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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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University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
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Triclusters of Close Values for the Analysis of 3D Data

Automation and Remote Control. 2022. Vol. 83. No. 6. P. 894–902.
Egurnov D., Ignatov D. I.

Abstract: The paper deals with the problem of triclustering in multivalued triadic contexts in termsof one multidimensional extension of formal concept analysis; triclustering can be viewed as asearch for dense subtensors in three-dimensional tensors over the field of real numbers. Twomethods are proposed for solving this problem, namely, NOAC—a version of the OACtriclustering method for numerical data based on delta operators—and a triadic version ofthek-means method with an improved metric based on Manhattan distance andproximity predicates in each of the three dimensions. Numerical experiments are carried out bothon real and synthetic data and confirm the superiority of the NOAC method in terms of theperformance criteria for the resulting triclusters. © 2022, Pleiades Publishing, Ltd.

Research target: Computer Science Mathematics
Language: English
DOI
Keywords: Formal Concept Analysistriclustering3D tensormultivalued context
Publication based on the results of:
Development of mathematical models and methods for natural language processing, knowledge discovery in data and recommender systems (2022)
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