Factor‐Based Quantile Forecasting With Textual Data

ABSTRACT Words matter for predicting tail risks. We propose an attention mechanism embedded in a quantile factor model, yielding information that is quantile‐specific, target‐specific, and horizon‐specific. We establish new asymptotic results and show empirically that targeted textual data improve quantile forecasts of exchange‐rate returns and industrial production growth relative to strong benchmarks and other conditional‐quantile models. Bigrams and trigrams drive these gains, extending evidence that collocations enhance forecasting. A portfolio‐management application yields better allocations and higher risk‐adjusted performance. Robustness checks include a synthetic‐misinformation stress test, which shows that fabricated news does not create spurious predictability. Results are consistent across targets, horizons, and both rolling and recursive schemes.