We identify, quantify, and characterize an overlooked hazard for digital financial infrastructure: so- lar storms. Because Bitcoin transaction processing, validation, and mining depend on stable electricity, internet connectivity, and satellite-supported timing systems, geomagnetic disturbances constitute an ex- ogenous operational risk to cryptocurrency markets. To measure it, we develop a step-by-step Artificial Neural Network (ANN) causality test that captures nonlinear, horizon-specific transmission from geo- magnetic intensity to market activity, and we formally establish its large-sample properties. Using nearly a decade of daily data, we document statistically significant and economically material effects. They are robust to alternative activation functions and to bootstrap inference, and a linear multi-horizon bench- mark consistently fails to detect them. Scenario-based stress simulations translate storm intensities into monetary consequences: in 2015 alone, geomagnetic storms were associated with a reduction in Bitcoin trading activity of about 3 million BTC, worth approximately $0.82 billion at 2015 prices. These findings establish space weather as a decision-relevant risk factor for digital finance, and the accompanying anal- ysis translates the estimates into concrete guidance for financial institutions, insurers, and regulators, from early-warning triggers and infrastructure hardening to space-weather stress testing. Because the exposed infrastructure, power, connectivity, and timing systems also imply payment, settlement, and custody services, the analysis carries broader societal implications, including for the resilience of safety- critical services that share this infrastructure, and for the public-health and environmental dimensions of infrastructure-dependent digital activity.

Keywords: Operational Risk, Decision Analysis, Finance, Bitcoin, Artificial Neural Networks, Solar Storms.

JEL Classification: C22, C50, C51, C58, D8, G17, G23, G32.