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Subject
News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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ВЫЯВЛЕНИЕ ПРИЧИН НЕДООЦЕНКИ РОССИЙСКИХ КОМПАНИЙ ПРИ ПЕРВИЧНОМ РАЗМЕЩЕНИИ АКЦИЙ

Вестник Московского университета. Серия 6: Экономика. 2021. № 21(3). С. 240–270.
Nazarova V., Анисина Д. В.

An initial public offer (IPO) involves new financial opportunities for the issuing company such as: gaining access to capital markets, expanding the investor base, increasing the company's market value. In most primary placements, there is an underestimation of companies entering the market, which may be due to the influence of both external and internal factors. The purpose of the study is to identify factors that affect the initial underestimation of Russian companies during IPOs on internal and external sites. The novelty of the study lies in a comprehensive analysis of the factors influencing the underestimation of high-tech companies' shares in the initial public offering. Along with the previously studied factors (market conditions, the volatility of the price range of shares relative to the average value, the return on assets of the issuer's company for the year preceding the IPO), the study is supplemented by such factors as the choice of whether the share offering market belongs to Russian or foreign platforms, the impact the choice of several sites for placement, the prestige and reputation of the underwriter, the affiliation of companies to high-tech industries. In this study, the prestige of an underwriter will be determined by belonging to the Big Four, which may be an additional factor in the prospects and success of entering the stock market.

According to the results of the study, it was determined that the underestimation of Russian companies when placing on the domestic market is influenced by factors such as belonging to a high-tech industry and an indicator characterizing the state of the stock market. At the same time, when conducting an IPO on foreign stock markets, industry affiliation and placement price are significant factors. The underwriter's prestige, placement on several sites, return on assets and the width of the price range were not significant according to the results of the study.

Research target: Economics and Management
Language: Russian
Full text
DOI
Keywords: российский фондовый рынокнедооценка акций при вторичном размещении
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