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August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
August 12, 2026
‘I Would Like My Research to Help Make the World a Calmer and Better Place
Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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Эффективность применения прогнозов волатильности в активных торговых стратегиях институциональных инвесторов на российском рынке акций

Фундаментальная и прикладная математика. 2026. Т. 26. № 3. С. 33–42.
Lysenok N.

This study examines the impact of realized volatility forecasts on the performance of active trading strategies in the Russian equity market. Using a sample of 17 liquid stocks over the period 2014–2026, a hybrid forecasting model is developed that combines HAR-J with gradient boosting; its superiority over the baseline HAR-J specification is confirmed by the Diebold–Mariano test (p < 0.001). Six directional strategies across three categories are tested with three mechanisms for integrating volatility forecasts and without them. The aggregation is performed sequentially at three levels: individual instruments, strategy portfolios across the 17 stocks, and multi-strategy portfolios (MSP). At the MSP level, a bootstrap procedure confirms a statistically significant increase in the Sharpe ratio when applying regime filtering (ΔSharpe = +1.19, p < 0.001) and volatility targeting (ΔSharpe = +1.22, p < 0.001). The MSP statistically significantly outperforms the IMOEX index (ΔSharpe = +2.05, p = 0.001) and maintains positive returns in all four years of the out-of-sample control period.

Research target: Economics and Management
Language: Russian
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Keywords: машинное обучениеторговые стратегииeconomic valueэкономическая ценностьtrading strategiesrealized volatilityреализованная волатильность machine learningvolatility targetingmulti-strategy portfolioтаргетирование волатильностимультистратегийный портфель
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