SUMMARIES
S.I. Shanygin, I.N. Shustrov
Innovation in Modern Russia: Regional Patterns and Models
The article presents the results of the analysis of the innovation sphere of the Russian Federation in the regional context in order to identify structural (territorial) features and patterns. The analysis of publications in scientific journals is carried out, the problems of the subject area are defined, the relevance and novelty of the study are substantiated, the terminology base and methods for calculating the indicators are presented. The patterns of indicator dynamics for Russia as a whole and in the context of federal districts are described and compared, trends are identified and models are built. These districts are rated and the ratings are summarized according to various indicators. Frequency distributions are compiled in the context of the subjects of the Russian Federation according to the official administrative division, the main parameters and their changes over time are determined. The rating of the subjects of the Russian Federation and comparison of ratings by various indicators were conducted, the mutual influence of factors was assessed by correlation and regression methods based on spatial (territorial) data. Federal districts and subjects of the Russian Federation that have a significant impact on the research and production components of the development of the country’s innovation sphere were identified. Generalizations, conclusions and forecasts were formulated. The results of the study can be useful for making management decisions in the development and adjustment of National (Federal) projects and state programs for the development of regions.
Keywords: innovation sphere; innovation activity; research costs; volume of innovative products; mutual influence of indicators; structural dynamics; frequency distribution.
A.V. Omarova, Yu.V. Vymyatnina, Yu.V. Raskina
Financial Incentives and Their Impact on Borrowers: Early Repayment of Mortgage Loans in Different Conditions
The paper investigates the early repayment behavior of Russian borrowers regarding mortgage loans, particularly in relation to shifts in refinancing options and potential alternative investments for their funds, such as banking deposits. The findings indicate that under stable macroeconomic conditions, Russian borrowers act rationally, opting for full or partial early repayment of their mortgages when it aligns with their financial interests. However, during times of macroeconomic instability, borrowers prioritize accelerating mortgage repayment, often overlooking potentially more lucrative investment opportunities.
Keywords: mortgage; borrower behavior; prepayment; key rate; deposit rate; Russia.
I. Berzon, S. A. Rechmedina, N. I. Lysenok
Factor Investing: Can the Change in the Stock Price be Now Explained?
The article shows the development of asset pricing models in the stock market from the random walk hypothesis of 1900 to the ability to explain more than 90% of returns using the factor approach today. By better identifying risks, researchers are able to better predict prices and build lower-risk portfolios. The work highlights the difficulties encountered when implementing factor investing based on current statistical data: instability of factors over time, their dependence on the economic cycle. The place of approach regarding active and passive investing is shown; future development prospects are highlighted. The work complements the niche of research on the factor method in the stock market.
Keywords: asset pricing; factor model; stock market.
G.A. Khaziev
Detecting Nanipulation on the Russian Stock Market Using Artificial Intelligence
This paper examines the the effectiveness of artificial intelligence models to detect manipulation of Russian stocks based on the authors' sample of 866 manipulation cases over the period from 2012 to 2024. We build four artificial intelligence classification models to find the most effective method for manipulation detection: Logistic Regression, CatBoostClassifier, CatBoostCustom and Stacking model. Machine learning and artificial intelligence are effective in detecting manipulation in the Russian stock market (detecting over 81% of market manipulation). Models using a combination of machine learning algorithms (stacking models) demonstrate superior performance in detecting manipulation cases compared to standard machine learning models. Incorporating the intensity of stock discussions on social media into stock manipulation detection models can improve their reliability by 0,1−9,8%.
Keywords: stock market; stocks; market manipulation; manipulation detection; machine learning; artificial intelligence.
L.P. Bakumenko, N.S. Vasilyeva
Assessment of the Relationship Between Cryptocurrency and Financial Markets
The article examines the results of a comprehensive analysis of the relationship between the cryptocurrency market and traditional financial markets using regression and correlation analysis methods. The study aims to determine the extent of dependence and the nature of interactions between these two asset classes, which will provide a deeper understanding of their roles in the contemporary financial world and help to ascertain whether cryptocurrencies represent a new, independent asset class or are increasingly integrating into the existing financial system.
Keywords: cryptocurrency; Bitcoin; financial market; financial assets; investments; cryptocurrency market; regression; correlation.
M.S. Bobrova
The Problem of the Applicability of Modern Models for Diagnosing the Risk of Falsification of Reporting by Industrial Enterprises When Receiving State Support from the Industrial Development Fund (FRF)
Due to economic challenges and international sanctions, a significant part of Russian industrial enterprises is in serious condition and needs affordable government support, which is provided by the Industrial Development Fund (FRF). However, the statistical data on the repayment of loans in the FRP are disappointing and indicate that in the period from 2014 to 2023, the share of enterprises that do not fulfill their obligations increased from 3% to 27%. The issuance of loans to unscrupulous borrowers not only destabilizes the Fund’s activities, but also hinders the implementation of state policy in the field of industrial development and curbs the pace of economic development. These facts determine the relevance of research aimed at assessing the applicability of existing and developing new tools for assessing the risk of distortion of financial reporting data used when the Fund decides to provide state support.
The purpose of this article is to evaluate the predictive power of common Russian and foreign models for assessing the risk of falsification of financial statements on the data of industrial enterprises that received a loan from FRP in the period from January 2016 to August 2019, as well as to search for ways to improve existing tools for assessing the risk of falsification of financial (accounting) statements for Russian industrial organizations. The practical basis of the study covers 110 industrial enterprises, including 54 industrial companies, for which a court decision confirmed the fact of falsification of accounting data for obtaining a loan (55 sets of reports), and 56 industrial enterprises with reliable reporting, confirmed by a positive audit opinion, which fulfilled their obligations to the Fund on time, without any violations (58 sets of reports).
Keywords: falsification of reports; profile of manipulation; risk of falsification; problems of state support; industrial enterprises.
V.D. Gazman
Emission Activities of the Europe Leader in Car Leasing
The article examines the issue activity in the transactions of securitization of leasing assets of the leading car leasing company in Europe and in the world. The profile of the lessor, the characteristics of its development, the proportions that are formed between the funds received during securitization and the cost of new lease agreements are presented. The main advantages and disadvantages of issuing bonds by leasing companies have been identified. The conditions for the securitization of leasing assets, including car leasing, are justified by means of a constructed system of inequalities, taking into account the levels of profitability of the participants in the transactions. Recommendations have been prepared for operators of the Russian leasing market.
Keywords: leasing; issue; bonds; securitization; investments.
- F. Yusupova,
P. S. Oseneva
Competition Between Marketplaces in Russia: Role of Model of Interaction with Sellers, Buyers and Owners of Pick-up Points
This study examines the competition among leading digital transaction platforms in Russia: Ozon, Wildberries, AliExpress, SberMegaMarket, and Yandex.Market. The purpose of this article is to compare the interaction models of marketplaces with product sellers, buyers, and pick-up points in terms of price and non-price policies, as well as the policies for the location of pick-up points. The results indicate that despite the unique competitive advantages of each platform, none of them dominate the market. To maintain a competitive edge, continuous innovation in service offerings and flexible interaction models are necessary.
Keywords: digital platforms; competition; marketplaces; interaction models; pick-up points; e-commerce; transactional platforms.
A.A. Gavrilenko
Trends in the Mortgage Lending Market
Abstract: the end of work on preferential mortgages and high current mortgage rates have caused the emergence of new ways to purchase housing using borrowed funds. Developers are interested in maintaining consumer demand and here various discounts and installment options help attract buyers. This trend gives rise to various forecasts describing what will happen next with the real estate market in conditions of cooling of the market and when it is better to buy housing.
Keywords: mortgage lending; Central Bank of Russia; real estate; key rate; preferential programs; developers.
H.I. Penikas
Artificial Intelligence and Labour Market (Brief Literature Review)
Bank of International Settlements published two working papers on its website in September 2024. They were devoted to the artificial intelligence (AI) impact on the labour market. One of them contains a claim that the AI application leads to productivity boost by 50%. Disregarding such a tremendous rise in productivity, the second working paper argues that the white collar workers should not worry of the employment perspectives until their core activities are hard to automate. The objective of the current review is to understand how to properly perceive the announced statements. More specifically, to get how the findings were derived and which findings a reader of those two working papers should indeed take away.
Keywords: AI; technological progress; GPT; labour market.