Pub Date : 2023-12-31DOI: 10.17323/2587-814x.2023.4.94.112
Maneesh Kumar Pandey, Amit Kumar Pathak, Irina Sergeeva
Sustainable development, a prominent issue in the twenty-first century, is significantly influenced by the rapid global IT revolution. This study employs bibliometric analysis to explore the role of scientific research in sustainable development reporting, aligning with international standards and utilizing IT tools. It assesses countries’ awareness of sustainable development reporting’s importance in achieving socio-economic and environmental goals. The study examines article frequency, source countries, authors, co-authorship, citations, key term co-occurrences, and bibliometric coupling. The result concludes that active engagement among research work of academic institutions, government organizations, and industries of emerging countries on the development and role of information technology in sustainable development reporting practices can foster cost-effective ways for sustainable development reporting which may play a vital and crucial role in sustainable development reporting for middle- and low-income countries to ensure a green and sustainable future. This work can benefit middle- and low-income nations in their pursuit of a green and sustainable future. The research highlights the significance of academic institutions in enhancing sustainable development reporting, especially for Micro, Small, and Medium-Sized Enterprises (MSMEs) in middle and low-income countries, offering valuable insights for future actions, which in turn may help these countries to put more effort into this domain through their academic establishments.
{"title":"A bibliometric review of scientific research on the significance of information technology relating to sustainable development reporting practice","authors":"Maneesh Kumar Pandey, Amit Kumar Pathak, Irina Sergeeva","doi":"10.17323/2587-814x.2023.4.94.112","DOIUrl":"https://doi.org/10.17323/2587-814x.2023.4.94.112","url":null,"abstract":"Sustainable development, a prominent issue in the twenty-first century, is significantly influenced by the rapid global IT revolution. This study employs bibliometric analysis to explore the role of scientific research in sustainable development reporting, aligning with international standards and utilizing IT tools. It assesses countries’ awareness of sustainable development reporting’s importance in achieving socio-economic and environmental goals. The study examines article frequency, source countries, authors, co-authorship, citations, key term co-occurrences, and bibliometric coupling. The result concludes that active engagement among research work of academic institutions, government organizations, and industries of emerging countries on the development and role of information technology in sustainable development reporting practices can foster cost-effective ways for sustainable development reporting which may play a vital and crucial role in sustainable development reporting for middle- and low-income countries to ensure a green and sustainable future. This work can benefit middle- and low-income nations in their pursuit of a green and sustainable future. The research highlights the significance of academic institutions in enhancing sustainable development reporting, especially for Micro, Small, and Medium-Sized Enterprises (MSMEs) in middle and low-income countries, offering valuable insights for future actions, which in turn may help these countries to put more effort into this domain through their academic establishments.","PeriodicalId":36213,"journal":{"name":"Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139132986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-31DOI: 10.17323/2587-814x.2023.4.7.24
Michael Laskin, Oleg Rusakov
Real estate market price forecasting is always in the focus of interests of scientists-economists, market analysts, market participants (sellers and buyers), marketing services of building complex enterprises, analysts working for banks and insurance companies and investors. Under present day conditions, the price behavior of properties on real estate markets takes especially important meaning subject to the influence of such factors as changes in the structure of household incomes, changes in mortgage rates and their availability, dynamic changes in the macroeconomic and other external socio-economic and political type factors. However, unlike the financial and securities markets, the real estate market is always characterized by a delayed reaction to external perturbations, often up to half a year, which allows us to hope for an appropriate construction of forecasts, at least in time for the delayed reaction. Traditional autoregressive forecasting methods are characterized by rapidly increasing forecast variance, because they assume a factor of stochastic volatility. This paper proposes a model and method of forecast construction based on stochastic processes of the “Poisson random index” having a short time for reaching a stationary stable variance. The model is based on the “principle of replacements” of current prices with new ones. We analyze in detail an example of the application of the “principle of replacements” for construction of price forecasts on secondary residential real estate in St. Petersburg which is based on data of four-year observations of offer prices.
{"title":"Prediction of distributions of unit prices for real estate properties on the basis of the characteristics of PSI-processes","authors":"Michael Laskin, Oleg Rusakov","doi":"10.17323/2587-814x.2023.4.7.24","DOIUrl":"https://doi.org/10.17323/2587-814x.2023.4.7.24","url":null,"abstract":"Real estate market price forecasting is always in the focus of interests of scientists-economists, market analysts, market participants (sellers and buyers), marketing services of building complex enterprises, analysts working for banks and insurance companies and investors. Under present day conditions, the price behavior of properties on real estate markets takes especially important meaning subject to the influence of such factors as changes in the structure of household incomes, changes in mortgage rates and their availability, dynamic changes in the macroeconomic and other external socio-economic and political type factors. However, unlike the financial and securities markets, the real estate market is always characterized by a delayed reaction to external perturbations, often up to half a year, which allows us to hope for an appropriate construction of forecasts, at least in time for the delayed reaction. Traditional autoregressive forecasting methods are characterized by rapidly increasing forecast variance, because they assume a factor of stochastic volatility. This paper proposes a model and method of forecast construction based on stochastic processes of the “Poisson random index” having a short time for reaching a stationary stable variance. The model is based on the “principle of replacements” of current prices with new ones. We analyze in detail an example of the application of the “principle of replacements” for construction of price forecasts on secondary residential real estate in St. Petersburg which is based on data of four-year observations of offer prices.","PeriodicalId":36213,"journal":{"name":"Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139132463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-31DOI: 10.17323/2587-814x.2023.4.57.72
Ivan Solovyov, Viatcheslav Semenikhin, Sergey Kushch
In today’s economy based on knowledge and innovation, the development of absorptive capacity by companies is a critical aspect of business competitiveness. In this study, the tech stack of sites is considered as a specific, measurable part of the digital and innovative development of a company. In literature to date, there is no clear answer to which technologies and in what quantity should be included in the tech stack. From the point of view of assessing the tech stack, mainly qualitative methods are proposed that are quite resource intensive. Accordingly, the purpose of this study is to determine the impact of the technologies used by the characteristics of quantity, uniqueness and popularity in the tech stack of the product on the result (the absence of critical errors); as well as in developing a quantitative approach for assessing the impact of the technologies used on the result of a digital product. The quantitative approach was developed and conceptualized based on previous literature, tested on 12 sites of large Russian banks, including 12 main domains and 595 subdomains. An analysis of a study of 216 online applications for banking products showed a positive relationship between the share of unique technologies in the bank’s visible tech stack and the number of errors, as well as a negative relationship between the share of popular technologies in the stack and errors. This study expands the discussion on the development of absorptive capacity, contributes to the understanding of the limitations of absorptive capacity of companies and proposes a quantitative approach for auditing the operational tech stack of companies’ websites.
{"title":"The influence of the breadth of the tech stack on the result of the digital product: А view through the theory of absorption capacity","authors":"Ivan Solovyov, Viatcheslav Semenikhin, Sergey Kushch","doi":"10.17323/2587-814x.2023.4.57.72","DOIUrl":"https://doi.org/10.17323/2587-814x.2023.4.57.72","url":null,"abstract":"In today’s economy based on knowledge and innovation, the development of absorptive capacity by companies is a critical aspect of business competitiveness. In this study, the tech stack of sites is considered as a specific, measurable part of the digital and innovative development of a company. In literature to date, there is no clear answer to which technologies and in what quantity should be included in the tech stack. From the point of view of assessing the tech stack, mainly qualitative methods are proposed that are quite resource intensive. Accordingly, the purpose of this study is to determine the impact of the technologies used by the characteristics of quantity, uniqueness and popularity in the tech stack of the product on the result (the absence of critical errors); as well as in developing a quantitative approach for assessing the impact of the technologies used on the result of a digital product. The quantitative approach was developed and conceptualized based on previous literature, tested on 12 sites of large Russian banks, including 12 main domains and 595 subdomains. An analysis of a study of 216 online applications for banking products showed a positive relationship between the share of unique technologies in the bank’s visible tech stack and the number of errors, as well as a negative relationship between the share of popular technologies in the stack and errors. This study expands the discussion on the development of absorptive capacity, contributes to the understanding of the limitations of absorptive capacity of companies and proposes a quantitative approach for auditing the operational tech stack of companies’ websites.","PeriodicalId":36213,"journal":{"name":"Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139135973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-31DOI: 10.17323/2587-814x.2023.4.41.56
R. Rogulin
The formation of raw material supply chains is very closely related to production problems at a timber processing plant. Since the beginning of the second industrial revolution, one urgent question has been the formation of supply chains for raw materials and the optimal calculation of production volumes for each individual day. This article examines a forestry enterprise that does not have its own sources of wood, which daily solves the problem of ensuring the supply of raw materials and optimal production load. A commodity exchange is considered as a source of raw materials where lots randomly appear every day in different raw material regions. In the scientific literature, there are many approaches to calculating the optimal profit value over the entire planning horizon, but they do not consider many features that are important for a timber processing enterprise. This paper presents a mathematical model which is a mechanism for making daily decisions over the entire planning horizon and differs in that it allows one to take into account the share of useful volume and the delivery time of raw materials under conditions of uncertainty. The result of the model is the optimal profit trajectory, considering the volume of raw materials, the delivery time of lots, the volume of profit and the production volume of goods. The model was tested on data from the Russian Commodity and Raw Materials Exchange and one of the Primorsky Territory enterprises. Analysis of the results showed that there are difficulties in planning supply chains and production volumes. An assessment of the optimality of raw material regions was carried out. The advantages and disadvantages of the mathematical model are formulated.
{"title":"Mathematical model of the formation of supply chains of raw materials from a commodity exchange under conditions of uncertainty","authors":"R. Rogulin","doi":"10.17323/2587-814x.2023.4.41.56","DOIUrl":"https://doi.org/10.17323/2587-814x.2023.4.41.56","url":null,"abstract":"The formation of raw material supply chains is very closely related to production problems at a timber processing plant. Since the beginning of the second industrial revolution, one urgent question has been the formation of supply chains for raw materials and the optimal calculation of production volumes for each individual day. This article examines a forestry enterprise that does not have its own sources of wood, which daily solves the problem of ensuring the supply of raw materials and optimal production load. A commodity exchange is considered as a source of raw materials where lots randomly appear every day in different raw material regions. In the scientific literature, there are many approaches to calculating the optimal profit value over the entire planning horizon, but they do not consider many features that are important for a timber processing enterprise. This paper presents a mathematical model which is a mechanism for making daily decisions over the entire planning horizon and differs in that it allows one to take into account the share of useful volume and the delivery time of raw materials under conditions of uncertainty. The result of the model is the optimal profit trajectory, considering the volume of raw materials, the delivery time of lots, the volume of profit and the production volume of goods. The model was tested on data from the Russian Commodity and Raw Materials Exchange and one of the Primorsky Territory enterprises. Analysis of the results showed that there are difficulties in planning supply chains and production volumes. An assessment of the optimality of raw material regions was carried out. The advantages and disadvantages of the mathematical model are formulated.","PeriodicalId":36213,"journal":{"name":"Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139132955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-31DOI: 10.17323/2587-814x.2023.4.25.40
Alexander Kudrov
The economic complexity index defines the basis of the modern theory of economic complexity and reflects the level of knowledge embedded in the production structure of the economy. This study examines the direct relationship between the economic complexity index and gross regional product (GRP) while taking into account other factors of the GRP production function in its generalized representation. As a result, we can isolate the impact of the economic complexity index from other phenomena. The non-linear nature of the relationship between economic complexity and GRP is revealed, and the direct relationship is manifested only at sufficiently high values of economic complexity, exceeding a certain threshold, which is found endogenously using econometric methods. In addition, the paper studies the relationship between economic complexity and indices of sectoral specialization. We found that there is a direct relationship between economic complexity and the extractive industry index and no relationship with the level of development of manufacturing industry. We obtained a clarification of the generalized production function of GRP, in which the threshold effect of the influence of economic complexity manifested itself as a factor of nonlinear dependence describing the elasticity of labor: a high level of economic complexity provides greater labor productivity. Overall, the results of the study of the dependence of GRP on economic complexity lead to the conclusion that increasing economic complexity can be an effective way to stimulate economic growth and development, but only starting from a certain threshold level. This suggests that an economy must reach a minimum level of diversity and complexity in its industrial activities before it can experience the productivity gains necessary for substantial GRP growth.
{"title":"The impact of economic complexity and industry specialization on the gross regional product of Russian regions","authors":"Alexander Kudrov","doi":"10.17323/2587-814x.2023.4.25.40","DOIUrl":"https://doi.org/10.17323/2587-814x.2023.4.25.40","url":null,"abstract":"The economic complexity index defines the basis of the modern theory of economic complexity and reflects the level of knowledge embedded in the production structure of the economy. This study examines the direct relationship between the economic complexity index and gross regional product (GRP) while taking into account other factors of the GRP production function in its generalized representation. As a result, we can isolate the impact of the economic complexity index from other phenomena. The non-linear nature of the relationship between economic complexity and GRP is revealed, and the direct relationship is manifested only at sufficiently high values of economic complexity, exceeding a certain threshold, which is found endogenously using econometric methods. In addition, the paper studies the relationship between economic complexity and indices of sectoral specialization. We found that there is a direct relationship between economic complexity and the extractive industry index and no relationship with the level of development of manufacturing industry. We obtained a clarification of the generalized production function of GRP, in which the threshold effect of the influence of economic complexity manifested itself as a factor of nonlinear dependence describing the elasticity of labor: a high level of economic complexity provides greater labor productivity. Overall, the results of the study of the dependence of GRP on economic complexity lead to the conclusion that increasing economic complexity can be an effective way to stimulate economic growth and development, but only starting from a certain threshold level. This suggests that an economy must reach a minimum level of diversity and complexity in its industrial activities before it can experience the productivity gains necessary for substantial GRP growth.","PeriodicalId":36213,"journal":{"name":"Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139135374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-31DOI: 10.17323/2587-814x.2023.4.73.93
Yury F. Telnov, V. Kazakov, A. Bryzgalov, I. Fiodorov
The process of digital transformation of enterprises is associated with the organization of manufacturing and business processes within the framework of selected types of business models and digital platforms, the distribution and economic substantiation of the roles of participants in network interactions, and ensuring the semantic interoperability of their interaction. Currently, certain experience has been accumulated in the implementation of modern business models for the digital transformation of enterprises which is reflected in the concepts of the Industrie 4.0, the Industrial Internet of Things, the creation of cyber-physical production systems, smart enterprises and intelligent manufacturing. At the same time, the issues of conceptual modeling of the architecture of digital enterprises, which determines the construction of manufacturing and business processes, and its economic substantiation depending on various factors of the external environment and internal economic potential have not yet been sufficiently researched and developed. All of the foregoing determines the relevance of the work presented here. The purpose of the study was to develop ontological and economic methods for substantiating application scenarios for the digitalization of manufacturing and business processes depending on the selected types of business models and digital platforms. To solve the problem, methods of classification, ontological engineering, activity-based costing and analysis of cash flows of income and expenses are used. The article presents an analysis of enterprise digitalization scenarios depending on the types of manufacturing and business processes, the types of business models and digital platforms used. An ontological model of enterprise digital transformation has been constructed, providing a choice of application scenarios for the digitalization of manufacturing and business processes for various types of business models and digital platforms. An economic model is proposed to justify options for constructing application scenarios for the digitalization of production and business processes depending on the distribution of roles of participants in network interaction using methods of activity-based costing and cash flow analysis.
{"title":"Methods and models for substantiating application scenarios for the digitalization of manufacturing and business processes of network enterprises","authors":"Yury F. Telnov, V. Kazakov, A. Bryzgalov, I. Fiodorov","doi":"10.17323/2587-814x.2023.4.73.93","DOIUrl":"https://doi.org/10.17323/2587-814x.2023.4.73.93","url":null,"abstract":"The process of digital transformation of enterprises is associated with the organization of manufacturing and business processes within the framework of selected types of business models and digital platforms, the distribution and economic substantiation of the roles of participants in network interactions, and ensuring the semantic interoperability of their interaction. Currently, certain experience has been accumulated in the implementation of modern business models for the digital transformation of enterprises which is reflected in the concepts of the Industrie 4.0, the Industrial Internet of Things, the creation of cyber-physical production systems, smart enterprises and intelligent manufacturing. At the same time, the issues of conceptual modeling of the architecture of digital enterprises, which determines the construction of manufacturing and business processes, and its economic substantiation depending on various factors of the external environment and internal economic potential have not yet been sufficiently researched and developed. All of the foregoing determines the relevance of the work presented here. The purpose of the study was to develop ontological and economic methods for substantiating application scenarios for the digitalization of manufacturing and business processes depending on the selected types of business models and digital platforms. To solve the problem, methods of classification, ontological engineering, activity-based costing and analysis of cash flows of income and expenses are used. The article presents an analysis of enterprise digitalization scenarios depending on the types of manufacturing and business processes, the types of business models and digital platforms used. An ontological model of enterprise digital transformation has been constructed, providing a choice of application scenarios for the digitalization of manufacturing and business processes for various types of business models and digital platforms. An economic model is proposed to justify options for constructing application scenarios for the digitalization of production and business processes depending on the distribution of roles of participants in network interaction using methods of activity-based costing and cash flow analysis.","PeriodicalId":36213,"journal":{"name":"Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139131558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-30DOI: 10.17323/2587-814x.2023.3.87.100
Anna Zinenko
Financial time series are big arrays of information on quotes and trading volumes of shares, currencies and other exchange and over-the-counter instruments. The analysis and forecasting of such series has always been of particular interest for both research analysts and practicing investors. However, financial time series have their own features, which do not allow one to choose the only correct and well-functioning forecasting method. Currently, machine-learning algorithms allow one to analyze large amounts of data and test the resulting models. Modern technologies enable testing and applying complex forecasting methods that require volumetric calculations. They make it possible to develop the mathematical basis of forecasting, to combine different approaches into a single method. An example of such a modern approach is the Singular Spectrum Analysis (SSA), which combines the decomposition of a time series into a sum of time series, principal component analysis and recurrent forecasting. The purpose of this work is to analyze the possibility of applying SSA to financial time series. The SSA method was considered in comparison with other common methods for forecasting financial time series: ARIMA, Fourier transform and recurrent neural network. To implement the methods, a software algorithm in the Python language was developed. The method was also tested on the time series of quotes of Russian and American stocks, currencies and cryptocurrencies.
{"title":"Forecasting financial time series using singular spectrum analysis","authors":"Anna Zinenko","doi":"10.17323/2587-814x.2023.3.87.100","DOIUrl":"https://doi.org/10.17323/2587-814x.2023.3.87.100","url":null,"abstract":"Financial time series are big arrays of information on quotes and trading volumes of shares, currencies and other exchange and over-the-counter instruments. The analysis and forecasting of such series has always been of particular interest for both research analysts and practicing investors. However, financial time series have their own features, which do not allow one to choose the only correct and well-functioning forecasting method. Currently, machine-learning algorithms allow one to analyze large amounts of data and test the resulting models. Modern technologies enable testing and applying complex forecasting methods that require volumetric calculations. They make it possible to develop the mathematical basis of forecasting, to combine different approaches into a single method. An example of such a modern approach is the Singular Spectrum Analysis (SSA), which combines the decomposition of a time series into a sum of time series, principal component analysis and recurrent forecasting. The purpose of this work is to analyze the possibility of applying SSA to financial time series. The SSA method was considered in comparison with other common methods for forecasting financial time series: ARIMA, Fourier transform and recurrent neural network. To implement the methods, a software algorithm in the Python language was developed. The method was also tested on the time series of quotes of Russian and American stocks, currencies and cryptocurrencies.","PeriodicalId":36213,"journal":{"name":"Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135032121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-30DOI: 10.17323/2587-814x.2023.3.38.52
Lilia Rodionova, Elena Kopnova
An important feature when working with financial data is the fact that the residuals of GARCH-models often have fatter tails than the tails of a normal distribution due to the large number of “outliers” in the data. This requires more detailed study. Kurtosis and quantile-based measure of heavy-tailedness were analyzed and compared in the article in relation to the problem of choosing the GARCH(1,1)-model specification. The data of indices of the Moscow Exchange were considered for the period from April 01, 2019 to February 22, 2022. Kurtosis values ranged from 3 to 52. Empirical data showed that kurtosis was very sensitive to “outliers” in the data, which made it difficult to make assumptions about the distribution of model residuals. The approach considered in this paper based on the heavy-tailedness measure made it possible to justify the choice of degrees of freedom of the t-distribution for the model residuals to explain the fat tails in financial data. It was found that GARCH(1,1)-models with t(5)-distribution in the residuals are common.
{"title":"Application of measures of heavy-tailedness in problems for analysis of financial time series","authors":"Lilia Rodionova, Elena Kopnova","doi":"10.17323/2587-814x.2023.3.38.52","DOIUrl":"https://doi.org/10.17323/2587-814x.2023.3.38.52","url":null,"abstract":"An important feature when working with financial data is the fact that the residuals of GARCH-models often have fatter tails than the tails of a normal distribution due to the large number of “outliers” in the data. This requires more detailed study. Kurtosis and quantile-based measure of heavy-tailedness were analyzed and compared in the article in relation to the problem of choosing the GARCH(1,1)-model specification. The data of indices of the Moscow Exchange were considered for the period from April 01, 2019 to February 22, 2022. Kurtosis values ranged from 3 to 52. Empirical data showed that kurtosis was very sensitive to “outliers” in the data, which made it difficult to make assumptions about the distribution of model residuals. The approach considered in this paper based on the heavy-tailedness measure made it possible to justify the choice of degrees of freedom of the t-distribution for the model residuals to explain the fat tails in financial data. It was found that GARCH(1,1)-models with t(5)-distribution in the residuals are common.","PeriodicalId":36213,"journal":{"name":"Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135032297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-30DOI: 10.17323/2587-814x.2023.3.70.86
Armen Beklaryan, Levon Beklaryan, Andranik Akopov
This article presents a new simulation model of an intelligent transportation system (ITS) for the “smart city” with adaptive traffic light control. The proposed transportation model, implemented in the AnyLogic, allows us to study the behavior of interacting agents: vehicles (V) and pedestrians (P) within the framework of a multi-agent ITS of the “Manhattan Lattice” type. The spatial dynamics of agents in such an ITS is described using the systems of finite-difference equations with the variable structure, considering the controlling impact of the “smart traffic lights.” Various methods of traffic light control aimed at maximizing the total traffic of the ITS output flow have been studied, in particular, by forming the required duration phases with the use of a genetic optimization algorithm, with a local (“weakly adaptive”) switching control and based on the proposed fuzzy clustering algorithm. The possibilities of optimizing the characteristics of systems for individual control of the behavior of traffic lights under various scenarios, in particular, for the ITS with spatially homogeneous and periodic characteristics, are investigated. To determine the best values of individual parameters of traffic light control systems, such as the phases’ durations, the radius of observation of traffic and pedestrian flows, threshold coefficients, the number of clusters, etc., the previously proposed parallel real-coded genetic optimization algorithm (RCGA type) is used. The proposed method of adaptive control of traffic lights based on fuzzy clustering demonstrates greater efficiency in comparison with the known methods of collective impact and local (“weakly adaptive”) control. The results of the work can be considered a component of the decision-making system in the management of urban services.
{"title":"Simulation model of an intelligent transportation system for the “smart city” with adaptive control of traffic lights based on fuzzy clustering","authors":"Armen Beklaryan, Levon Beklaryan, Andranik Akopov","doi":"10.17323/2587-814x.2023.3.70.86","DOIUrl":"https://doi.org/10.17323/2587-814x.2023.3.70.86","url":null,"abstract":"This article presents a new simulation model of an intelligent transportation system (ITS) for the “smart city” with adaptive traffic light control. The proposed transportation model, implemented in the AnyLogic, allows us to study the behavior of interacting agents: vehicles (V) and pedestrians (P) within the framework of a multi-agent ITS of the “Manhattan Lattice” type. The spatial dynamics of agents in such an ITS is described using the systems of finite-difference equations with the variable structure, considering the controlling impact of the “smart traffic lights.” Various methods of traffic light control aimed at maximizing the total traffic of the ITS output flow have been studied, in particular, by forming the required duration phases with the use of a genetic optimization algorithm, with a local (“weakly adaptive”) switching control and based on the proposed fuzzy clustering algorithm. The possibilities of optimizing the characteristics of systems for individual control of the behavior of traffic lights under various scenarios, in particular, for the ITS with spatially homogeneous and periodic characteristics, are investigated. To determine the best values of individual parameters of traffic light control systems, such as the phases’ durations, the radius of observation of traffic and pedestrian flows, threshold coefficients, the number of clusters, etc., the previously proposed parallel real-coded genetic optimization algorithm (RCGA type) is used. The proposed method of adaptive control of traffic lights based on fuzzy clustering demonstrates greater efficiency in comparison with the known methods of collective impact and local (“weakly adaptive”) control. The results of the work can be considered a component of the decision-making system in the management of urban services.","PeriodicalId":36213,"journal":{"name":"Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135032298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-30DOI: 10.17323/2587-814x.2023.3.53.69
Georgij Zavalin, Olga Nedoluzhko, Konstantin Solodukhin
The development of intellectual capital theory through the introduction of the concept of implicitness involves considering intellectual capital as an implicit factor, so that the process of its formation is largely determined by the impact of specific hidden factors whose impact is expressed implicitly and is difficult to formalize. Currently, the process of selecting explicit and implicit factors affecting intellectual capital is not formalized in domestic and foreign studies, and therein is the relevance of this work. The purpose of this study was to develop a scheme for selecting explicit and implicit factors in the development of the organization’s intellectual capital in conjunction with its strategy based on a modified Balanced Scorecard, taking into account the distribution of indicators by types of cognitive activity. The implementation of this scheme was carried out by developing a fuzzy economic and mathematical model suitable for practical use. The main feature of the model is the possibility of fuzzy setting of “cut-off boundaries” for explicit and implicit factors. We present the results of testing the model on the example of a large regional university. Sets of explicit and implicit factors of the university’s intellectual capital are given for various “cut-off boundaries” using various defuzzification methods.
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