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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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Efficiency Evaluation of Regions’ Firefighting Measures by Data Envelopment Analysis

P. 257–266.
Aleskerov F. T., Demin S.
Language: English
DOI
Text on another site
Keywords: efficiency evaluationinterval data Data Envelopment AnalysisFirefighting measures
Publication based on the results of:
Study of models and methods of decision-making under conditions of deep uncertainty: anticipating natural disasters and logistics challenges (2023)

In book

Handbook for Management of Threats: Security and defense, resilience and optimal strategies
Book 205. , Springer, 2023.
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