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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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Informed Trading in Cryptocurrency Markets

HSE Economic Journal. 2024. Vol. 28. No. 4. P. 615–646.
Grigorii Kuzmin, Alexei Boulatov

This paper empirically estimates information asymmetry in cryptocurrency markets using the Probability of Informed Trading (PIN) and Adjusted PIN metrics . These markets, characterized by a high proportion of algorithmic trading and large volumes of high-frequency data, present a promising environment for analyzing informed trading behavior. We introduce a modified estimation procedure for Adjusted PIN, addressing floating-point errors and issues with local extrema, thereby improving its accuracy compared to the traditional naive approaches com­monly used in the literature. Additionally, we propose an alternative trade aggregation method at higher frequencies than the conventional daily aggregation to enhance the efficiency of both PIN and Adjusted PIN models. Through analysis of both simulated and real data, we demonstrate that aggregating total buy and sell trades on a daily basis results in less meaningful estimates due to noisy input data, making it difficult to capture informed trader activity. The true optimal trade aggregation frequency is still to be further investigated, as increasing the frequency introduces heterogeneity in order imbalances, and the specific frequencies at which informed traders operate are still unknown. Finally, several empirical studies are conducted to evaluate the behavior of the metrics, revealing that illiquid cryptocurrencies exhibit relatively higher estimated probabilities of informed trading. This finding aligns with similar results observed in equity markets.

Research target: Economics and Management Mathematics
Language: English
Full text
DOI
Text on another site
Keywords: PIN information asymmetrycryptocurrency marketsAdjusted PINprobability of informed trading
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