Signal-TTT: Efficient Test-Time Adaptation for Event-Driven Portfolio Construction

Event-driven asset ranking aims to predict market impact and construct long-short portfolios following market-moving information, such as Donald Trump's Truth Social posts. While frozen large language models can score individual (post, instrument) pairs, test-time adaptation (TTA) is critical to adjust for post-training market regimes. However, existing TTA methods fit a single parameter per test input, which in this context represents an entire event cross-section. We prove that any additive or strictly monotonic per-event correction inherently preserves within-event relative orderings, leaving rank metrics and portfolio performances unchanged. To address this limitation, we propose , a closed-form cross-sectional test-time training framework. computes a per-(event, instrument) offset using ridge-regularized least squares over retrieved historical outcomes, automatically calibrating its regularizer via empirical Bayes. Requiring no gradients, learning rates, or access to model weights, our update is provably a shrunk per-instrument mean residual with a explicit error-reduction condition. Evaluated on a development set of nEvTest Truth Social policy events across nBackbones frozen backbones, improves Rank IC by devIcGain and boosts dollar-neutral net returns by devPrimaryMean bps per event, whereas collapsing to a single per-event offset completely eliminates these gains.