A gate-based AI-driven decision framework for cryptocurrency derivative investment: mathematical modeling and computational implementation for exchange risk management

Purpose The innovative crypto-world represents one of the most prominent manifestations of volatile, uncertain, complex and ambiguous (VUCA) environments. In this context, the use of technology plays a fundamental role in ensuring the survival of firms operating within the business environment. Recently, the cryptocurrency derivatives market reached a monthly trading volume of $1.33 trillion, exceeding traditional spot markets and creating unprecedented challenges for digital asset exchanges in managing investment decisions. This paper presents a novel gate-based artificial intelligence (AI) framework for optimizing cryptocurrency derivative investment strategies in crypto-exchange companies’ operations through the implementation of new technology-based solutions. Design/methodology/approach The paper is supported by the quantitative methodology of system dynamics (SD). This method allows the development of a mathematical model that integrates sentiment analysis with technical momentum indicators through a cascade gate system. The mathematical formulation includes stochastic differential equations for price dynamics, Bayesian inference for sentiment analysis and multi-objective optimization for risk management. Monte Carlo simulations demonstrate the framework’s robustness across different market conditions, with Sharpe ratios consistently above 1.8. Findings The framework returns a complex decision-making output in order to reach an optimal choices pathway for derivative investments. The framework matches leading indicators relative strength index, lagging indicators (moving averages, moving average convergence/divergence [MACD]) and volume indicators on-balance volume with real-time sentiment analysis. Our computational implementation uses historical data from major cryptocurrency exchanges (2020–2024) to validate the theoretical model, achieving risk-adjusted returns of 23.7% annually with maximum drawdown limited to 8.2%. Research limitations/implications The present research is not without limitations. Among others, these include the appropriate software for the effective development of SD; information asymmetry that may arise during sentiment analysis via social networks; and the real-time availability of price and volume data for the optimal consideration of technical indicators. Practical implications From a practical perspective, the work can contribute to improving decision-making and risk management assessment based on integrated approaches that combine business choices with mathematical models and the development of enabling technologies such as AI. Moreover, this research can be useful to develop an updated regulatory framework for cryptocurrencies investments. This research addresses the critical make-or-buy decision facing cryptocurrency exchanges, providing a systematic approach to in-house derivative investment management with built-in risk controls and regulatory compliance features. Social implications From a social perspective, this study contributes to a better knowledge of the behavior of crypto-exchanges in the formulation that guides them in their choice regarding investments in derivatives. This aspect now involves millions of users globally. Originality/value This study contributes to several streams of research, including the decision-making process, risk management assessment and financial instruments. The paper also presents social and practical implications.