Option-Implied Signals and Crash Risk: Predictability and Machine-Learning Evidence from U.S. Equity Options

We re-estimate canonical option-implied predictability evidence using a unified 2015--2026 panel of 12.36 million U.S. equity firm-day observations across 10,026 underlyings. We split the sample into three regimes: late-post-crisis low volatility (2015--2019), high-volatility transition (2020--2022), and AI/mega-cap concentration (2023--2026). The Xing et al. (2010) smirk--return relationship weakens steadily: the next-month univariate smirk coefficient falls from $-0.023$ $(t=-5.5)$ in 2015--2019 to an insignificant $-0.006$ $(t=-1.5)$ in 2023--2026, and turns positive at the three-month horizon $(+0.016,\ t=+2.1)$. In joint specifications with all six canonical signals, the smirk is insignificant throughout and again changes sign in the latest regime. By contrast, the Cremers--Weinbaum IV spread and a Bakshi et al. (2003)-style risk-neutral skewness measure remain significant across regimes and specifications. A boosted-tree benchmark using IV-surface, trading-activity, Greeks, and liquidity features outperforms linear models for next-month return prediction only in the AI/mega-cap regime, with $R^2_{\mathrm{OOS}}=+1.29%$ versus $+0.07%$. Permutation importance identifies different leading predictors by regime, and no canonical hand-engineered signal enters the top five. For firm-level five-day crash classification, AUC is highest in calm markets $(0.706,\ \text{XGBoost})$ and lowest during the high-volatility transition $(0.561)$. Overall, option-implied predictability is real but regime-dependent, and the post-2023 AI/mega-cap period differs sharply from the pre-COVID setting in which the canonical results were established.