Using Fishburne’s sequences in suitable modeling used for sample data

This article deals with probabilistic and statistical modeling of managerial decision-making in the economy based on sample data for the previous periods of time. For better definition, the study is limited to Markowitz’s models in the problem of finding an effective portfolio of the field in the third information situation. The third information situation is a widespread decision-making situation and is characterized by the fact that the decision-maker sets, according to his opinion, are a linear order relation on the components of an unknown probabilistic distribution of the states of the economic environment. Often, from the point of view of the decision-maker, the components of an unknown probability distribution of the states of the economic environment must satisfy a partially reinforced linear order relation. As a result, the use of traditional statistical estimates turns out to be impossible, while the following question arises, which is practically not studied in the scientific literature. In this case, what formulas should be used to find statistical estimates and, above all, estimates of unknown probabilities of the state of the economic environment? As an estimate of an unknown probability distribution, we proposed to use the Fishburne sequence that satisfies all available constraints, while corresponding to the opinion of the decision maker and the linear order relation given by him. Fishburne sequences are a generalization of the well-known Fishburne formulas. It is fundamentally important that any Fishburne sequence satisfies a simple linear order relation, and under certain conditions, a partially strengthened linear order relation. Particular attention is paid to the entropic properties of generalized Fishburne progressions, which represent the most important class of Fishburne sequences, as well as the use of generalized Fishburne progressions to take into account the opinion of the decision maker. Such a scheme for estimating an unknown probability distribution has been developed, which makes it possible to achieve the correctness of probabilistic and statistical modeling, as well as appropriate consideration of the opinion of the decision-maker, uncertainty and risk.

To the question of restoring symbol sequences encoding noisy periodic functions

In business informatics, one of the research subjects is the analysis of data on processes in applied subject areas; here problems of qualitative analysis arise. Such problems arise, for example, in the qualitative study of log files of business processes, in the analysis and prediction of time series and other processes of a different nature. Quite often, to represent information about the processes under study, the methods of qualitative analysis use symbolic coding, which makes it possible to remove unnecessary detailing of numerical descriptions. The relevance of this study is due to the fact that when working with the raw data, researchers often face the presence of noise and distortions of the data, which significantly complicates the solution of the problems of qualitative analysis. When working with symbolic representations of the processes under study, which quite often have a periodic nature, we observe noise of deletion, insertion and replacement of symbols, which complicate the solution of the problem of revealing and analyzing the periodicity. This article deals with the problem of recovering periodic symbolic sequences obtained by coding from samples of continuous periodic functions and distorted by noise of insertion, replacement and deletion of symbols. Trigonometric functions are considered as a specific example of synthetic time series data. To encode trigonometric functions, alphabets of various cardinalities are used. The article presents an experimental study of the dependence of the quality characteristics of the method of period and a periodically repeating fragment recovery, previously proposed by the authors and improved in this study. For alphabets of different cardinalities at fixed sampling intervals, the fraction of sequences with a satisfactorily reconstructed period and the relative error in determining the period are given. The quality of reconstruction of a periodically repeating fragment is estimated by the edit distance from the reconstructed periodic sequence to the original sequence distorted by noise.

Technology for designing innovative processes for creating products and services of a network enterprise using an i4.0 knowledge-based system

The creation of network enterprises based on the digital technologies of the Industrie 4.0 (the 4th Industrial Revolution, i4.0) opens broad opportunities for increasing production flexibility, customer focus and continuous innovation in products and services provided. At the same time, new opportunities necessitate the development of new methods and technologies for designing innovative processes in the context of digital i4.0 platforms, all of which highlights the relevance of the presented research topic. This work aims to define technologies for designing innovative processes to create products and services using i4.0 systems which are based on multi-agent interaction of asset administration shells (AAS), displaying digital twins of product components, and the use of ontological and cognitive methods for forming and justifying design decisions. The work presented here uses the Domain-Driven Design approach, an architectural framework for building i4.0 systems, methods of ontological engineering, quality function deployment (QFD), analysis of the types and consequences of potential inconsistencies (FMEA) and processing of fuzzy sets. The paper proposes principles for identifying bounded contexts of the domain under the design activities for the stages of the life cycle and products’ subsystems (components). For bounded contexts of the domain, it is envisaged to create AAS of i4.0 systems, with the help of which the innovative process is supported and the multi-agent interaction of its participants is carried out. As cognitive tools for making design decisions, we proposed to use services for assessing the importance of the determined quality characteristics of products and minimizing deviations of the proposed solutions from the formed functional and non-functional requirements. The methods of ontological engineering and data modelling allow us to dynamically develop an innovative project and support various versions of the project in the design process. Application of the proposed technology for designing innovative processes to create products and services at network enterprises using i4.0 systems will improve the quality of design decisions, increase the dynamism and continuous design of innovative projects.

Modeling the links between institutional and actual globalization in the countries of the world

The paper is devoted to modeling the links between the institutional and actual level of globalization in the countries of the world. Vector models of error correction, quantile regression, and a stochastic frontier model are considered. As a measure of globalization and its components, the KOF-index of globalization system is used, which allows us to analyze individual globalization processes in the economy, social sphere and politics. According to 2020 data, we determine the dynamic relations between the actual and institutional components of globalization, and the priority of the institutional component for informational and financial globalization is revealed. The example of financial globalization shows the uneven degree of influence of the institutional component on the actual globalization, in particular, its prevailing importance for less globalized countries, indicating the alignment of the degree of internationalization in the global financial system. The degree of effectiveness of the impact of institutional measures, together with the overall level of well-being on the actual financial globalization is analyzed. It is shown that the spread across the countries of the world in the efficiency indicator is almost 70%. Almost 10% of countries have a low efficiency of up to 50%. One third of the countries has average efficiency (50–75%). The share of countries with high efficiency over 75% is about 60%.

Methods of digital marketing positioning in the global civil passenger aircraft market

Under conditions of intense competition, the creation of new products and their promotion to the market requires marketing support. The central element of marketing innovation management is marketing positioning. In contrast to the methodological aspect, there is almost no procedural component of positioning in scientific works and publications. Moreover, due attention is not paid to the issues of market segmentation and the allocation of the competitive advantage of such high-tech products as civil aircraft. This determines the relevance of the task of developing a procedure for marketing positioning within the framework of managing the product offer of the aircraft industry enterprises. From the standpoint of a systematic approach, the necessity of studying the adjacent air transportation market is reasonable, as well as the analysis of the correlation of its segmentation with the allocation of target segments of the aircraft market. When conducting a competitive analysis, we propose to consider the market position not only of certain types of civil aircraft, but also of aircraft families. Modeling marketing management shows the role of positioning in the processes of creating, manufacturing and operating aircraft. A distinctive feature of the procedure is the proposed stage of evaluating the effectiveness of the selected product position implementation, which allows us to analyze the feasibility of marketing decisions throughout the product life cycle. Special attention is paid to the issues of information support. The research results presented here reflect the specifics of the marketing activities of aviation industry enterprises and contribute to the development of the conceptual foundations of industrial marketing.

Robotizing bond portfolio selection on the Russian debt market on the basis of a modified strategy of riding the yield curve

The modern global debt market features historically low average interest rates, convergence of yields on bonds with different maturities, an increase of yield curve inversion emergence frequency and a large-scale trend to automate financial decision making. The researchers’ attention in these fields is mainly focused on designing models that describe the state of the debt market as whole or its individual instruments in particular, as well as on risk management methods. At the same time, the specialized literature offers very few works concerning the topic of computer algorithms for bond portfolio selection based on traditional or advanced investment strategies. The aim of the present research is to create a modification of the existing algorithm of riding the yield curve strategy application, employing, first, average bond yield over the holding period instead of traditional bond yield to maturity; second, a developed algorithm for calculating the market spread on bonds; and, third, alternative risk evaluation indicators (compensation coefficients), which allow us to measure objectively price risk, liquidity risk, transaction costs risk and a general risk. The modification and the development of the algorithm for calculating the market spread were carried out using the direct measurement of the result technique, which entails application of the strategy to the data on bond issues received through the Moscow Exchange API. The selection of financial instruments was conducted in all sectors of the Russian debt market: public bonds, sub-federal and municipal bonds, corporate bonds. The modified algorithm enabled us to get extra yield for each selected bond issue, thereby proving the high effectiveness of the technique compared to the traditional strategy. Software implementation of the algorithm can be integrated into any robotized or semi-robotized stock exchange trading application.

Efficiency of Green Supply Chain in the presence of non-discretionary and undesirable factors, using Data Envelopment Analysis

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.

Analysis of the impact of central bank communications on money market indicators

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.

Data preprocessing for machine analysis of sales representatives’ key performance indicators

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.

Integrated neurosymbolic decision support systems: problems and opportunities

The current problem of developing new kinds of decision support systems for different categories of management personnel is addressed in this study. A critical feature of such systems is their distributed and decentralized nature, which enables the construction of next-generation information systems in the form of Multi-Agent Systems, Internet of Things, or Fog Computing Architectures. Parallel models of the dynamics of artificial neural networks are produced under such realistic circumstances, demonstrating their potential for addressing a variety of issues. The purpose of this study is to conduct a critical analysis of the problem of integrating Artificial Neural Networks with decision support systems using a corpus of relevant scholarly literature. To tackle this question, the Design Science Research methodology was considered. According to this methodology, a literary search strategy was established, scientific literature was collected and analyzed, and key comparisons between different solutions were emphasized. The study resulted in the presentation of the most important findings, outstanding issues, and potential areas of fundamental and applied solutions. A consistent trend toward the development of decision support systems based on integrated neural-network methods has been observed, which is efficient and cost-effective since it enables the creation of distributed and trainable decision support systems.