When Predictive Ranking Is Not Enough: Warning Governance and Economic Value in Bitcoin Tail-Risk Analytics

This study forecasts unusually large daily Bitcoin price movements and examines whether the resulting warnings improve investment decisions. We compare simple volatility signals, a benchmark based on the previous day’s return magnitude, logistic regression, and random forests in chronological tests covering 2340 days. Large movements include gains and losses whose absolute daily log returns exceed the 95th percentile of the preceding training sample. The evaluation contains 51 such events. The selected volatility signal issues 77 warnings and captures nine events. Better predictive ranking does not consistently produce better warnings. Additional comparisons use model tuning, common nominal warning budgets, and a separate analysis of large losses. Investment evaluation accounts for trading costs, cash returns, execution delays, and investor preferences. Moving fully to cash after the selected loss warnings can reduce investor utility relative to buying and holding Bitcoin. In contrast, adjusting exposure using volatility over the preceding 20 days improves utility at the highest tested risk aversion, despite lowering total return. These historical comparisons are exploratory; uncertainty estimates condition on recorded forecasts and are not adjusted for multiple comparisons. The findings support separate evaluation of predictive ranking, warning governance, and economic value.