ORCA -- Online Regime Correlation Analyzer

2026-04-19

AI Summary

This study addresses the limitations of traditional risk models that overlook the topological structure of financial asset correlation networks and rely solely on scalar volatility, thereby failing to accurately forecast extreme market movements. To overcome this, the authors introduce spectral graph theory and random matrix theory into market state prediction for the first time. They construct dynamic feature vectors by extracting spectral properties from multi-scale rolling correlation matrices alongside price-based indicators, and employ a weighted deep random forest to enable end-to-end probabilistically calibrated forecasting. The proposed method substantially enhances the detection of sharp market crashes—improving AUC by 10.3 percentage points to 0.741—and yields a backtested strategy with an annualized return of 15.6%, a Sharpe ratio of 1.13, and a maximum drawdown of only −7.5%, significantly outperforming a buy-and-hold benchmark.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Social networks and social learning
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• correlation networks
• market regime detection
• financial risk modeling
• crash prediction
• spectral graph theory

Innovation

• spectral graph theory
• random matrix theory
• correlation network
• crisis prediction
• walk-forward backtesting