This paper explores novel methods for identifying structural changes in the Russian stock market environment using data on queries from the Yandex search engine. Utilizing data on 16 economic and 12 political topics for the period from 2018 to 2025, the author applies methods of statistical analysis, the Granger causality test, and cluster analysis. The events of February 2022 serve as the temporal cutoff point for analyzing structural changes. The results showed a statistically significant increase in interest in the topics "Yuan exchange rate," "Key rate," and "Sanctions." Correlation analysis revealed inverse relationships between queries on currency topics and the return of the IMOEX index. The Granger causality test found causal relationships between queries on the topic of negotiations and market returns, as well as between market returns and queries on the topic "Exchange rates." Furthermore, cluster analysis enabled the identification of three regimes (clusters) of market functioning with different characteristics of returns and search activity. The study demonstrates that search queries are a valuable source of information for analyzing structural changes and can be used as an additional tool for monitoring market sentiment, especially during periods of geopolitical instability.
Rodionov et al. (2026) studied this question.