# ORCA -- Online Regime Correlation Analyzer

arXiv: https://arxiv.org/abs/2604.17251 · HTML: https://mlsanity.com/paper/2604.17251/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2026.

## Built on by (most cited first, 2 of 2)

- [Artificial Adaptive Intelligence: The Missing Stage Between Narrow and General Intelligence](https://mlsanity.com/paper/2605.16844/) (2026)
- [The Cost of a Physics Prior Is Bounded by the Ablation Gap](https://mlsanity.com/paper/2608.21059/) (2026)

## Abstract (arXiv)

Standard risk models reduce the rich dependence structure of financial markets to scalar volatility estimates, discarding the topological information encoded in cross-asset correlation networks. We present ORCA (Online Regime Correlation Analyzer), an end-to-end framework that fuses spectral graph theory, random matrix theory, and supervised machine learning to deliver calibrated probability estimates for both rally and crash events over a ten-day forward horizon. ORCA constructs rolling correlation matrices from 24 diversified exchange-traded instruments using three parallel estimators at different time scales, and extracts 127 spectral features (absorption ratios, eigenvalue entropy, effective rank, spectral gap, eigenvector concentration, and graph-topological descriptors at multiple correlation thresholds), concatenated with 79 traditional price-derived indicators to form a 206-dimensional feature vector. A depth-limited Random Forest with balanced sub-sample weighting is evaluated under a strict eight-fold walk-forward protocol with ten-day anti-leakage gaps spanning fifteen years of daily US market data. ORCA achieves a Balanced Crisis Detection AUC (BCD-AUC, the geometric mean of rally and crash AUC) of 0.741, ranking first against all baselines. Ablation studies show that spectral features contribute +10.3 percentage points of AUC for crash detection and +5.2 for rally detection over traditional features alone, with SHAP analysis revealing that graph-topological descriptors (clustering coefficient, edge density, and dominant-eigenvalue percentile rank) are the three most important crash predictors. A backtested walk-forward strategy mapping the joint rally-crash signal to dynamic equity exposure with risk-on/risk-off rotation achieves a Sharpe ratio of 1.13, a CAGR of 15.6%, and a maximum drawdown of only -7.5%, versus 3.7% CAGR and -33.7% drawdown for buy-and-hold.