The purpose of the study is to systematize factors of successful and sustainable functioning of special economic zones, as well as to develop tools to map of zones on a map of a country with the competitive advantages of individual territories identified therein in order to make recommendations for improving the zonal policy. The relevance of the study is due to the fact that in the context of global shocks, a special preferential regime, flexible governance models of special economic zones are not determinants of their successful application in order to promote national investment strategies. The geo-economic advantages of the territories within which the special economic zones operate come to the fore. Such advantages are the basis for the formation of poles of economic, commercial, industrial and innovative growth. The use of methods of theoretical (analysis, synthesis, generalization) and empirical (comparison, measurement) research allowed the authors to reveal the content of effects associated with the functioning of special zones, to highlight the problems of their measurement; to generalize the features of preferential regimes of Russia; to systematize the geo-economic factors of successful and sustainable operation of special zones. The method of geo-economic mapping was used to identify the correspondence between the competitive advantages of territories and special zones created within their borders. As a result, it is proposed to classify key geo-economic factors that determine the potential successful functioning of special zones into three groups: spatial, economic and organizational. These groups of factors, according to the authors, should be considered in terms of formation and retention of geo-economic advantages: general, caused by public management and specialized. The method of geo-economic mapping identifies regions whose special zones correspond to the level of development of the identified geo-economic advantages, as well as those whose conditions are most likely not to maximize the effect of the special zones localized in their territory. It is recommended to establish a system for monitoring the conformity of specialization of regions with the profile of special economic zones established within their borders.

The object of the study is regional investment projects (RIPS). The subject of the study is a methodological toolkit for assessing the investment attractiveness of regional projects, including criteria, indicators, methods and stages of making informed decisions about government financial support measures in relation to them. The relevance of the study is due to the state’s interest in the socio-economic development of regions under the conditions of sanctions pressure and the need to ensure effective spending of budget funds allocated to regional investment projects, which requires the formation of new methodological recommendations for evaluating projects implemented within the framework of state financial support measures. The purpose of the study is to develop methodological recommendations for screening and evaluating regional investment projects in order for public authorities to make informed decisions on providing financial support. The methods of comparative analysis, classification, regulatory regulation, statistical indicators, screening and investment assessment, and the method of hierarchy analysis were used. Methodological recommendations on screening assessment of regional investment projects are proposed, within the framework of which: 1) the characteristics of RIP are identified and their classification is considered; 2) the criteria for assessing the investment attractiveness of the project and its contractor (partner) are defined: general (the purpose of the RIP, its significance, the quality of project documentation) and special (economic, budgetary, social, environmental efficiency, performance feasibility, compliance with ESG principles of doing business, business image); 3) evaluation indicators and their thresholds, the achievement of which means the expediency of investing budget funds in the project. It is concluded that in order to make a decision on the provision of state financial support to RIP, it is necessary to achieve target values by indicators corresponding to three components: “State” (customer), “Project” and “Partner (contractor). At the same time, using the hierarchy analysis method, it was found that the investment attractiveness of the contractor has the greatest importance (weight) when choosing a project. The choice of the performer is based on screening of applicants according to the specified criteria.

The stock market is unstable, but the use of machine learning algorithms allows to predict its future dynamics before spending. The most popular area of scientific research right nowadays is machine learning, which involves enabling computers to perform tasks that often require human intelligence. The purpose of this paper is to construct a model using a network of Long-Short Term Memory model (LSTM) to forecast future stock market values. The paper presents the advantages and disadvantages of machine learning for assessing and forecasting the stock market. A review of literature on the application of machine learning models in key areas of finance using methodological model assessment and data manipulation is also available. This paper focuses on the losses of the SME sector due to COVID-19 by doing a comparative study using secondary data collection between the predicted closed stock prices and actual stock prices of the BSE SME IPO index for the period from 1 January 2018 to 30 April 2021.The LSTM network of Recurrent Neural Networks (RNNs) most effective deep learning model, is used to predict stock prices. The study provides insight and direction on where lockdown has a massive impact on the stock prices of BSE SME IPOs. The authors developed a model for predicting the future value of stock in the market, the application of which gave some positive results, demonstrating the need for machine learning and how it can change the world of finance. The novelty of the study is that in India, machine learning and deep learning methods in the field of finance are used much less often than in other countries.

A stable financial system acts as a catalyst for the economic growth and development of a country. The healthy banking sector is the core of a sustainable economy as banks act as intermediaries between depositors and lenders of money. In the surge of the COVID-19 pandemic, the financial sector witnessed significant transitions in terms of digital transformation. In India, the banking sector has remained resilient throughout the pandemic due to government and regulators’ policy efforts and the maintenance of capital adequacy requirements. Banks have maintained higher capital buffers, better liquidity requirements, and lower leverage, cushions against pandemic shock. In the present paper, the researcher provides a conceptual elucidation of Basel norms, analyzes the component-wise Capital to Risk-Weighted Asset Ratio (CRAR) of Indian Scheduled Commercial Banks (SCBs) and examines the CRAR position of SCBs during the COVID-19 pandemic. The study also evaluated the distribution of SCBs by CRAR and examined the capital ratios of public, private, and foreign sector banks from 2016 to 2022. The ANOVA analysis output revealed a significant difference in the CRAR of public, private, and foreign banks. The study concludes that adequate CAR levels help banks mitigate the risks that arise during pandemic crises and aid them in conducting their banking operations effortlessly. Further, it concludes that public sector banks (PSBs) still lag behind their counterparts in maintaining adequate CRAR, and hence, they need to reduce the accumulation of risk-weighted assets (RWA).

The paper is devoted to improving the methodology for conducting laboratory experiments to study the actions of taxpayers. We note that the use of standard economic methods is not enough to study citizens’ behavioral motives (in particular, the desire to evade their duties). The authors analyzed experimental methods of studying tax evasion, carried out their comparative characteristics and identified the problems of their implementation in practice. Based on the analysis of the results of previous experiments, we proved that involving students as interviewers enables us to identify and evaluate the behaviour trends of taxpayers. The research methodology is based on the use of tools and methods of comparative analysis, tabular and graphical methods of data visualization. In particular, the comparative characteristics of the form factor surveys (vignette with one profile, vignette with a double profile, single-profile association, conjugate profile, conjugate paired profile) made it possible to identify as a priority for use in laboratory tax experiments the conjugate paired profile in the form of a survey. In the resulting part of the paper, we presented the disadvantages of laboratory experiments and suggested possible options for their solution, which is an element of scientific novelty and the significance of the research’ results.

The subject of the study is a set of measures of tax incentives for the information technology industry in the Russian Federation — “tax maneuver in IT”, launched from the beginning of 2021. The purpose of the study is to identify and qualitatively assess the stimulating effect of the tax maneuver in IT, which is expressed in changes in key financial and natural performance indicators of IT industry organizations, and to develop proposals on ways of tax incentives for the development of the industry. The article describes the distortion of the aggregate tax reporting characterizing the IT industry, which takes into account not only recently established IT organizations, but also “nominal” separates from large organizations IT subdivisions or technically clarified classification code, making it inappropriate to use such reporting as a basis for analysis and reliable conclusions. Based on the data of the public financial statements of selected sample from the top-100 Russian IT organizations, the article analyzes the dynamics of financial indicators of their activities, among which are the profiles of profiles, operating profit, net profit, investment in basic assets, the number of staff, the amount of products supplied for export, the capitalization of the company. The observation was conducted for 2017– 2022 and covers the periods both before and during the engagement of tax benefits. A comparative study was carried out with similar indicators of the organizations of the “control group”, which included IT companies operating in other countries that were not affected by such tax benefits. According to the results of the study, small or no extra growth was observed in key financial indicators of IT organizations due to the tax maneuver compared with the “pre-maneuver” period and compared with the indicators of the control group. It is concluded that there is no evidence of a significant impact of the tax maneuver on the development of the IT industry in Russia. We proposed dismantling of the “maneuver” and transition, based on the Chinese and some EU countries approaches, to taxation of the qualified profit. The latter is the profit of Russian and foreign IT developers from the localization of IT development and value creation in Russia. The achievable effective rate is 2.5%.

The study is devoted to the use of central bank digital currencies in cross-border settlements. The purpose of the paper is to identify the capacity of cross-border settlements using multi-CBDC/mCBCD mechanisms based on different interoperability models. The study identified the main problems of modern cross-border settlements and the possible risks associated with the implementation of mCBDCs. The features of various models of interoperability in mCBDCs arrangements are revealed and prospects of their use are defined. It was concluded that the main problems of traditional cross-border settlements are legacy technology platforms, fragmented data presentation formats; complex processing of compliance checks; long transaction chains and etc. It was identified that the main risks associated with the implementation of mCBDCs are: “digital dollarization”, international “spillover effects” of economic and financial shocks, the use of digital currency for tax evasion and supervision of the domestic monetary system and financial market, etc. The obtained results allowed us to conclude that among the three main models of interoperability of mCBDCs, the single system model is the most prospect, since it allows to mitigate of cross-border and cross-currency risks, expand opportunities for infrastructure integration and technical compatibility, reduce the number of intermediaries and improves the security of settlements. In order to successfully implement mCBDC projects, in addition to the chosen operating model, a sufficiently high overall level of technological and infrastructural development of national CBDC systems, as well as economic and geopolitical interest in carrying out cross-border settlements between participating countries.

The digital transformation of the world economy has updated the issues of using digital technologies in the system of cross-border payments. The key moment was the introduction of virtual currencies into monetary systems and payment turnover. The circulation of cryptocurrencies along with fiat money in the global economy has created paradoxical situations that require research both scientific, theoretical and applied context. The subject of the study is the payment functionality of digital currencies. The purpose of the study is to explore the possibility of using central bank cryptocurrencies and digital currencies for cross-border payments within the existing traditional financial system, both in the general context and with the application of sanctions restrictions that hamper international settlements in traditional methods. In the process of research, content analysis, retrospective analysis, methods of logical and comparative analysis were used. This study is one of the first to explore the possibility of using bitcoin in cross-border payments under sanctions restrictions. It is shown that the volatility of cryptocurrencies is the most important characteristic that limits their use as payment instruments, but this risk can be neutralized by using cryptocurrency as a transit instrument, as a temporary intermediary in the exchange of fiat currencies. The conclusion is made about the possibility of using cryptocurrencies as a transit instrument in the implementation of exports and imports settlements in toxic currencies of unfriendly countries, and to use the Central Bank Digital Currencies in cross-border settlements within the framework of integration and non-integration interstate associations with the participation of Russia.

The aim is to present the results of the development of a modified method of chain substitutions, which is based on the use of the arithmetic mean sum of the results of the influence of each factor on the indicator of interest, taking into account the priority of each factor in all possible variants. At the same time, from the point of view of accuracy, the results obtained using the modified technique practically do not differ from the results of the integral method, however, they exceed it in terms of using a simpler mathematical apparatus. The relevance of the work is determined by the fact that in modern economic conditions (noticeably increased inflation, problems with energy prices), the issue of applying methods of deterministic factor analysis of expenses and incomes becomes especially significant in order to determine the size of the impact of each factor on a specific economic indicator as accurately as possible. However, the chain substitution method used in the vast majority of cases for deterministic factor analysis is inferior in accuracy to the integral method. The scientific novelty of the work lies in the fact that the author uses strict mathematical proofs of the coincidence of the accuracy of the results of the modified methodology and the integral method for various types of deterministic factor models (additive, multiplicative, multiple), which are supported by real practical calculations. Conclusions: the proposed modified method of chain substitutions, due to its mathematical simplicity and proven accuracy of the results obtained, can be widely used in real practical calculations using methods of economic analysis, especially taking into account the computer implementation of algorithms developed in this technique.

The famous Capital Asset Pricing Model (CAPM), widely used in practice, takes into account only the business risk associated with investments in a specific company [not the entire market (or industry)]. In practice, most listing companies use debt financing and operate at a non-zero leverage level. This means that the financial risk associated with the use of debt financing, along with business risk, must be taken into account. The purpose of this paper is to simultaneously account for business and financial risk. We combined the CAPM theory and the Modigliani-Miller (MM) theory, which is the perpetual limit of the BFO (Brusov-Filatova-Orekhova) theory. The article shows that R. Hamada’s attempt to take into account both business and financial risks has proved unsustainable, and the formulas he obtained, widely used in practice, are incorrect. The paper outlines the correct formulae that made it possible to generalize CAPM for the first time, taking into account both business and financial risk. The application of the new CAPM 2.0 model to a number of companies is considered and the difference between the results obtained within the framework of CAPM 2.0 and CAPM is demonstrated. CAPM is one of the main models [along with APT (arbitrage pricing theory) and WACC] within the income approach to business valuation. This significantly increases the value of the developed CAPM 2.0 approach, which can significantly improve the accuracy of the assessment.

In assessing the risk of investing in various financial assets, risk management focuses on the analysis of the worst possible losses (the right tail of the loss distribution). At the same time, most often, when speaking about losses, it is assumed that losses can, in principle, take on negative values (which corresponds to receiving positive profits). However, there are many theoretical studies suggesting that losses take only positive values. Many risk managers use only a portion of the sample of data that corresponds to positive losses when assessing the relevant risk measures using the statistical method or the Monte Carlo method. The purpose of this paper is to study the transformation of risk estimates of various levels of catastrophicity with such a change in the space of elementary events, and hence the law of loss distribution. The paper uses methods of analysis of financial risks of various levels of catastrophicity, including methods developed in the author’s previous papers. As a result of the study, it turned out that with such a transformation of the random value of losses, all the most important estimates are significantly transformed with the help of risk measures of various catastrophicity. The author concludes that the theoretical conclusions of the work will also contribute to a more conscious understanding of the theoretical results and the results of practical risk assessments, depending on the basis on which this assessment was made: allowing losses to accept negative values or focusing only on their positive values.

The sustainable development of the agro-industrial complex is a priority task and a factor in ensuring food security in Russia. The relevance of the study is due to the lack of transparency and limited existing ratings of agribusiness companies, the lack of consideration of the impact of sanctions, their consequences and the ability of companies to promptly reconfigure their business models. In this regard, the purpose of the study was to form a methodology for assessing the sustainability of the development of agribusiness companies in modern conditions. The presented methodology is based on the principles of prioritizing the impact of sustainable development criteria on competitive opportunities; availability of accessible information, its regularity and understandability for users; using the risk factor approach as a navigator for assessing competitive positions. The research methodology is based on an industry approach, followed by an assessment of the impact of identified risk factors on trends in production indicators, market share dynamics, efficiency of operating, financial and investment activities, and business development rates. To visualize the results, the method of constructing competitiveness polygons was used, which provides a clear assessment of the competitive advantages and management abilities of companies to quickly adapt to changing market conditions. The scientific novelty of the study lies in the development of a situational approach to assessing the sustainability of the development of agribusiness companies, based on the impact of identified industry risk factors on business performance. Approbation of the methodology was carried out on the companies, which are participants in the credit ratings for the agro-industrial complex sector of national rating agencies accredited by the Bank of Russia. The theoretical significance of the study lies in the development and adaptation of the methodology of sectoral analysis to the specifics and needs of the agro-industrial complex for the purpose of its sustainable development. The practical results are of value to the Ministry of Agriculture and private investors interested in an independent assessment of companies in order to minimize the risks of investing in sustainable development projects.

The subject of the study is the carbon border adjustment mechanism (CBAM), one of the European climate regulation tools aimed at curbing the “carbon leakage” that occurs when importing goods from countries with less stringent climate regulation to countries with more stringent regulation. For this reason, the carbon tax affects the interests of exporters of carbon-intensive goods to the EU, especially Russia, Turkey, China, which will suffer the greatest damage. The purpose of the paper is to assess the dynamics of the export of Turkish goods to the EU countries and to determine Turkey’s position on the introduction of a carbon tax. One of the main tasks of the work is to determine the extent to which Turkey supports Russia in the EU’s opposition to the introduction of this tax. The research methodology is based on the use of statistical analysis methods (sampling, comparison, grouping, etc.) and analysis of identified trends. An analysis of the dynamics and structure of trade between the EU and Turkey led to the following results: 1) Turkey is one of the leading countries exporting carbon-intensive products to the EU; 2) The existence of a weak dependence of the EU on carbon-intensive Turkish goods due to the differentiation of its imports and, conversely, a strong dependence of the Turkish economy on the EU due to the significant orientation of Turkish exports to EU markets. It is concluded that Turkey is in a difficult situation in connection with the CBAM. On the one hand, there is a threat of a decrease in the competitiveness of products of the cement, mechanical, and metallurgical industries; on the other hand, national companies are successfully integrated into European production chains, and the strategy of adaptation to the European Green Deal may be preferable both for them and the national economy as a whole. Therefore, there is a possibility that Turkey will take a “pro-European” position. If a “pro-European” position prevails, this will create additional risks for the Russian Federation in the fight against EU carbon taxation.

Over the past few years, many research papers have referred to stock market volatility in relation to investor attention and sentiment and our article adds to the current literature on financial market reactions to the economic consequences of COVID-19. An event such as an outbreak of an infectious disease causes a negative change in investor sentiment, which strongly influences their investment decisions and, consequently, stock market prices. The subject of the study is the mutual influence of stock market characteristics and market sentiment, during a COVID-19 pandemic crisis. The purpose of the study is to provide empirical support for the hypothesis of indirect impact of uncertainty and panic under the COVID-19 pandemic on the dynamics of the stock market in Russia. The World Health Organization and experts forecast that the world will face more than one crisis related to the spread of infectious diseases in the future, so understanding the mechanisms of mutual influence of sentiment and financial markets remains relevant. In this study, we take a novel approach to deriving an indicator for panic that has not been used before. We perform econometric modeling using a Vector Autoregressive Model (VAR) and a Vector Error Correction Model (VECM), which allows us to describe in the model not only the long-term equilibrium but also the dynamics towards it. As a result, we got consistent and efficient estimates of the long-term and short-term effects of panic and mortality rates on the volatility of the RTS stock index and found that the market reaction to COVID-19 changed as the pandemic spread: the effects of uncertainty and panic, while having a significant impact at the beginning of the crisis, faded away. The conclusions obtained in the analysis of the Russian stock market dynamics coincide with those obtained by other authors in their analysis of markets in other countries over a similar period.

In the context of an obvious 32% growth, the relationship between the circular economy, risks and returns is becoming increasingly relevant. ESG indicators are increasingly pivotal in global investment decisions. The purpose of the study is to demonstrate that ESG-mandated companies are more likely to yield sustainable long-term performance, advocating for investors to consider ESG-based mutual fund schemes. The research evaluates the performance of the top 10 high-capitalization and ESG equity funds, comparing them to the Nifty-50 benchmark index using various performance metrics. An increasing trend in ESG-compliant investing is observed, contributing to the circular economy. It was concluded that even post-risk adjustment, ESG funds remain lucrative, offering sound long-term returns. Statistically significant returns are noted in both funds and index. The study recommends companies revise policies towards ESG compliance and investors kindness ESG funds. The novelty of the study is that it gives a new insight into the performance of two different categories of funds, how well circular economy strategies can contain investment risk and provide risk-adjusted returns.

The COVID pandemic, which broke out at the end of 2019, caused many changes in human life. Restrictions on access to entertainment and socializing activities have an impact on all aspects of life, but on the other hand, the business side of online streaming service platforms that show favorite movies and series benefits from this pandemic. The purpose of the study is to analyze the influence of perceived enjoyment and attitude toward money on intentions to subscribe to online streaming platforms. The study was conducted in Makassar, one of the metropolitan cities in Indonesia. The sample in this study was 151 people. Data was collected through a self-administered questionnaire, and the data then analyzed using PLS analytical tools. The results showed that perceived enjoyment and attitude toward money have a significant effect on the subscription intentions of online streaming platforms.

In the context of increasing globalization, income inequality is one severe problem in several countries because it widens the income gap between the rich and the poor, which leads to social instability. Narrowing this gap has become one of the main agendas in many developing countries to satisfy the millennium goals proposed by the United Nations. Meanwhile, government expenditure is one crucial fiscal instrument as it contributes significantly to running the economy and overcoming economic cyclicality. In particular, governance/institutional can positively adjust the public debt — income inequality relationship in developing economies. The purpose of the study to identify the impact of institutional quality, public debt and their interaction on income inequality on a balanced data panel of 34 developing economies for the period 2002–2020. For monitoring endogenous problems and serial autocorrelation in empirical equations, two-step and one-step system GMM (Generalized Method of Moments) assessments are used. The results from the study show that public debt and the quality of institutions increase income inequality, but their interaction narrows it. These results seem to be counter-intuitive. Besides, education enhances income inequality in these economies. The results of the study provide some policy recommendations for reducing the inequalities in society through public debt and the quality of institutions in developing economies. Accordingly, governments in developing economies should use spending financed by public debt to support low-income individuals through social transfers throughout economic development. Importantly, they should spend more on education and health to help the poor improve their skills and knowledge, narrowing the income difference between the rich and the poor. In particular, they should be prudent in controlling and managing public debt to avoid a public debt crisis and social instability.

The Central Bank of Turkey’s policy to decrease the nominal interest rate has caused episodes of severe fluctuations in Turkish lira exchange rates during 2022. According to these conditions, the daily return of the USD/TRY have attracted the risk-taker investors’ attention. Therefore, the uncertainty about the rates has pushed algorithmic traders toward finding the best forecasting model. While there is a growing tendency to employ sophisticated models to forecast financial time series, in most cases, simple models can provide more precise forecasts. To examine that claim, present study has utilized several models to predict daily exchange rates for a short horizon. Interestingly, the simple exponential smoothing model outperformed all other alternatives. Besides, in contrast to the initial inferences, the time series neither had structural break nor exhibited signs of the ARCH and leverage effects. Despite that behavior, there was undeniable evidence of a long-memory trend. That means the series tends to keep a movement, at least for a short period. Finally, the study concluded the simple models provide better forecasts for exchange rates than the complicated approaches.