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Liburkina L. M.

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Economic Factors of Artificial Intelligence Adaptability under Cryptocurrency Market Volatility
Schuchmann V. A.

Schuchmann, Vadim A. (2026) “Economic Factors of Artificial Intelligence Adaptability under Cryptocurrency Market Volatility.” Business Inform 1:490–498.
https://doi.org/10.32983/2222-4459-2026-1-490-498

Section: Finance, Money Circulation and Credit

Article is written in Ukrainian
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UDC 004.8:330.45:336.74

Abstract:
This article explores the economic factors influencing the adaptability of artificial intelligence (AI) systems within the context of cryptocurrency market volatility and substantiates the importance of considering them as part of models’ economic performance and technical quality. It is demonstrated that cryptocurrency market instability, volatility and liquidity regime shifts, informational asymmetry, and microstructural trading constraints create an environment where the effect of adaptation is determined by the balance between expected performance gains and total costs. The methodological foundation of the study combines conceptual analysis with a synthesis of contemporary approaches in financial econometrics, machine learning, and reinforcement learning. A comparative analysis of research approaches concerning data non-stationarity and drift was conducted, along with the harmonization of evaluation criteria systems focused on economic outcomes. Particular attention is paid to the role of transaction costs (commissions, spreads, slippage), information costs (collecting, cleaning, and updating data), the cost of computing resources, risk limits, and institutional constraints in determining the permissible intensity of model updates and decision-making rules. A methodological framework is proposed for classifying the determinants of adaptability according to groups of regime-market conditions, cost parameters, and resource-organizational constraints, as well as a two-loop adaptation logic that combines monitoring changes in market regimes and data drift with an economically motivated decision to update the model, feature set, and rules. The scientific novelty lies in the development of adaptation assessment criteria that link technical decisions with net effect after costs, risk metrics, and stability across different market regimes. Practical conclusions can be applied to the design of adaptive AI systems in cryptoanalytics, algorithmic trading, and risk management, as well as to the standardization of comparisons between alternative adaptation strategies, taking into account actual transaction costs and risk constraints. Future research prospects are related to the empirical validation of the framework on different trading platforms and time horizons, and to the formalization of the thresholds at which adaptation provides added value after costs.

Keywords: digital economy, economic factors, artificial intelligence, cryptocurrency, international market, economic efficiency, digital tools.

Fig.: 1. Tabl.: 2. Bibl.: 16.

Schuchmann Vadim A. – Applicant, Department of Economic Cybernetics and Informatics, West Ukrainian National University (11 Lvivska Str., Ternopil, 46009, Ukraine)
Email: [email protected]

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