R3-TTA: Test-Time Adaption From Matured Outcomes for Post-event Market Prediction

Standard Test-Time Adaptation assumes that an unlabelled query carries a useful learning signal. We show this assumption fails in market response prediction, where outcomes have not occurred at prediction time and posts arriving within the same time bar share a single future return, causing standard gradient adapters to severely degrade performance relative to the frozen baseline. We propose Regime-Routed Retrieval Test-Time Adaption (R3TTA), which grounds adaptation in historical events with matured outcomes. For each query bar, R3TTA retrieves past matured bars routed by arrival timing and lagged market state, fits a prior-shrunk Bernoulli response model with a closed-form minimizer, and fuses this local estimate with the frozen prediction in logit space. By design, zero parameters are updated, no signals are extracted from unlabelled test batches, and shared outcomes are counted strictly once. Fusing the local nonparametric estimate with the frozen prediction drives the primary performance gains, far outperforming naive bias-offset adjustments. On post-event market prediction, R3TTA consistently improves performance across diverse architectures and walk-forward evaluations achieving substantial gains on tabular models where all gradient adapters fail. Without retuning, R3TTA seamlessly transfers to broad market assets. We release the aligned post–market panel with a reproducible content tagging layer. validated adaptation for on.