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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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От чего зависит амбициозность климатической политики разных стран?

Вестник международных организаций: образование, наука, новая экономика. 2021. Т. 16. № 4.
Stepanov I. A., Агикян Н. Д., Музыченко Е. Э.

Climate change is considered one of the most challenging problems of the 21st century and requires coordinated action by governments across the globe. The Paris Agreement, ratified by most countries of the world, sets the goal of keeping the average temperature rise within 2°C compared to pre-industrial levels. As part of the Agreement, countries set nationally determined contributions (NDCs)—targets for reducing greenhouse gas emissions—which are determined voluntarily. Because these targets are nationally determined, they depend on domestic constraints and the additional opportunities that individual countries’ emission reduction strategies present. As a result, climate policies vary widely among countries, both in terms of the emission reduction targets and the policy instruments used. The purpose of this study is to systematize the factors influencing climate policy using factor and cluster analysis methods. Factor analysis is used to aggregate a set of investigated statistical indicators, reflecting a country’s development level, exposure to climate risks, energy endowment, and foreign tradespecialization, into a series of principal components. Based on the selected principal components, the countries are clustered into homogeneous groups, and the indicators of climate policy ambition are compared among the clusters. The results of the study demonstrate that climate change vulnerability is not a determinant of climate policy. As a rule, the poorest and most vulnerable countries set the least ambitious emission reduction targets. At the same time, rich and energy-abundant countries are more likely to implement active climate policies and set more ambitious emissions reduction targets compared to energy-intensive countries and countries that specialize in exporting carbon-intensive products. Advanced climate policy instruments, such as a carbon tax or emissions trading system, are used with greater frequency in more advanced and energydeficient countries.

Research target: Economics and Management Political Science, International Relations, and Public Administration
Language: Russian
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Keywords: изменение климатаэкономическое развитиеeconomic developmentэнергетическая зависимостьклиматическая политикаclimate changeclimate policyПарижское соглашениеParis Agreementenergy dependencyGHG emissionsnationally determined contributionsсокращение выбросов парниковых газовнационально определяемые вклады
Publication based on the results of:
Assessment of the impact of reducing greenhouse gas emissions on economic growth and prospects for solving the problems of poverty and inequality: aspect of global governance (2021)
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