ORCA -- Online Regime Correlation Analyzer
Boris Kriuk, Fedor Kriuk

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…
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