Purpose – quantitative analysis and identification of determinants of Russian universities’ global competitiveness.Methods. Parametric and non-parametric methods for data analysis and machine learning.Results. The authors figured out the hidden determinants of international competitiveness of Russian universities and the national educational system of the Russian Federation. We proposed four working hypotheses. (1) The positions of Russian universities in the international QS ranking are different for metropolitan and regional universities. (2) The positions of Russian universities in the international QS ranking depend on the status of universities. (3) The positions of Russian universities in the international QS ranking differ depending on the participation of universities in the Russian Academic Excellence Project “5-100”. (4) The positions of Russian universities in the international QS ranking are different depending on the level of annual university income. We obtained dataset for the study from the analytical materials of the British consulting company QS, as well as information and analytical materials from the results of monitoring the effectiveness of educational institutions of higher education in the Russian Federation.Conclusions. In the empirical part, we have established the significance of the university geographical location influence on its international competitiveness. We have found a high competitiveness level of Moscow and St. Petersburg universities. The international market for educational services does not give preference to universities with the status of a «national research» or «federal». Increased state funding, burdened with approved roadmaps of the Project Office of the Program 5-100, contributes to competitiveness only in the regional educational markets, which is still not enough to strengthen the global international competitiveness of Russian universities and the national educational system of the Russian Federation. Although the level of universities annual income was a significant factor in international competitiveness, university management efficiency played an important role, not only increased funding. Universities with greater institutional management freedom will have greater international competitiveness.

Purpose: Development of the apparatus of the stochastic processes econometric modeling. Discussion: The authors identify risk component in the dynamics of stochastic processesin the economy. Theoretical justification of the alternative and proportional expectations is usedto make probabilistic nature of the risk. Results: The authors suggest stochastic process decomposition based on econometric approach to allocate a probability space of risks, and to identify shocks realizations that lie beyond the boundary of this space. Proportional expectations hypothesis distinguished two types of the event influence on the stochastic process realization: continuous (risk) and discrete (shock). The authors suggest model errors and residualsas the main source of information for the identification of the probability space of risks. The technique of econometric modeling of the price and return processes on stock market under theconditions of the proposed hypotheses is considered in the empirical part of the study. F-testresults have not disproved the statement that the model residuals provide additional information about the simulated rate in the case of lack of relevant factors.

Testing an adaptive modification of the portfolio analysis model with a two-level mechanism of return generation is used to identify the temporal structure of efficient frontiers. The stock market is volatile and, although it is consolidated, it follows different trends at different times. Therefore, stock market processes are viewed as multitrend in nature. It particularly applies to the process of generating returns. For the convenience of the analysis, a multitrend process can be presented as a finite decomposition using adaptation principles. As we have already said, adaptive mechanisms are an important factor for the effectiveness of the stock market. Considering these requirements, the most suitable method is adaptive trend decomposition. In our study, we used data analysis and machine learning methods. The article presents a method of portfolio analysis based on the decomposition of efficient sets into temporal components. This allows for a comparative analysis of portfolio sets regarding their efficiency over different time intervals and enables a dynamic analysis of the temporal structure of efficient sets in order to determine the optimal time for holding the portfolio or changing its structure. A family of efficient sets provides a better understanding of investment opportunities. Our calculations also demonstrated that the temporal structure of a family of efficient sets is more likely to remain robust during the prediction period.

The key to understanding the dynamics of stock markets, particularly the mechanisms of their changes, is in the concept of the market regime. It is regarded as a regular transition from one state to another. Although the market agenda is never the same, its functioning regime allows us to reveal the logic of its development. The article employs the concept of financial turbulence to identify hidden market regimes. These are revealed through the ratio of the components, which describe single changes of correlated risks and volatility. The combinations of typical and atypical variates of correlational and magnitude components of financial turbulence allowed four hidden regimes to be revealed. These were arranged by the degree of financial turbulence, conceptually analyzed and assessed from the perspective of their duration. The empirical data demonstrated ETF day trading profits for S&P 500 sectors, covering the period of January 1998–August 2020, as well as day trade profits of the Russian blue chips within the period of October 2006–February 2021. The results show a significant difference in regard to the market performance and volatility, which depend on hidden regimes. Both sample data groups demonstrated similar contemporaneous and lagged effects, which allows the prediction of volatility jumps in the periods following atypical correlations.

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