2021. No. 3 Vol.15

      Press releases on monetary policy play an important role in the communication policy of the central bank. These press releases explain key rate decisions and provide signals about the future direction of the central bank’s monetary policy. Information signals can influence the expectations of financial market participants and increase the predictability and effectiveness of monetary policy. There are not enough research papers dedicated to the text analysis of the Bank of Russia press releases and the assessment of information signals. Hence, this article examines the impact of information signals about monetary policy on the money market rate, term and credit spreads. First, we estimate latent Dirichlet allocation to determine the topics of information signals. Second, we use sentiment analysis to construct signals about easing or tightening of the monetary policy. Third, the impact of signals about the future monetary policy on the money market indicators is assessed using the exponential GARCH model. Empirical research has shown that signals of future monetary policy easing are associated with lower money market rates and term spreads, and an increase in the credit spread. The result proved to be resistant to various ways of vectorizing the text of press releases. The article was prepared as a part of the state assignment research of Russian Presidential Academy of National Economy and Public Administration

      Together with ubiquitous, global digitalization, cybercrime is growing and developing rapidly. The state considers the creation of an environment conducive to information security to be a strategic goal for the development of the information society in Russia. However, the question of how the “state of protection of the individual, society and the state from internal and external information threats” should be achieved in accordance with the “Information Security” and the “Digital Economy of Russia 2024” programs remains open. The aim of this study is to increase the efficiency whereby automated control systems identify confidential data from html-pages to reduce the risk of using this data in the preparatory and initial stages of attacks on the infrastructure of government organizations. The article describes an approach that has been developed to identify confidential data based on the combination of several neural network technologies: a universal sentence encoder and a neural network recurrent architecture of bidirectional long-term short-term memory. The results of an assessment in comparison with modern means of natural language text processing (SpaCy) showed the merits and prospects of the practical application of the methodological approach.

      Significant transformation of the operational activity of product and service distributors is driven by changes in data-receiving and processing technology. At present, the work of these companies’ representatives is digitized to a large extent: for example, the road time, the number and places of meetings with customers are automatically recorded. At the same time, the productivity of managers who do not make direct sales is usually evaluated with the help of surveys, experts and costly double visits, although the existence of large data samples makes possible the use of statistical analysis to identify both insufficient and inflated values of performance indicators. Source data: a relational database that accumulates information about 28 categorical, quantitative, geolocation and temporal parameters of sale representatives’ activities for the last year. Based on available data, we created synthetic features (the latitude and longitude features produced the index, region, street, and house features; based upon identifiers we calculated the sum of activities of sales representatives; according to temporary features we defined the season of the year, the day of the week and the period of day features). The methodology for statistical analysis consists of three main stages: collection and processing of primary data; summary and grouping processed information; setting statistical hypotheses and interpreting the results. A probabilistic approach was used to model the level of distortion of sale representatives’ activities. As a result, with the built tag cloud we highlighted: the most popular season for advertising campaigns; the most productive departments and sale representatives; days of the week with the largest number of contacts to customers. We established a significant number of records about meetings with clients at the weekends. As a result of the data mining, we made a statistical hypothesis about the possibility of identifying the sale representatives who distort the number and parameters of meetings. A set of synthetic integer, real and categorical features was created to identify hidden relationships. Doubtful data (such as working at weekends or at night) were revealed. The resulting aggregated dataset is grouped by a sale representative’s activity ID and the distribution of this feature is plotted. For each sale representative, integer and real features are summarized and outliers that characterize inefficient performance or distortion of data have been detected. Thus, the presence of a large sample of data on the history of movements and activities allowed us to evaluate the productivity of the distribution company’s sales representatives based upon indirect features.

      The formation of supply chains for raw materials is closely related to production problems involving the determination of prices for sold goods. The question often arises about the need to study the sources of raw materials and the methodology for pricing the goods produced, taking into account a large number of external aspects of the market. Often, only particular approaches to solving production problems are considered in the literature, and methods for solving the complex problem of forming supply chains for raw materials and pricing are poorly developed. This paper presents a mathematical model that makes it possible to assess the feasibility of interaction between a timber industry enterprise and a commodity exchange, with the daily formation of a price vector over the entire planning horizon. A two-stage algorithm for finding a suboptimal solution is considered, which at the first stage is based on linear optimization, and at the second, on gradient descent with the use of penalty functions. The model was tested on the data of the commodity and raw materials exchange of Russia and one of the enterprises of the Primorsky Territory. The result of testing was the volume of production of each type of product over the entire planning horizon, the volume of delivery of raw materials from regions to enterprises, as well as the methods of delivery of goods to the consumer and the policy of pricing. It is shown that almost all goods should increase in price due to a reduction in the excess volume of applications (demand) over the entire planning horizon, with the exception of two types of products. It is noted that the exchange can provide the necessary volume of raw materials for high-capacity production, which demonstrates the possibility, if necessary, to increase the volume of raw materials purchases. It is shown which goods will be included in the release plan more often than others when optimizing the price vector. The ways of delivery of final types of products are analyzed. The disadvantages and advantages of the mathematical model and algorithm are presented.

      A new approach that has dominated the production operations management field in recent years is supply chain management. A supply chain includes all the facilities, tasks and activities involved in manufacturing a product from suppliers to customers. Its various elements are planning, supply and demand management, procurement of raw materials, production scheduling, distribution and delivery of products to the customer. Special structures in the supply chain have been less studied in previous research. In this paper, the supply chain and its performance evaluation are examined in the presence of non-discretionary, undesirable and negative data. For this purpose, another model of the network DEA is presented which evaluates performance of the chain in the presence of non-discretionary inputs and outputs, undesirable outputs and negative outputs even in its internal structure. The efficiency of the chain stages is also calculated using a dual model. Subsequently, 42 cement companies listed on the Tehran stock exchange were evaluated, each of which has a chain of four stages including suppliers, manufacturers, distributors and customers. Based on the implementation of the model, six companies were found to be efficient and the rest were introduced as inefficient. Moreover, 25 cement companies in the Supplier sector, 18 companies in the manufacturing sector, seven companies in the distribution sector and finally 17 companies in the customer service sector were found to be efficient.