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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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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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Hybrid multilevel-multiwavelength signaling scheme for optical data transmission

Physica Scripta. 2023. Vol. 98. No. 12. Article 125119.
Maximov M., Shernyakov Y., Gordeev N., Dubrovskii V., Alexey M. Nadtochiy, Alexey E. Zhukov

We suggest an idea of data encoding scheme based on the switching from the ground state (GS) to excited state (ES) lasing in quantum dot (QD) lasers with increase in injection current. The groups of two bits are assigned to lasing spectra that comprise either one or both GS and/or ES lasing lines depending on injection current. We expect that the proposed encoding scheme can potentially combine some advantages of multilevel signaling and wavelength division multiplexing.

Research target: Physics Nanotechnologies
Language: English
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Keywords: Quantum dot laserstwo-state lasingmultilevel signalingdata encoding
Publication based on the results of:
Creation and investigation of semiconductor micro- and nanolasers for photonic integrated circuits (2024)
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