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News
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
Pocket Money, Personal Interest, and Family Practices: What Shapes Students Economic Literacy?
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.
June 3, 2026
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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Covid-19 and financial hardship in London

2021. No. 9/21.
Collado D., Popova D., Richiardi M.
Using UKMOD, the UK tax-benefit microsimulation model, we analyse the impact on Londoners of the Covid-19 crisis, of the emergency policies put in place since March 2020 and of some counterfactual policy options, including the continuation of the £20 weekly uplift in Universal Credit and Working Tax Credit. Our main results can be summarised as follows: 1) The emergency measures introduced by the Government in March 2020 proved very effective at protecting the incomes of Londoners, particularly some of the most disadvantaged groups. 2) Withdrawing the £20 a week uplift to Universal Credit and Working Tax credits will put 130,000 more Londoners in poverty, with some of the most disadvantaged groups, including 60,000 lone parents, being disproportionately affected. The number of Black Londoners living in poverty would grow by 8% compared to 6% for all ethnic groups. 3) Less well-off Londoners will be worst affected, with the poorest 10% experiencing an 8% decrease in their incomes. 4) While Londoners will be more affected than the rest of the country by the withdrawal of the £20 uplift; across the UK, the poorest 10% of people will see their incomes cut by 5%. 5) On the other hand, keeping the £20 a week uplift to Universal Credit and Working Tax credits and going further by removing all the Benefit Caps would cut child poverty in London by 10%, keeping more than 70,000 children out of poverty.
Research target: Sociology (including Demography and Anthropology Economics and Management
Priority areas: economics sociology state and public administration
Language: English
Full text
Text on another site
Keywords: бедностьincome distributionраспределение доходовmicrosimulationtax-benefit policyмикромоделированиеincome povertyКОВИД-19система налогов и трансфертовCOVID-19
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