ORCA -- Online Regime Correlation Analyzer

TL;DR

ORCA is an advanced framework combining spectral graph theory, random matrix theory, and machine learning to predict market rallies and crashes using correlation networks, outperforming traditional models over fifteen years of US market data.

Contribution

It introduces a novel spectral feature-based approach for regime detection in financial markets, integrating multiple estimators and machine learning for improved prediction accuracy.

Findings

ORCA achieves a BCD-AUC of 0.741, ranking first among baselines.

Spectral features improve crash detection AUC by 10.3 percentage points.

Graph-topological descriptors are key predictors for crash events.

Abstract

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.