Beyond Volatility: A Leakage-Safe Residual-Stress Signal for Drawdown Risk Monitoring

Monitoring equity drawdown risk requires real-time indicators that can be implemented without look-ahead bias and that may add information beyond standard volatility measures. This study develops a leakage-safe residual-stress indicator from cross-sectional PCA reconstruction errors in U.S. sector excess returns. Using daily adjusted prices for SPY and 11 U.S. sector ETFs, sector excess returns are computed relative to SPY, the common component is estimated with principal component analysis (PCA), and residual stress is defined as the cross-sectional root-mean-square magnitude of out-of-sample reconstruction residuals. The PCA mapping is estimated using information available only through t−1, the stress score is computed at t, and high-stress regimes are defined using rolling train-only quantile thresholds shifted forward by one trading day. The results show that realized volatility remains the stronger standalone benchmark in overall early-warning classification performance. Residual stress is therefore not proposed as a replacement for volatility. Instead, it is most useful as a complementary indicator of cross-sectional market dislocation. In the baseline sample, residual-stress spikes cluster around several drawdown-onset episodes, and conditional regime analysis shows that when volatility is low, high residual stress is associated with a higher probability of a drawdown onset within the next H=21 trading days than the low-stress/low-volatility regime. Event-overlap and lead-time diagnostics suggest that residual stress can identify some onset episodes not captured by a simple volatility-threshold rule, although its main incremental value lies in conditional risk stratification rather than systematically earlier triggering. The contribution of the paper is to develop a leakage-safe and interpretable residual-stress diagnostic for conditional drawdown-risk monitoring. The evidence supports a balanced interpretation: residual stress adds state-dependent information beyond standard volatility measures, especially in otherwise low-volatility states, but it does not dominate realized volatility as a standalone predictor.