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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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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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Language Policies and Local Identities in the Russian-Nordic Borderlands

Sociolinguistics. 2021. No. 2 (6). P. 128–144.
Ryzhkov A., Petrova E., Vlakhov A.

The paper investigates some aspects of language policies and local identities in the Russian- Nordic borderlands. It focuses on the interplay between identification strategies of local groups and linguistic environment of the multicultural region. The concept of language policy used in the study encompasses a wide range of practices, attitudes, beliefs as well as management strategies that are connected to linguistic codes used by borderland inhabitants. The primary data was sourced from the in-depth anthropological interviews collected during the fieldwork in the north-west of Karelia and the west of Murmansk Oblast. Due to the nature of the material, the analysis in the article is limited to the Russian side of the region. One of the findings of the study is that good command of a Nordic language (Finnish for Karelia, Norwegian for Murmansk Oblast) is not common on the Russian side of the borderlands and associated with particular life scenarios. Another observation worth mentioning is that Finnish and Norwegian are represented differently in local identity narratives as well as in linguistic landscapes and educational practices. Besides, linguistic and cultural heterogeneity uniquely reflects the social life of the each of the two Russian borderland territories. Overall, the study contributes to the discussion on the multilingual and multicultural environment of the Russian-Nordic borderlands.

Research target: Philology and Linguistics Sociology (including Demography and Anthropology
Language: English
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Keywords: ФинляндияКарелияязыковая политикаlanguage policyFinlandлокальная идентичностьNorwayНорвегияBorderlandlocal identityприграничье Republic of Karelia
Publication based on the results of:
Indigenous languages and cultures of the Arctic (2021)
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