Abstract: The paper is devoted to approaches and methods of oil and gas companies valuation based on financial statements and industry factors. The focus of the study are russian public vertically-integrated oil and gas companies. The purpose of the study is to identify the key financial statement indicators of the companies, which have the greatest impact on their market value, and the method of assessing the value created by a company considering these indicators. The main methods of a company valuation, including comparative analysis and income approach, are reviewed in the paper and their key advantages and disadvantages are identified. In particular, the special attention was given to indicators, which have the greatest influence on fundamental value of companies. In order to identify this correlation the statistical method and the methods of financial analysis and forecasting were used for the period 2015–2024 in relation to the selected companies. The results of calculation demonstrate high correlation between market capitalization of the selected companies and their assets and equity during this period. These items are also the key components of the invested capital used to calculate the value created by a company via the economic profit indicator (EVA). Despite some advantages of EVA in comparison to alternative methods of valuation, for the most of analyzed companies it deviated significantly from market value change during 2015–2024. Therefore, the scientific novelty of the study is the modified approach for EVA components calculation considering the results of correlation analysis and actual financials. In particular, the special attention was given to the adjustments of fixed assets and debt based on historical cash flows for these items. The results of the study demonstrate the increase in EVA for some of selected companies resulting in potential feasibility of the introduced adjustments considering the limited financial statements disclosure.

Keywords: valuation, oil and gas companies, economic profit, invested capital, market value, income approach, capital expenditures, fixed assets, debt obligations, financial statements

Abstract: This article presents an empirical study of the potential of socially oriented non-profit organizations (SO-NPOs) in St. Petersburg for the period 2020–2025. Based on an analysis of official data from the Register of SO-NPOs and the results of monitoring 150 non-profit organizations, structural transformations in the sector are identified: a reduction in the number of organizations with a simultaneous increase in institutional maturity and diversification of funding sources. Previous studies conducted by the Committee on Social Policy of St. Petersburg (2019–2022) provided an understanding of the dynamics of the development of the non-profit sector only in the context of individual support measures from regional executive bodies, without taking into account the resource potential of the non-profit sector and the opportunities opened up by Russian legislation and financial and economic support measures for this sector. A systemic-resource approach is applied to assessing potential across five dimensions: financial stability, social productivity, resource security, information transparency, and accounting policy potential. It was established that during the analyzed period there was a transition from the dominance of the grant financing model to the formation of hybrid models combining state support with the attraction of extra-budgetary resources. Positive dynamics were revealed in terms of information activity and accounting discipline, while problems of financial stability and staffing remained in a significant number of organizations. Based on the conducted study, a number of conclusions were formulated: (1) The NPO sector of St. Petersburg underwent a consolidation stage in 2020-21 with a subsequent increase in institutional maturity; (2) A qualitative transformation of the financing structure took place: a transition from the dominance of the grant model to the formation of a polycentric structure with a dramatic increase in sponsorship support and the development of hybrid financing practices among effective organizations; (3) The potential of NPOs has significant potential to contribute to the achievement of the strategic goals of the socio-economic development of St. Petersburg until 2035, but requires targeted support from the state to overcome structural constraints.

Keywords: socially oriented non-profit organizations, potential, regional economy, St. Petersburg, performance assessment, government support, assessment methodology, objective assessment, regional government system, system of evaluation criteria

Abstract: The object of this work is investment in financial derivatives. The subject of this article is empirical strategies for evaluating options and methods of hedging risks in investments in options. The methodological basis of the study was the fundamental concepts of risk assessment using option sensitivity parameters (Delta, Gamma, Theta, Vega) and Black-Scholes option pricing model. Empirical methods of research included experimental mathematical modeling in the Python software environment of option price strategies. Predicting the prices of basic assets and assessing the traits that influence volatility was done using machine learning algorithms Random Forest and Extreme Gradient Boosting (XGBoost). The scientific novelty of the research is in explaining the approach, which integrates volatility metrics and probabilistic stock price forecasts obtained using machine learning technologies into the option strategy selection process. The assessment of the importance of the characteristics that influence price volatility in basic option assets is particularly practical for making sound investment decisions. The decomposition of factors determining price volatility provides additional analytical arguments for choosing an investment strategy. The main result of this article demonstrated that in case of a volatile market for basic assets, it is necessary to use ML models to make investment decisions, which give a probabilistic idea about the direction of price movement and factors influencing the volatility of prices of basic assets. Thus, in this article the strategies of evaluation of options are analyzed on the example of an option with a basic asset – a stock; calculations were made for strategies Long Straddle, Long Strangle, Calendar Spread. It is shown that the application of ML models provides an empirically grounded insight into the significance of the characteristics determining the volatility of the price of a basic asset, which simplifies the choice of investment strategy.

Keywords: derivatives, options, hedging, investment strategy, option greeks, investments, volatility forecasting, option pricing, machine learning models, Black-Scholes Model

Abstract: In this study, the author aims to answer the question of whether narrative gaps in the central bank's short-term communications can influence the magnitude of errors made by professional analysts regarding key economic variables and the key interest rate. To achieve this, tasks such as assessing the dynamics of forecasting errors by professional analysts; calculating the magnitude of narrative gaps in the central bank's short-term communications; analyzing the presence of statistically significant correlations between the average magnitude of forecasting errors by professional analysts and narrative gaps, as well as developing recommendations for improving short-term communication for central banks, are addressed. The work is focused on taking into account the peculiarities of narrative gaps in the short-term communication of the central bank when establishing policies for predictability and clarity of decisions in the area of monetary policy. For this purpose, methods of metric analysis of text arrays based on central bank press releases and materials from consulting firms, independent research institutes, and investment funds were used, as well as methods for measuring professional analysts' errors, measuring the readability of published materials, and statistical methods for establishing significant relationships between variables. From January 2021 to December 2025, narrative gaps in the short-term communication of the Central Bank of Chile were identified, and a statistically significant relationship was determined between narrative gaps according to specific metrics and the magnitudes of analysts' errors in forecasting key economic variables. Furthermore, the study notes that the reduction in the frequency of Central Bank of Chile press releases, following a decrease in the number of board meetings within the calendar year, did not have a significant impact on the average magnitude of professional analysts' errors regarding the key interest rate. This may be explained by a noticeable increase in the volume of press releases published on the official website. It was also found that the largest average error in analysts' forecasts occurs under conditions of active tightening or loosening of monetary policy while maintaining a consistent communication policy.

Keywords: monetary policy, communications, text analysis, uncertainty, professional analysts, narratives, central bank, predictability, transparency, forecasting

Abstract: The article examines the Russian support model for small technology companies (STCs) and the extent to which existing support measures correspond to the actual profile of firms included in the STC register. Particular attention is paid to the mechanism used to identify the target group of innovation policy. The Russian model is considered in comparison with foreign approaches to supporting innovative small firms. The study proposes a typology of national support systems based on identification mechanisms and distinguishes status-based, program-based, and hybrid models. It is shown that these models differ not only in institutional design, but also in the possibilities for evaluating policy effectiveness. The empirical part of the study focuses on the cohort of Russian STCs classified as “startups with minimal revenue.” The methodological framework combines comparative institutional analysis with matching the STC cohort to a control group of statistical twins using administrative data from the Federal Tax Service, the STC register, and public procurement databases. The results show that companies in the examined STC cohort are more likely to exhibit expenditures exceeding revenues, a higher share of active organizations, and lower participation in public procurement compared with the matched control group. Robustness checks confirm the stability of these differences across age cohorts and tax regimes. The analysis of support measures demonstrates that many instruments either do not provide direct material resources or are oriented toward firms with higher revenues and more stable profitability. The study concludes that there may be a structural mismatch between the identification mechanism for STCs and the current configuration of support measures.

Keywords: small technology companies, SMEs, innovation policy, institutional design, innovative enterprises, innovative SMEs, identification mechanism, targeted support, matched-pairs method, empirical evaluation

Abstract: The subject of this study is the professional judgments of a specialist regarding the methods of recognizing and evaluating the facts of economic activity and calculating the values of profit and loss statement items when preparing financial statements in accordance with the requirements of International Financial Reporting Standards. As part of the work, several tasks were set: to define the concept of high-quality professional judgment, to establish its characteristics; to deduce groups of reporting users depending on access to information about the company; Based on the selected groups, create categories of indicators that use data about the company available to different users to calculate; provide and justify an acceptable interval for each of the indicators; combine these indicators into a weighted model; test the model in terms of the category of public reporting indicators by assessing the fairness of the distribution of the evaluation of relative risk of data manipulation. Methods of logical and quantitative analysis were used in the process of forming financial indicators. Modeling was used in the formation of the judgment quality assessment system. The model was tested using the method of experimentation. The elements of scientific novelty in the work are the definition of high-quality professional judgment, the distinguishing features of competent and incompetent use of judgment, and a model for evaluating the quality of judgments based on publicly available information.It is proved that the use of the developed model provides a fair assessment of the relative risk of manipulation of the reporting data. It has been revealed that the risk of manipulation in certain industries is much higher than in others, which may be due to the peculiarities of applying accounting rules to industry specifics. The tested model can be used in the process of analyzing financial statements as one of the factors of reliability of the disclosed information.Due to the revealed dependence of the risk of manipulating reports and the amount of judgment used, the conceptual question is raised about the expediency of its application in such a significant volume. Moreover, the question is raised about the need for external control over the quality of judgments made through the formation of a supervisory authority.

Keywords: Accountant's professional judgment, Accounting, Revenue, Risk of manipulation, Reliability of financial statements, Professional skepticism, IFRS reporting, Financial analysis, Risk assessment, Financial ratios

Abstract: The article examines the socio-economic factors that determine the transformation of marketing communications for small and medium-sized B2B enterprises in the Russian Federation as of April 2026. The purpose of the study is to identify and systematize the socio-economic factors that influence the marketing communications of B2B SMEs in the Russian Federation. The research methodology includes a systematic analysis of legal acts, statistical processing of survey data, content analysis of industry publications, and the theory of generations adapted for marketing communications. The study uses the method of modeling using mind maps to visualize the obtained data. The study identifies the following economic factors of influence: macroeconomic context, staff shortages, economic uncertainty, sanctions and import substitution, digitalization. The social factors are professional communities, social capital, human-centricity, career mobility, ESG, new language of communication, advertising fatigue, and generational shifts. As a result of the research, three interconnected mind maps were developed: a mind map of the economic factors that influence the marketing communications of B2B SMEs, a mind map of the social factors that influence the marketing communications of B2B SMEs, and an integrated mind map of the factors that influence the marketing communications of B2B SMEs. The mind maps include factors, second-level factors, and their components. The study also develops a three-level model of the factors that influence the marketing communications of B2B SMEs, which includes the mega-level (technological and generational factors), the macro-level (social, economic, and regulatory factors), and the meso-level (production & technical, and typological factors). The resulting model reveals the cascading influence of the factors on the marketing communications of small and medium-sized B2B enterprises.

Keywords: marketing communications, B2B, SMEs, generational theory, generational shift, digital technologies, mind maps, socio-econimic factors, regulatory changes, technological re-equipment