This paper examines the survival determinants of small and medium-sized enterprises (SMEs) in Russia, drawing on data covering 5.22 million legal entities that combine the Federal Tax Service’s SME register, the Russian Financial Statements Database (RFSD), and the Unified State Register of Legal Entities (EGRUL) records on ownership and governance. We distinguish four modes of discontinuation: reorganization, probable bankruptcy, removal from the register, and other forms of liquidation. Cox proportional hazards models yield two principal findings. First, the apparent survival advantage of small and mediumsized firms relative to micro-enterprises vanishes — and indeed reverses — once financial performance and ownership structure are controlled for, indicating that what is commonly attributed to size in fact reflects specific organizational characteristics. Second, the four exit scenarios are driven by substantively different factors: bankruptcy is primarily associated with financial performance (ROA), whereas reorganization is linked to affiliation with a corporate group. The effect of the limited liability company (OOO) legal form likewise changes sign once corporate governance variables are included in the specification.

The article examines the structure of consumer demand among Russian households using a two-stage Almost Ideal Demand System (AIDS) model. The model assumes that households allocate their consumption expenditures in two stages. First, they distribute total spending across three broad product groups — food, non-food goods, and services — and then allocate expenditures across more detailed categories within each group. The study estimates elasticities for all aggregate and disaggregated product groups. Overall, the results are consistent with theoretical expectations. Tourism, alcohol, automobiles, and eating out are classified as “luxury goods”, whereas most food and non-food items, as well as basic services, fall into the category of “necessities”. The analysis also identifies products with anomalously low income elasticities, indicating their essential nature and the weak dependence of demand for these goods on households’ current level of well-being. The article shows that, given this structure of consumption, the model parameters are stable, while in the consumption of non-food goods and services the income effect dominates. In addition, for each food product, the study examines the effect of a price increase on the prices of substitute goods, decomposing this response into income and substitution effects. Three types of reactions are identified: local substitution amid an overall contraction of expenditures, weak adaptation dominated by the income effect, and more complex interrelations involving elements of substitution. Finally, the article develops a scenario-based allocation of additional consumer expenditures that takes into account the macro-level objectives of public policy.

The article presents the results of a study examining the relationship between time and monetary expenditures among Russian households across different spheres of consumption. Drawing on data from a nationwide population survey combining questionnaire-based information with time-use diaries, the study demonstrates that, depending on the specific sphere of consumption, time and money may function either as complements or as substitutes. An example of complementarity is investment in children, where adult household members predominantly report both financial expenditures and time spent with children. By contrast, charitable activities represent a case of substitution, as the share of respondents reporting monetary contributions substantially exceeds the share indicating time expenditures. At the same time, variations in the relationship between time and money are determined not only by the sphere of expenditure itself, but also by the material well-being of households. More affluent Russians possess broader opportunities to substitute monetary expenditures for time expenditures, however, they do not resort to such substitution uniformly across all domains. The analysis of the scale of time and monetary expenditures suggests that the population is differentiated not only by material status. An equally important factor shaping these differences is the stage of the individual and family life cycle, which influences not only the principal directions of expenditure, but also the extent to which time and money function as complementary or substitutive resources.

Combining leisure with other activities reflects “overlapping consumption”, which must be taken into account for a reliable assessment of the population’s socioeconomic behavior. The paper analyzes the patterns of overlapping leisure consumption in contemporary Russian society, using the data from the HSE academic research project “Economic Behavior of Households”. The authors estimate the prevalence and the volume of overlapping leisure consumption for specific types of everyday activities, identify the sociodemographic characteristics of such consumption, and describe the relationship between the volume of overlapping leisure consumption and subjective time deprivation. The empirical analysis shows that the share of overlapping time within leisure varies considerably across activities. For the Internet, television, and reading, this share accounts for about one-third of the total time, whereas for radio and music it represents the vast majority.

The article presents the methodology and results of the assessment of the impact of climate risks on the loss ratio of Russian insurance companies on the 2030 horizon based on the analysis and mathematical modeling of the expert opinions and statistical data published by scientific institutes of the Ministry of Emergency Situations of Russia. It is expected that in the worst-case scenario, loss ratio of insurance operations for basic types of insurance may increase by 18.15 p. p. only due to the influence of climate risks. The diversity of the expected increase of the loss ratio is not the same for different classes of insurance: in agricultural insurance it may increase by 44.6 p. p.; in motor casco insurance — by 23.3 p. p.; in householders insurance — by 20.8 p. p.; in life insurance, accident insurance and voluntary medical insurance — from 11.0 to 19.4 p. p.

The article examines the institutional adaptation of Russian exchange institutions under limit regimes — war and a sanctions-induced infrastructural rupture — when the policy priority shifts from competitive price discovery to preserving settlement continuity, collateral valuation, and the controllability of intermediaries’ balance sheets. The main comparison covers 1913—1919 and 2021—2026, while the 1998 Russian crisis is used as a control episode to test the limits of the “financial fortress” concept. The paper argues that the 1918 “grey market” was not an analogue of today’s over-the-counter segment but a symptom of the collapse of public price formation after the dismantling of private property rights and the repudiation of the previous debt order. By contrast, the post-2022 trajectory unfolded under the preservation of private property, corporate legal continuity, and the rights of domestic investors. Consequently, a “financial fortress” is defined not merely as technological segregation and a sovereign-debt anchor, but as a coordinated regime combining legal continuity of ownership rights, macroeconomic discipline, national settlement infrastructure, segmentation of the external perimeter, and a domestic investor base.

This paper analyzes the informational value and predictive power of retail investor sentiment, formed by news flows and social media publications, for the dynamics of the Russian stock market. The aim of the study is to assess the informational value and predictive power of sentiment indicators, calculated using the FinBERT model, for key stock market indicators: stock returns, trading volume, volatility, and the MOEX Russia Index. The empirical dataset includes data on 925 companies listed on the Moscow Exchange from 2015 to 2024, as well as messages from popular financial Telegram channels. Textual sentiment analysis and user visual reactions (emojis) were employed to measure sentiment, from which proxy variables were constructed. Hypothesis testing was conducted using gradient boosting models and vector autoregression (VAR), controlling for macroeconomic and financial variables. The results indicate that investor sentiment is significantly associated with market dynamics, with the strongest effect on trading volumes and the returns of low-capitalization companies. We find that negative sentiment has a more pronounced effect than positive sentiment. The proposed machine learning models, which incorporate sentiment proxy variables, outperformed classical econometric approaches in forecasting accuracy. Model interpretation using Shapley values helped identify the key sentiment factors. Event study analysis revealed the dual nature of sentiment: despite systematically improving the quality of shortterm forecasts (predictive value), there is no significant post-event market reaction to sentiment shocks, and in cases with borderline effects, a pronounced pre-trend in market indicators is observed. This indicates that news sentiment acts primarily as a reactive indicator, promptly reflecting processes already underway, rather than an independent causal factor in market dynamics. These findings underscore the importance of accounting for behavioral factors in investment analysis and can enhance the accuracy of predictive models.