Factor‐Based Quantile Forecasting With Textual Data
提出一种嵌入注意力机制的分位数因子模型，利用文本数据（如双词和三词组合）提升汇率收益率和工业产出增长的分位数预测，并通过投资组合应用验证其经济价值。
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.