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.
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.
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.
Olga Blinova, Tarasov N., Frontiers in Artificial Intelligence 2022 Vol. 5 Article 1008530
This article proposes a hybrid model for the estimation of the complexity of legal documents in Russian. The model consists of two main modules: linguistic feature extractor and a transformer-based neural encoder. The set of linguistic metrics includes both non-specific metrics traditionally used to predict complexity, as well as style-specific metrics developed in order to ...
Blinova O. V., Мир русского слова 2022 № 2 С. 4–13
The paper describes the metrics-based model for assessing complexity of Russian legal texts. The architecture of the model implies the use of 130 metrics divided into following categories: “basic metrics”, “readability formulas”, “words of different part-of-speech classes”, “n-grams of part-of-speech tags”, “frequency of lemmas”, “word-building patterns”, “grammes”, “lexical and semantic features, multi-word expressions”, “syntactic features”, ...