The purpose of the study is to assess the effectiveness of public-private partnership (PPP) projects during the COVID-19 pandemic, taking into account the specificities of the industry. The empirical research base included the main characteristics (contract price, federal district of the project, customer, general contractor, project start period, project start year, project deadlines, price reduction during project implementation, application security, contract security and type of activity) of 144 regional investment projects. The research methodology included mathematical modeling using the DEA (Data Envelopment Analysis) data convolution method, on the basis of which an efficiency index was assigned to each PPP project from 0 to 1. The study concluded that prior to the COVID-19 pandemic, regional investment projects received more funding; after the start of the COVID-19 pandemic, the implementation time of regional investment projects has increased, the contract value more often changes downwards during the project implementation; and the average support for a project application has increased. The efficiency of implementation of regional investment projects, calculated using the DEA method, was reduced. The results obtained will be useful to private investors and government authorities when implementing joint projects to improve their efficiency.

Financial development has a significant impact on the restructuring of the economy, long-term economic growth and improvement of the level and quality of life of the population. In this regard, this study aims to address the challenge of adequate measurement and assessment of the level and dynamics of financial development a relevant task for public administration. The goal of the study is to develop a system of indicators to measure the level and dynamics of financial development of countries. These indicators could improve the effectiveness of public decision-making in the sphere of finance. The research used the methods of systemic, comparative, and matrix analysis. As a result, the authors present a matrix system of financial development indicators, which characterizes the levels, dynamics, and interrelationships of financial development in the country as a whole, and in the context of financial market segments and sectors of the economy. This system reflects the real value of financial assets per capita, thereby, providing the scientific novelty of the study and increasing the objectivity of the results of analysis and evaluation. The developed indicators were tested with regard to the Russian Federation for the period of 2013–2021 using statistical data of the System of National Accounts in terms of financial balances. The results made it possible to determine the level and dynamics of financial development of the Russian Federation, to identify the sectors of the economy and financial instruments that contributed most to financial development in 2021; the sectors and instruments that impeded financial development; as well as to determine prospective directions of financial development in the near future. The use of new indicators will improve the comprehensiveness and quality of the analysis of financial development, as well as ensure the adoption of researchbased and effective decisions in the design of state strategic documents.

The purpose of the study is to justify the use of the invariance property of the index method to study the change in the structure of tax revenues of Russian regions in the period from 2017 to 2021. The object of the study is eighty-five regions of the Russian Federation, and the subject is their financial and economic activities. Data from Rosstat and the Russian Federation’s FTS were used for the analysis. To date, the index method is actively used in the conduct of economic analysis at the macro- and meso-levels. The novelty of the study is that only the authors of the article on the basis of indices monitor the state of activity of the regions of the country, based on their tax revenues. The quantitative analysis is implemented using the statistical processing and data visualization functions of the R programming language. The intersubjective comparison was done to identify areas that require financial and economic transformation to improve the activities of the country’s regions. The comparison is made not only for one time, but also in dynamics. The results of the statistical analysis showed that the proposed tax income effectiveness index is an invariant indicator, independent of time and changes in the amount of tax income. It follows from the stationarity of the considered feature that the index values for 2017–2021 can be combined into a single homogeneous statistical aggregate. It was concluded that the index of effectiveness could be used as a grouping feature for the classification of Federation entities. The methodology developed can allow to intensify the socio-economic growth of the regions, indicating points requiring changes. In this regard, the results of the analysis can be useful to: the Ministry of Finance of the Russian Federation and the Federal Tax Service of the Russian Federation for the development of financial and tax policy; the Ministry of Economic Development and administrations of the subjects of the Russian Federation, indicating the economic zones of regions that need to be improved; to representatives of the business community when conducting economic analysis of regions.

The relevance of the study is due to the fact that in the context of the spread of new models of business organization, including foreign trade transactions with electronic commerce goods (ECG) purchased by individuals on foreign electronic trading platforms, it is necessary to develop issues related to the determination of tax consequences for persons — participants of such models. The term “goods” in this study means any movable property, including currency of the member states of the Eurasian Economic Union (EEU), securities and (or) currency values, travel cheques, electricity, as well as other movable things equated to immovable property. Since the value added tax (VAT) is one of the most significant for both the state and taxpayers (tax agents), the subject of the study is the mechanism for determining the tax consequences of VAT when foreign sellers sell to Russian individuals electronic commerce goods (ECG) purchased through foreign trading platforms (“marketplaces”), while being (stored) at the time of conclusion of the contract of sale in customs warehouses in Russia. The purpose of the study is to solve the problems related to the determination of VAT payment obligations in connection with the sale of ECG stored in a customs warehouse, namely: economic aspects related to the grounds for the emergence of VAT payment obligations, and the feasibility of changes in the current regulation with regard to the possible consequences. The methodology of the study is based on the use of classical for indirect taxation approaches to the determination of the place of sale of goods and, accordingly, to the decision on the occurrence in the territory of Russia of the object of taxation by the VAT. The scientific novelty of the study consists in the development of approaches to the determination of tax consequences on VAT on the sale of goods from the territories of customs warehouses within the framework of cross-border electronic commerce, as well as possible changes in the current regulation, based on the consideration of the economic sense of the considered business model. It is concluded that when a foreign seller sells goods to Russian individuals through a “marketplace” that are stored in a customs warehouse on the territory of Russia during the purchase period, the seller is subject to VAT. A person of an EAEU member state (Russian organization) — an e-commerce operator — who transfers goods to a purchaser is obliged to present to the purchaser the corresponding amount of VAT, performing the duties of a tax agent. Proposals on the establishment of VAT concessions for transactions on the implementation of ECG from the territory of the customs warehouse were elaborated, the results concluded that the discussion and insufficient economic justification of such proposals.

An important target of the UN Sustainable Development Goals (SDGs) is the efficient use of the planet’s resources. In this study, the authors show a strong exponential relationship between the economic complexity index and the efficiency of resource use in a country. The economic complexity index is a characterization of the productive capacity of large economies. This index measures the level of knowledge accumulated by a society that enables production. Assessing the level of a country’s index also makes it possible to predict future trends in the region’s economy. The model of economic sophistication index proposed by the authors includes the service economy, retail trade and manufacturing. Thus, in the paper, the authors identify how the economic complexity index affects the product level by defining the product space for each country and identifying the main products that contribute to a high product complexity index and prospective scalability, indicating the potential to produce better products in the future. Policies focused on increasing economic complexity and investing in staple products appear to be a priority for achieving sustainable development.

Taking into account the conditions of the real functioning of companies, one of the most striking effects in financial management is investigated: the “golden age” of the company (when the cost of capital raised is below the perpetuity limit, and the company’s value is higher). With this aim the dependence of cost of raising capital, WACC, on the age of company, n, is studied at various leverage levels, at various values of equity and debt costs, at different frequencies of tax on income payments, p, with advance payments of tax on income and payments at the end of periods, at variable income of the companies. The existence of the weighted average cost of capital, WACC, minimum and its behavior at wide range of above parameters is investigated. All calculations are made within modern theory of capital cost and capital structure by Brusov-Filatova-Orekhova (BFO theory), generalized to the conditions of the real functioning of the company. Practical recommendations for using and maintaining the “golden age” effect are given. It is shown, that “the golden age” depends on the financial indicators of the company. It can change and be controlled by changing parameters such as the cost of capital (equity and debt), frequency and method of tax on income payments, growth income rate etc. The study of the dependence of WACC on the age of the company n, WACC(n), which can only be carried out within the framework of the BFO theory, turns out to be very important in the income approach to business valuation. This allows you to link a retrospective analysis of a company’s financial condition with a representative analysis as part of a business valuation.

The purpose of this research was to assess the practise and effect of pricing strategy on the brewery company’s performance. To achieve the study’s objective, the researcher applied both descriptive and explanatory research designs using a mixed-methods approach. For the research, both first-hand and second-hand sources of information were gathered. This research involved 310 employees of Ethiopia’s brewery companies who completed questionnaires to obtain data. The study used simple random sampling. Using SPSS version 21.00, descriptive statistical methods, such as mean and standard deviation, and inferential statistical techniques, such as correlation and multiple regression analysis, were used to analyse the questionnaire data. According to the findings of this research, pricing strategy has a statistically significant and strong positive relationship with organisational performance. Moreover, the pricing strategy explains 74.5 percent of the variances in organisational performance in a substantial manner. The study will help firms establish an effective pricing strategy to increase performance and compete in the marketplace. Dealers should base their price selections on this context, set fair and competitive rates, and clearly explain these charges to consumers. The company might use price promotion strategies like discounts, bonuses, and bundles to increase the number of units sold to customers. To expand market share and sales volume, it is also necessary to use a pricing penetration strategy.

The purpose of the study is to determine the influence of a business group on the assessment of the borrower’s creditworthiness, as well as to identify the most significant credit risk factors. Despite the fact that creditworthiness assessment is widely disseminated in both domestic and foreign literature, the impact of the consolidated group in the context of this problem is practically not mentioned. The authors use a statistical modeling method using logistic regression. The variable models are based on the annual financial statements of both individual companies and business groups. To select factors and build a model, approaches used in statistics and machine learning were used to obtain unbiased and effective estimates, independent of the sample generating these estimates. Analyzed data of 8691 companies providing annual financial statements in accordance with Russian accounting standards from 2015 to 2021. The total sample size was 22 201 observations. The number of bankruptcy events in the sample is 238 observations. Variables calculated from consolidated financial statements in accordance with international standards were used as information about the group. Various views on the concepts of “business group” and “holding” in the domestic literature are considered and systematized. Features of the behavior of companies united in groups are given. Variables associated with the business group that are significant in assessing the probability of bankruptcy of individual companies have been identified. Various specific aspects of the activities of companies associated with the group are mentioned. A statistical model is constructed to confirm a number of hypotheses, which is subject to verification and analysis. The bankruptcy event is used to determine the significant deterioration of a company’s creditworthiness. It is concluded that the use of group reporting data can improve the quality of model prediction for companies associated with a business group.

The construction sector today accounts for the largest volume of public procurement, bankruptcies and corporate fraud. Practice shows that the methods used to identify unscrupulous companies are not effective enough, which determines the relevance of the development of integrated risk assessment tools for financial security. In this regard, the purpose of the study was to substantiate the risk-factor approach to assessing and diagnosing financial security risks using a comprehensive analytical toolkit developed based on the concepts of industry analysis, professional auditing standards and financial reporting. The scientific novelty of the study is to integrate methods of assessment of financial security risks, the application of basic data processing technologies and flexible situation modeling with the possibility of adjusting models to a specific situation, taking into account identified industry risks. The theoretical significance of the study is represented by the concept of financial security as an aggregate result of the action of identified risk factors in the conditions of a highly turbulent economy, which served as a navigator for the development of an analytical tool that ensures consistency of the interpretation of the results at the stages of industry analysis, assessment of the likelihood of bankruptcy by logit-model and diagnosis of corporate fraud risks based on indicators of probability of distortion of financial statements. The practical value of the toolkit is the application of the international classification of financial risks, the selection of adequate, statistically significant indicators, calculated on the empirical basis of the financial statements of companies in the construction industry, downloaded from the Spark-Interfax information resource, which also proves the relevance of the results obtained and the possibility of using for the selection of organizations — potential participants in public procurement, in the audit practice, related audit services, arbitration practice, investment and banking to identify unscrupulous borrowers.

The purpose of the study is to determine the impact of the COVID‑19 pandemic on the market response, measured by abnormal returns, cumulative abnormal returns and average abnormal returns. The authors use OLS events and regression analysis methods to measure market response at three-time intervals: in the beginning of COVID‑19, during the onset of Delta and Omicron viruses. OLS is used to measure the capital market reaction in the window (–10, +10) for each industry index. The results of the study show that investors reacted very sharply to the onset of COVID‑19, which caused high volatility in the market. Most abnormal returns after the pandemic announcement reacted negatively. Only three sectors — consumer, infrastructure and trade — were in the safe zone. At the same time, the spread periods of Delta and Omicron viruses are characterized by slight differences in the average abnormal yield after the announcement. The results of a study in three time frames concluded that the market response was significant only to five-day (0, +5) ads based on AAR and CAAR.

“Demonetisation” means the removal or rejection of one or more legitimate payment methods in the economy. This term is as old as the use of currency, which can be traced back to 7th and 6th centuries BCE. The Indian government conducted an unannounced demonetization in 2016. Similar cases were observed in India in 1946 and 1978. Overall, the world has witnessed dozens of demonetisations in different countries, which were carried out for different reasons. Many were successful, but many were not. We need to understand why demonetisation has been implemented over the world. This study used a literature review method to determine the causes of demonetisation in nations such as Australia, Canada, New Zealand, Libya, Ghana, Myanmar, Zaire, Russia, North Korea, Pakistan, Sweden, Zimbabwe, and Belarus. It also describes the goals of Indian demonetisation in 2016 and determines whether they have been met. As a result, the authors found that pre-announced demonetisation were usually effective, whereas most undeclared demonetisation failed and had an impact on the economy and population. The results of this paper can help governments, policymakers and scientists to understand the purpose of demonetisation and the need for caution. The authors concluded that demonetisation could have both positive and negative effects, depending largely on the intentions of the country’s leadership and on the preparedness for demonetisation.

The significance of foreign direct investment (FDI) for the expansion and advancement of emerging economies has long been recognized. Yet, research on the factors that influence FDI inflows is still developing. This study focuses on examining the long- and short-term association between FDI inflows and its determinants, employing ARDL bounds testing approach and Error Correction Model to understand the relationship between the variables under study. The findings evidence the existence long- and short-term association between FDI and domestic investment, inflation, infrastructure, and trade openness. However, market size is observed to be insignificant in influencing FDI inflows. The coefficients of domestic investment, infrastructure and Trade Openness are observed to be significantly positive. The influence of inflation is found to be negative. The study suggests that the Indian economy should accelerate the process of integration with the world economy along with the enhancement of domestic investment and infrastructure facilities to attain higher FDI.

The financial embargo has a special impact on the economic systems of oil and gas regions. This is explained by the increased foreign trade turnover of oil and gas regions, as well as the high capital intensity of the oil and gas industry, a long period of return on financial investments, and the high profitability of innovative investments. The purpose of the study is to identify various aspects of the problem of investing in fixed assets in oil and gas regions, find ways, forms and methods of investment stimulation of their innovative production development and assess the possibilities of transitioning to new technological structures in the conditions of a financial embargo and the transformation of the global energy balance. Methods of regression analysis of the structure and dynamics of investment in fixed capital of organizations in the oil and gas regions of the Volga Federal District were used and polynomial trend lines were constructed until 2030. An economic analysis of the structure of investment in fixed capital of oil and gas regions was made by sources of financing, types of fixed assets, forms of ownership, types of economic activity, as well as a regression analysis of the dynamics and forecast of the balance (receipt minus withdrawal) of foreign direct investment in oil and gas regions according to the balance of payments of the Russian Federation. The result of the study was the development and justification of a system of priority factors for creating a favorable investment climate in oil and gas regions to increase their resistance to the conditions of the financial embargo and the economic transformation of the global energy balance.

This research examines the use of artificial intelligence (AI) as a financial risk management tool. The concept is motivated by the revolutionary effects that financial technology has on business operations. Traditional methods of financial risk management are no longer effective and require revision. The purpose of the study is to assess the role of artificial intelligence in the management of financial risks and offer recommendations for its further use in the financial sector of the economy. Methodological analysis of relevant scientific literature showed that AI, in particular machine learning, can help in managing financial risks. It has been concluded that AI improves the management of market and credit risks in model verification, risk modelling, stress testing and data preparation. AI helps to monitor the quality of information received, detect fraud and search for the right information on the Internet. In the future, financial technology will continue to influence the financial sector as operating companies modify their operations. Thus, financial risk management tools will include AI. The study examines the possibilities of AI use in financial (market and credit), risk management and operational sectors (business continuity and emergency recovery). The paper presents the most promising AI technologies and techniques such as RPA, Data Management, Blockchain, MRL, MRC, CRU, Deep Learning, OML, Modelling and Stress Testing, Machine Learning and Algorithms, Neural Networks, Decision Trees, CPM, CRA, Black Box, etc. to improve “Financial Risk Management (FRM)”.

The study’s relevance is due to the need to identify the scale and channels of the spread of crises in the economy based on the use of the financial contagion methodology. Understanding the mechanism of spreading financial contagion from one industry to another can help develop anti-crisis measures and ensure stable economic indicators. The purpose of the study is to assess the intersectoral financial contagion in the Russian economy during the spread of the coronacrisis, as well as to correlate the estimates obtained with the actual incidence of COVID‑19 in the Russian Federation. The novelty of the research lies in the development of the methodology of financial contagion and its use in relation to sectors of the Russian economy, where they are considered transmitters and/or receivers of financial contagion. The methodology of advanced correlation analysis was used — the Forbes-Rigobon sliding test was implemented, which made it possible to assess the scale and intensity of financial contagion in the Russian economy. We used high-frequency data on 8 MICEX industry indices and on the incidence of COVID‑19 in the period 2020–2021. The result was quantitative assessments of financial contagion, which showed that such industries as metallurgy, oil and gas sector, consumer sector, electric power industry had the highest susceptibility to financial contagion. Telecommunications, the financial sector, chemicals and petrochemicals, and transport have demonstrated resistance to the pandemic shock. The most powerful transmitters of financial contagion were the electric power industry, metallurgy, transport, and the financial sector. In general, the financial contagion in 2020–2021 between the sectors of the Russian economy spread unevenly, in some cases and in certain periods, the ups and downs of financial infectivity went in parallel with the ups and downs of the real incidence of COVID‑19. The main conclusion was that during the pandemic, financial contagion spread with varying intensity, and individual industries manifested themselves either as receivers or transmitters of financial contagion. At the same time, there was no large-scale financial infection of the sectors of the Russian economy.

The relevance of the study is due to the fact that in the conditions of the financialization of the economy, shocks arising in one market can spread rapidly and intensively to other markets, generating the effects of financial contagion. This fully applies to the commodity markets, which occupy a large share of exchange trading. The resulting excess volatility risks should be taken into account both by financial market players when developing optimal portfolio strategies, and by the state when adjusting anti-crisis policy. The purpose of the study is to identify financial contagion in commodity markets during periods of financial stress caused by the pandemic and sanctions, to determine the direction and extent of intermarket contagion. The novelty of the study lies in the construction of stress indices to separate periods of increased volatility in commodity markets, in the application of statistical tests for the co-moments of the return distribution to identify the financial contagion between the markets of energy (oil and gas), precious and non-ferrous metals during the pandemic and sanctions. The result of the study is the identification of a period of increased volatility in commodity markets and its division into two sub-periods based on turning points in the stress index, establishing the direction and extent of financial contagion between commodity markets during these periods. It is concluded that stress in commodity markets is accompanied by intense financial contagion. Moreover, volatility contagion turns out to be higher than return contagion and even higher than contagion caused by anomalies in the return distribution. The main sources and receivers of contagion in different periods are the markets of precious and some non-ferrous metals, and in the period from February 2018 to December 2020, also the oil market. At the same time, the gas market before SMO has demonstrated relative independence from other commodity markets, which made it possible to recommend gas futures as a tool for hedging investment portfolios during a period of increased financial stress.

The life cycle of an artificial intelligence model is the object of research. The purpose of the study is to develop a model life-cycle methodology that describes the economic content of the investment process in artificial intelligence technology. During the study, both general scientific methods such as analysis, synthesis, comparison, abstraction, induction and deduction were used, as well as project methodologies of the life-cycle, employed as the basis for the value creation life-cycle of the model. The analysis was based on identifying the necessary stages of model development in terms of the CRISP-DM methodology and determining the features of each of them in terms of cash flows. Modified versions of the model life-cycle containing risk assessment, including model risk, were also taken into account. In the process of research, the proposed generalized model life-cycle methodology was specified for a specific AI technology — large language models. As a result of the study, the author proposed a three-stage model. The possible optionality between the stages and the characteristics of cash flows are described. It was concluded that an investment project for the development of AI contains several real options — abandonment, reduction, expansion and replacement. For large language models, the life cycle structure and possible optionalities are preserved. The peculiarity is that the value creation process involves cash flows from different areas of application of the model in business processes. The results of the study are of practical importance for medium and large businesses engaged in the independent development of AI models and/or applying them to their business processes. The proposed concept of the model life-cycle can also be used to develop a methodology for evaluating investments in AI using real options.

The crisis caused by the COVID‑19 pandemic has clearly demonstrated the importance of financial resilience for households. It is necessary as a measure of preparedness for the economic shocks that may arise, especially in connection with the recession problem, which has been increasingly discussed recently. Financial resilience can be formed through resilience-building financial behaviors, including saving behavior. This study investigates the predictors that shape household saving behavior in a digital context by adopting the Theory of Planned Behavior (TPB), i.e., how human behavior is guided. This model also adds digital financial literacy (DFL) as an extension of the TPB. The digital context is taken because of the oblique shift in financial behavior with the rise of Digital Financial Services (DFS) in society. This study used a survey method with a well-structured questionnaire. The reflective measurement was performed using the Partial Least Square Structural Equation Model (PLS-SEM). Analysis was conducted on respondents in Java, Indonesia, to the survey’s screened data (N = 900). The study results show that all predictors influencing household decisions to save include all predictors of TPB and DFL. In the mediating effect, the intention toward saving behavior act as a partial mediating variable on the relationship between exogenous and endogenous variables. This study suggests policymakers, government, and educational institutions provide DFL to households.