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Subject
News
June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
June 5, 2026
‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
Polina Tabakova decided to apply for a Philology degree at HSE in Nizhny Novgorod because she grew up in Mari El and did not want to move far away from the Russian forests. In an interview for the Young Scientists of HSE University project, she spoke about the genre of the campus novel, the existential drama of Kolobok, and a blackout version of Eugene Onegin.
June 5, 2026
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

 

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The Impact of Corporate News on Stock Prices: Evidence from the Russian Stock Market

Journal of Corporate Finance Research. 2022. Vol. 16. No. 2. P. 44–55.
Ruzhanskaya L., Voytenkov V., Urazbaeva A., Danilian L.

In  the  face  of  complex  sanctions,  Russian  companies  are  looking  for  new  mechanisms  to  ensure  sustainability.  An  over - crowded  corporate  news  backdrop  could  be  the  source  for  a  significant  decline  in  a  company’s  market  value  or  growth.   Given the importance of information disclosure and corporate news for investors’ expectations, we estimate the effect that  four types of corporate news (Official publication of financial results for the quarters and for the year; M&A; Appointment  of new persons; Dividend policy) impose on shares of three Russian companies in different industries: the financial, energy  and high-tech sectors. In order to reflect corporate news’ influence, we developed an index of corporate news for the com - panies concerned utilizing daily data ranging 2015–2021. By applying a vector error-correction model, we demonstrate that  corporate news and corporate equities are related over the long run. Short-run results obtained with the Granger test sug - gest no evidence of causality. At the second stage, we construct impulse response functions that affirm the effect of corporate  news on stocks. Corporate news has a high and positive effect on stock prices in the high tech and energy sectors, while the  financial sector reacts in a mixed manner to corporate news, and the effect is weak. Obtained results serve as the basis for  practical recommendations to individual and institutional investors, as well as to companies for market value management.

Research target: Economics and Management
Language: English
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Keywords: российский фондовый рынокRussian stock marketcorporate newsкорпоративные новостицена акцииstock priceindex of corporate newsvicious cycle of influences between stocksиндекс корпоративных новостейциклический цикл влияния между акциями
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