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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.
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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Increasing the performance of a Mobile Ad-hoc Network using a game-theoretic approach to drone positioning

Vestnik Sankt-Peterburgskogo Universiteta, Prikladnaya Matematika, Informatika, Protsessy Upravleniya. 2019. Vol. 15. No. 1. P. 22–38.
Blakeway S., Gromov D., Gromova E., Kirpichnikova A., Plekhanova T. M.

We describe a novel game-theoretic formulation of the optimal mobile agents’ placement problem which arises in the context of Mobile Ad-hoc Networks (MANETs). This problem is modelled as a sequential multistage game. The definitions of both the Nash equilibrium and cooperative solution are given. A modification was proposed to ensure the existence of a Nash equilibrium. A modelling environment for the analysis of different strategies of the players was developed in MATLAB. The programme generates various game situations and determines each player move by solving respective optimisation problems. Using the developed environment, two specific game scenarios were considered in detail. The proposed novel algorithm was implemented and tested using Network Simulator 3 (NS-3). The results show that the proposed novel algorithm increases network performance by using game theory principles and techniques.

Priority areas: IT and mathematics mathematics
Language: English
Full text
DOI
Keywords: graphsdynamic gamesnetwork simulatorsdronesMANETmultistage games
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