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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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Ethnographic Case-Study Design for Discovering Identities in Russian Post-Industrial Neighbourhoods

The Qualitative Report. 2022. Vol. 28. No. 5. P. 1221–1238.
Polukhina E.

In the 20th century of industrialization, case-study research design was related to organizations, however in the 21st century of deindustrialization the post-industrial neighborhoods could serves as an indicator of neoliberal policies and as a reference space for identity studies. However, the instruments of these studies are still poorly described, which limits this type of research design and the comparative analysis of similar neighborhoods in the global context. This paper describes the methodology and insights from three ethnographic case-studies of neighbourhoods—a type of field research focused on detailed inquiry with multiple methods of data collection. This paper follows the Chicago School of Sociology (Park, 1967), understanding a neighbourhood as a field laboratory where social and transformational processes occur, and the social nature of these changes is exposed. The identities were elaborated by secondary data, different types of interviews, observations, and visual data. The paper demonstrates the importance of visual data and the contextualization of the groups studied in the space of the neighborhood, other residents, and changes in the material and social landscapes (the historical, material, and spatial contexts). Data from photographs and videos revealed the material context of transformation, exposing the rigidity of changes in the capital case, and the actualization of inequalities in the regional case. The analysis of the mental maps of the regional neighborhood demonstrates that the workers still perceived this area as an industrial place and the factory is still the core of the workers' identity. Thus, the visual materials expose the multiple layers of identities and new aspects of inequalities. The visual data formed the basis for a research exhibition and a film that shows the ambivalence of post-industrial processes and the multivoicedness of neighborhood residents to different publics.

Research target: Sociology (including Demography and Anthropology
Language: English
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Keywords: Chicago school of sociologyidentitiescase study researchindustrial neighbourhood
Publication based on the results of:
The role of social diversity in institutional contexts: the inclusive society’s potential and challenges (2022)
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