EventAlign: Auditing Input Exposure in Sparse-Record Forecasting

Financial forecasts can be made daily, whereas earnings-call transcripts arrive only occasionally and may be reused for weeks. However, evaluating models under a single reuse rule makes it difficult to separate how well a learner uses text from how that text is assigned across prediction samples. Our key observation is that transcript assignment changes the information supplied to the predictor, making it part of the evaluation rather than a neutral preprocessing step. To isolate this effect, we introduce EventAlign, which varies transcript assignment while keeping prediction samples, labels, numerical histories, and training and model-selection procedures fixed within each exposure comparison. On our financial case study, whose primary close-to-close target includes the initial overnight and same-day intraday movements on first-eligible earnings-call rows, one-day use yields higher mean AP than 30-day reuse across all ten learners. With matched numerical histories and company information, the GRU–logistic-regression gap is 1.08 AP points under one-day use but 3.76 under prolonged reuse; fresh/stale channels change this interaction, while a separate background study shows that stronger company information improves the no-content baseline more than the text model. The main learner-gap interaction is much smaller and uncertain on subsequent return windows. Together, these results show that measured text gains and model advantages depend on transcript assignment, what the predictor already knows, and the target window.