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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.
June 3, 2026
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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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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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Аспекты кэширования в информационно-ориентированных сетях

С. 70–76.
Iakimenko S.

The article is devoted to the issues of caching in information-centric networks (ICN), aimed at addressing by the content name and separating information from its producer node. Intra-network caching is one of the main features of ICN, turning them, in fact, into a network of interconnected caches that can accumulate packets in the buffer of intermediate routers, and also bring them closer to the consumer using a peripheral cache. The effectiveness of such a system depends on many factors discussed in the article: the law of popularity and the content replication strategy, the size and policy of cache eviction. Based on Che's approximation, the article presents a transition from the analysis of a single cache with the LRU (Least Recently Used) eviction policy to the analysis of a network of caches. For the strategy of content replication on a peripheral router, an original algorithm is presented that allows you to effectively store popular content in the cache, and surpasses the traditional LCE approach in several metrics (cache hit, delay, etc.). The simulation of the operation of such an algorithm for a car network scenario was carried out in the ndnSIM network simulator.

Language: Russian
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Keywords: cachingкэшированиеedge computingпериферийные вычисленияinformation-centric networksChe's approximationинформационно-ориентированные сетиприближение Че

In book

Современные сетевые технологии: труды 4-й Международной конференции "Современные сетевые технологии" (MoNeTec-2022), 27-29 октября 2022 г., г.Москва (короткие и стендовые доклады)
М.: Издательский отдел факультета ВМК МГУ им. М.В. Ломоносова, 2022.
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