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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
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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Overtrusting robots: Setting a research agenda to mitigate overtrust in automation

Paladyn, Journal of Behavioral Robotics. 2021. No. 12. P. 423–426.
Aroyo A., de Bruyne J., Dheu O., Fosch-Villaronga E., Hoch H., Gudkov A., Jones S., Lutz C., Sætra H., Solberg M., Tamò-Larrieux A.

A broad range of participants brought in their expertise, allowing formulation of a forward-looking research agenda on overtrust and automation biases in robotics and autonomous systems. Key points include the need
for multidisciplinary understandings that are situated in an eco-system perspective, the consideration of adjacent
concepts such as deception and anthropomorphization, a connection to ongoing legal discussions through the topic of liability, and a socially embedded understanding of overtrust in education and literacy matters. The article integrates diverse literature and provides a ground for common understanding for overtrust in the context of HRI.

Research target: Sociology (including Demography and Anthropology Philosophy, Ethics, and Religious Studies Psychology Law
Language: English
Full text
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Keywords: roboticstrustworthinessAIAI systems trustworthiness
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