The article is devoted to the study of the system analysis of the predictive ability of the tonality of information flows in the cryptocurrency market. A method of system analysis and combinatorial optimization of taxonomic filters for processing news information is proposed to maximize the effectiveness of the tonality coefficient in predicting the dynamics of cryptocurrency prices, taking into account time lags. A weighted tonality coefficient with a logarithmic multiplier of the information flow volume has been developed, accounting for sentiment polarity, event importance level, and news flow intensity. The paradox of the impact level has been experimentally established, in which low-visibility information demonstrates increased predictive ability compared to official high-impact messages due to the effects of information asymmetry and preliminary integration of critical events into prices by institutional participants. Systematic combinatorial optimization of 39 combinations of taxonomic filters for 10 cryptocurrencies revealed the lack of a universal approach to filtering and identified four different patterns of asset response to the information background. For Bitcoin, a correlation of 0.3611 was achieved with a leading lag of +3 days when using a low-visibility information filter, which provides a significant 32 % improvement over the basic method (correlation of 0.2737, lagging lag of −6 days). The method was validated on a corpus of 108637 classified information units for the period June-September 2025 using language models for multi-taxonomic classification.
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Musin Ilyas Rasulevich
Saint Petersburg State Electrotechnical University "LETI" named after V.I. Ulyanov (Lenin)
Saint Petersburg, Russian Federation
