16. No. 1. Article 101245.
Katchanov Y. L., Markova Y.

The paper presents a longitudinal analysis of the evolution of new physics keywords co-occurrence patterns. For that, we explore the documents indexed in the INSPIRE database from 1989 to 2018. Our purpose is to quantify the knowledge structure of the fast-growing subfield of new physics. The development of a novel approach to keywords co-occurrence analysis is the main point of the paper. In contrast to traditional co-keyword network analysis, we investigate structures that unite physics concepts in different documents and bind different documents with the same physics concepts. We consider the structures that reveal relationships among concepts as topological and call them “physics senses”. Based on the notion of trajectory mutual information, the paper offers clustering of physics senses, determines their period of life, and constructs a classification of senses’ “authority”.

Research target: Social Sciences Other social sciences
Language: English
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Keywords: KeywordsNew PhysicsCo-occurrence analysisINSPIRE
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