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Subject
News
September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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Media sentiment, news and liquidity of Chinese property developer stocks amidst the shadow of a mortgage crisis in China

International Journal of Emerging Markets. 2025. Vol. 20. No. 6. P. 2223–2242.
Gurov S., Teplova T.

Purpose
The study examines the relationship between news intensity, media sentiment and market microstructure invariance-implied measures of trading activity and liquidity of Chinese property developer stocks during the 2020–2022 Chinese property sector crisis.

Design/methodology/approach
The authors adopt the extension of the news article invariance hypothesis, which is a generalization of the market microstructure invariance conjecture, from January 2020 to January 2022 to test specific quantitative relationships between the arrival rate of public information, trading activity and a nonlinear function of a proxy for the probability of informed trading. Empirical tests are based on a dataset of 22,412 firm-day observations and two count-data models to correct for overdispersion and the excess number of zeros. Seventy-five stocks of Chinese companies from the property development industry (including the China Evergrande Group) were included in the sample.

Findings
The authors reject the news article invariance hypothesis but document a positive and significant relationship between the flow of public information and risk liquidity. Additionally, the authors find that the proxy for informed trading activity is positively related to the arrival rates of public information from October 2021 to January 2022.

Originality/value
The findings support the hypothesis that negative (positive) media sentiment induces significant deterioration (insignificant improvement) in stock liquidity. The authors find that an increase in the number of news articles about a company corresponds to a higher liquidity of Chinese property developers' stocks after controlling for media sentiment.

Research target: Economics and Management
Language: English
Full text
DOI
Keywords: ликвидностьмасс-медиаliquiditymarket microstructureмикроструктура рынкаторговая активность mass medianews sentiment trading activityновостной сентиментnews article invariance hypothesisгипотеза инвариантности новостных заголовков
Publication based on the results of:
Models and methods for identifying patterns in global and national financial markets (2025)
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