Graph-Based Uncertainty-Aware Financial Forecasting via Cross-Asset Conformal Prediction
Reliable financial forecasting requires not only accurate point predictions but calibrated uncertainty that remains valid under market stress. Conformal prediction offers distribution-free, finite-sample coverage guarantees and has recently been applied to risk-adjusted financial models, yet existing approaches calibrate each asset in isolation. Per-asset calibration is statistically inefficient, degrades sharply when historical data are scarce, and yields poor conditional coverage precisely when it matters most, that is, during volatile and highly correlated market regimes. We propose Cross-Asset Graph Conformal Prediction (CA-GCP), a framework that pools volatility-normalized nonconformity scores across the correlation-graph neighborhood of each target asset using a proximity- and recency-weighted quantile, grounded in the theory of weighted conformal prediction. A lightweight systemic-stress modulator further widens intervals on days of market-wide turbulence. On five years of daily returns for 100 S&P 500 constituents, CA-GCP reduces the cross-sectional standard deviation of per-asset coverage from 1.55% to 0.96% and improves worst-decile coverage from 91.4% to 94.1% relative to a faithful re-implementation of a state-of-the-art per-asset volatility-adaptive conformal baseline, while achieving 95.2% coverage on extreme-volatility days versus 90.4%. Under severe calibration scarcity, with as few as 20 samples per asset, CA-GCP maintains a coverage standard deviation below 0.9%, four to five times more stable than per-asset methods. The gains are robust across graph topologies and forecasting backbones, indicating that cross-asset pooling, rather than any particular graph, is the source of improvement. CA-GCP is model-agnostic, adds negligible computational overhead, and comes with finite-sample validity bounds.