Analytical analysis of cryptocurrency regulation and adoption: A machine learning-driven ablation study of the United States, Russia, and Indonesia

Abstract

We propose a novel machine learning framework to quantify the effects of regulatory policies on GDP normalized Bitcoin trading volume in the United States, Russia, and Indonesia. Our panel dataset integrates Bitcoin price series, country level adoption rates (2021–2024), macroeconomic indicators, and granular policy variables. An XGBoost regression model predicts future trading volume, and SHAP values to further elucidate feature importance and interactions. Using this calibrated model, we conduct policy ablation simulations by selectively removing regulatory instruments asset classification, licensing, taxation, AML/KYC stringency, and payment bans. Results indicate jurisdiction specific sensitivities: removing AML enforcement in the United States increases volume by +71.45%, while eliminating taxation in Indonesia reduces volume by -46.90%. Comprehensive removal of all regulations yields mixed outcomes: a +26.38% increase in the United States, versus -6.27% in Russia and -47.55% in Indonesia. These findings offer quantitative insights into the trade offs faced by policymakers when designing cryptocurrency regulation.