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News
October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
October 7, 2026
‘Our Team Consists of True Leaders in Their Respective Academic Disciplines
The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.
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International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.

 

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Uncertainty of Market Graph Identification

Operations Research Forum. 2025. Vol. 6. Article 164.
Koldanov P., Koldanov A. P., Tsygankov V. V.

One of the popular tools for analyzing network models of a market is the threshold similarity graph or market graph, which illustrates the strength and topology of correlations in the stock market. Several papers are devoted to the construction of such a graph in order to analyze correlations in specific stock markets. However, the reliability or uncertainty of the conclusions obtained has not been sufficiently studied. To carry out such studies, we investigate some measures of uncertainty. These measures include classical measures based on risk function and new measures based on recently proposed methods for dividing conclusions about correlations into two sets: a statistically significant set of conclusions and a statistically nonsignificant set of conclusions or zone of uncertainty. The paper shows the relationship of some new measures of uncertainty with classical ones. The proposed measures of uncertainty include the ratio of the size of the nonsignificant set of conclusions to the total number of conclusions and the ratio of the size of the nonsignificant set of conclusions to the size of the significant set of conclusions. These measures are divided into two types. Measures of the first type are defined as functions of the chosen threshold of the market graph. The measures of the second type, or uncertainty indicators, do not depend on the threshold of the threshold similarity graph. Data from 12 stock markets are used to investigate the proposed measures of uncertainty. The correlation is estimated using both the traditional Pearson correlation coefficient and the Kendall correlation coefficient.

Research target: Mathematics Computer Science Economics and Management
Language: English
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Keywords: Pearson correlationKendall correlationRandom variable networkMarket networkstatistically significant conclusionsThreshold similarity graph Market graph
Publication based on the results of:
Network and graph models, algorithmic complexity and data mining (2025)
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