Developing a System Dynamics Model for Analyzing Fluctuations in Major Cryptocurrency Markets

Abstract

The cryptocurrency market has attracted unprecedented investor attention in recent years. Bitcoin, the world’s largest digital currency, has experienced a price increase of more than 1,500% since the beginning of 2017. Despite numerous studies on the future of cryptocurrencies and their volatility, few studies have provided a comprehensive analysis of the broader cryptocurrency market and how cryptocurrencies evolve and fluctuate. The aim of the present study is to develop a system dynamics model for analyzing and forecasting cryptocurrency market fluctuations by considering a comprehensive set of influential variables. While focusing on Bitcoin, this study also examines several other common cryptocurrencies, such as Ethereum, which have received less attention in previous studies. In this study, using a system dynamics approach and Vensim DSS software, a comprehensive model was designed to analyze cryptocurrency market fluctuations. First, through a systematic review of the research literature, 28 influential factors were identified in six main categories, including fundamental, demand-related, macroeconomic, psychological-behavioral, technical and on-chain, and institutional-regulatory factors. Then, through the analysis of causal relationships and feedback loops, four reinforcing loops—price self-reinforcement, network effect, investment and mining, and halving effect—and four balancing loops—affordability, profit-taking, mining difficulty, and regulatory interventions—were mapped. To validate the model, historical data from four major cryptocurrencies—Bitcoin, Ethereum, Litecoin, and Ripple—over the period from 2017 to 2024 were used. The mean absolute percentage error (MAPE) was calculated as 10.5%, and the coefficient of determination (R²) was 0.88, indicating the acceptable accuracy of the model. The simulation results of five different scenarios, including halving, institutional adoption, strict regulation, global economic crisis, and a combined scenario, showed that the halving event alone leads to a 42% increase in price, institutional adoption leads to a 69% increase in price and a reduction in volatility to 32%, and the combined scenario leads to a 123% increase in price. Furthermore, dynamic correlation analysis showed that the correlation among cryptocurrencies changes under different market conditions, and stablecoins have a weak negative correlation with other cryptocurrencies, ranging from -0.15 to -0.35. Finally, operational composite indices, including the Market Pressure Index (MPI), Network Sentiment Index (NSI), Regulatory Risk Index (RRI), Fundamental Strength Index (FSI), and Composite Volatility Index (CVI), were proposed for monitoring and forecasting fluctuations. The proposed model can be used as a tool for scenario analysis and more informed decision-making by investors and policymakers.