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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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Моделирование нестационарной ковариационной функции гауссовского процесса на основе разложения по словарю базисных функций

С. 357–362.
Yanovich Y., Burnaev E. V., Панов М. Е., Зайцев А. А., Приходько П. В.
Language: Russian
Text on another site
Keywords: гауссовские процессы

In book

Сборник статей конференции Информационные технологии и системы (ИТиС'11)
М.: ИППИ РАН, 2011.
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