Moscow University of Finance and Law

We study financial returns on videogame attributes as digital alternative investments, using CS:GO skins as an example. Using an extensive dataset of monthly returns on 4,565 skins for 2013-2024, we find that these niche investments outperform most traditional and alternative assets with the average return about 40% pa and provide diversification benefits. The time-series analysis indicates independence of their returns from equity market risk factors, as well as other financial markets, however, the returns exhibit strong seasonality patterns with peaks observed in January and April. The cross-sectional analysis reveals that items from direct drops and of better quality sell for higher prices and generate higher returns, whereas the presence of premium features and higher rarity are associated with significantly higher prices, but lower average returns. We observe a strong “penny stock effect” that cheaper items yield higher percentage returns. Lower financial returns on expensive skins are likely compensated by the “emotional dividend” of gamers.

The article explores theoretical and methodological aspects of corporate financial strategy formation with a focus on financial risks as a key factor of strategic financial management. It is demonstrated that under conditions of economic uncertainty, financial market volatility, and increasing complexity of financing decisions, financial risks exert a systemic influence on the parameters of financial strategy, including capital structure, liquidity policy, investment priorities, and requirements for financial stability. Special attention is given to the role of financial risks in the process of strategic financial planning and to the identification of key directions through which risks affect strategic financial decisions. Approaches to integrating risk management into corporate financial strategy are examined, and the necessity of aligning strategic financial objectives with an acceptable level of risk is substantiated. The scientific novelty of the study lies in conceptualizing financial risks as a structural element of corporate financial strategy and in systematizing the directions of their influence on strategic financial parameters, thereby expanding the theoretical framework for developing resilient and adaptive financial strategies under uncertainty.

Thanks to the advent of modern digital technologies and advancements in the information and communications sector, there has been a significant increase in the capacity of information networks. This has led to a substantial improvement in the quality of services provided by telecommunications companies. Consequently, it has become imperative to introduce new, integrated business support systems and operational management solutions for these companies. The aim of this paper is to analyze the approaches and identify the key features of integrated computerized control systems for telecommunications companies, focusing on the examination of characteristics of computerized systems in telecommunications and the requirements for their integration, as well as exploring technical requirements for operational support and business process management. The key aspects of designing integrated computer systems with a structured control architecture are highlighted, and a mathematical formulation of the relevant issue is presented. A practical application of this design is a scenario for practical implementation based on the standard operating parameters of tier 2 operators. The simulation results indicate that the transition to a decentralized architecture with an intelligent orchestration layer can lead to a reduction in the time taken for services to become operational, as well as a decrease in errors and the need for manual intervention. This, in turn, can reduce service level agreements (SLAs) violations and lower operational costs for organizational management within telecommunications companies.By integrating computer systems into a managed structure, organizations can increase the efficiency and reliability of their management processes. This can also facilitate the faster introduction of new services and minimize risks associated with scaling and infrastructure complexity.

The article presents the results of a study of the features of the introduction of modern technologies into the work of the Russian tanker fleet, which performs the most important function of transporting domestic hydrocarbons for export, as well as possible models of innovative development of shipping companies. The aim of the work was to develop recommendations on building an innovative tanker fleet strategy in the context of the ongoing global "green agenda" to ensure its competitiveness. In the course of the study, the models of innovative development were compared from the perspective of the priority of various types of assets held by companies, as well as compliance with modern ideas about the nature of the organization and the driving forces of scientific and technological development. The features of the innovation process in the tanker fleet are investigated, which is constrained by the reasonable conservatism of the industry, and is also characterized by high barriers to innovation and risks, capital intensity, duration of implementation, dependence on external infrastructure, strict regulation and standardization. Based on an assessment of the specifics of the innovation process and possible models of commercialization of innovations in tanker shipping, it was found that when building an innovation strategy, preference should be given to the most realistic models of a vertically integrated project and the creation of consortia in the short term. These models make it possible to coordinate efforts in the context of import substitution, concentrate resources and reduce risks under the control of the state or systemically important companies. At the same time, commercialization will be successful only if the "green innovations" have a calculated economic effect or avoid specific financial losses (fines for pollution, bans on ship operation), and not just correspond to "environmental trends."

This study addresses the problem of automatic attack detection targeting Linux-based machines and web applications through the analysis of system logs, with a particular focus on reducing the computational requirements of existing solutions. The aim of the research is to develop and evaluate the effectiveness of machine learning models capable of classifying system events as benign or malicious, while also identifying the type of attack under resource-constrained conditions. The Linux-APT-Dataset-2024 was employed as the primary source of data. To mitigate the challenge of high computational complexity, model optimization techniques such as parameter quantization, knowledge distillation, and architectural simplifications were applied. Experimental results demonstrate that the proposed approaches significantly reduce computational overhead and hardware requirements while maintaining high classification accuracy. The findings highlight the potential of optimized machine learning algorithms for the development of practical early threat detection systems in Linux environments with limited resources, which is particularly relevant for deployment in IoT devices and edge computing systems.

Global programming language is a special instrument, which will initially be created to solve the problem of code translation between programming languages. It will allow users to conveniently and error-free translate code from one language to another. Therefore, its grammar will be developed based on the condition of maximum convenience of unification of source texts of programs implemented in different paradigms. Also Global programming language has a lot of advantages. It is a universal entry point for learning programming. All programs can be run on any device by translating the source code into a language supported by a particular device. There is no need to choose one programming language to implement the entire project. It becomes possible to write various blocks of code in the most convenient languages and then automatically translate result into a single target language.

In this paper, we classify the vacuum constraint equations of General Relativity using the representation theory of classical groups. We also provide general solutions for the differential equations of each class and prove vanishing (nonexistence) theorems for these solutions using the Bochner technique, which is an important tool in Geometric Analysis.

Accounts are an integral part of most modern information systems and provide their owners with the ability to authenticate within the system. This paper presents an analysis of existing methods for detecting simple account passwords in automated systems. Their advantages and disadvantages are listed. A method was developed to detect simple exploitable passwords that administrators can use to supplement other existing methods to increase the overall security of automated systems against threats from accounts potentially compromised by attackers. The method was based on the analysis of commands executed in automated or manual modes with the indication of credentials in plain text. Minimum password strength requirements are provided based on the security level. A special case was considered in which all passwords analyzed in this way were found explicitly in the system logs. We developed a unified definition of the classification of passwords into simple and strong, and also developed machine learning technology for their classification. The method offers a flexible adaptation to a specific system, taking into account the level of significance of the information being processed and the password policy adopted, expressed in the possibility of retraining the machine learning model. The experimental method using machine learning algorithms, namely the ensemble of decision trees, for classifying passwords into strong and potentially compromised by attackers based on flexible password strength criteria, showed high results. The performance of the method is also compared against other machine learning algorithms, specifically XGBoost, Random Forest, and Naive Bayes. The presented approach also solves the problem of detecting events related to the use and storage of credentials in plain text. We used the dataset of approximately 770,000 passwords, allowing the machine learning model to accurately classify 98% of the passwords by their significance levels.

Relevance. Fundamental differences in the processes of development and maintenance of artificial neural networks from the algorithmic approach determine the relevance of the development of the existing scientific and methodological platform for solving technological and regulatory issues in the field of software development and legal protection of intellectual property of artificial intelligence systems.The purpose of the study is to formulate scientifically based conclusions that determine the directions for solving the scientific problem of legal protection of intellectual property on artificial neural networks.Objectives: to formulate a list of scientific tasks necessary for solving the scientific problem of legal protection of intellectual property rights to artificial neural networks; to develop proposals for improving the regulatory framework for registering intellectual property rights to artificial neural networks.Methodology. The methodological basis of scientific research was the dialectical method of understanding the phenomena and processes of the surrounding reality.Results. The obtained results of the study provide the opportunity to improve the regulatory framework establishing the basic principles of legal relations arising during the registration of intellectual property rights to artificial neural networks.Conclusion. The solution to the problem of legal protection of intellectual property on artificial neural networks should be based on the improvement of the existing regulatory framework for the registration of intellectual property rights, as well as regulatory acts that determine the rules for registering and securing intellectual property rights to arrays of training sample data and (or) arrays of trained models of artificial neural networks, as well as to the topology of integrated circuits of neuromorphic processors.

Abstract The Sustainable Development Goals direct the activities of all sectors of the economy to preserve the environment and minimize the negative consequences of anthropogenic activities. The agro-industrial complex is a major source of pollution of soil, water, air and the entire biosphere in general, and therefore significant changes have been taking place in agriculture over the last 20 years, both in the methods of its organization and in the directions of development. Europe was chosen as the object of analysis for the study, since it is here that almost all countries use organic technologies in the agricultural sector, the area of organic land, the number of organic producers, the volume of produced, exported and imported organic products, and their consumption increase every year. As part of the work, countries are grouped (by calculating a multidimensional average from a number of indicators) according to the level of development of organic agriculture, and the reasons for the differences are established; based on the method of correlation and regression analysis, the relationship between the demand for organic products and the level of average income in the country was identified and described; the structure of exports and imports of organic products was studied; the pace of development of the production of organic products was determined according to key characteristics, the main projects adopted in the field of crop production were considered in order to increase the volume of production of organic products. Based on the analysis, a conclusion was made about the pace and directions of development of organic agriculture in the European Union.