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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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Value Booms

Journal of Banking and Finance. 2026. Vol. 190. Article 107745.
Chibane M., Victoria Dobrynskaya, Ouzan S.

This study reveals that the historical performance of value strategies is concentrated in relatively rare episodes of exceptionally high returns, which we refer to as value booms. Behavioral theories suggest that the value premium should vary with investor sentiment, a hypothesis supported by our empirical findings. In the US, the value premium is about three times its unconditional counterpart following two years of negative market returns, but disappears after two years of positive returns. These value booms often coincide with sharp momentum underperformance. We control for momentum exposure and confirm that this pattern is not merely a manifestation of momentum crashes. We propose a dynamic investment strategy that switches between value and momentum, depending on trailing market conditions, improving the Sharpe ratios of standard value and momentum strategies by over threefold and 60%, respectively. Our results are robust across alternative value definitions, investor sentiment measures, and international markets.

Research target: Economics and Management
Language: English
DOI
Text on another site
Keywords: market efficiencystock market anomaliesoverreactionMomentumValue
Publication based on the results of:
Models and methods for identifying patterns in global and national financial markets (2025)
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