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Factor‐Based Quantile Forecasting With Textual Data

Jie Wei et al.

Journal of Applied Econometrics2026https://doi.org/10.1002/jae.70047article
AJG 3ABDC A*
Weight
0.50

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.

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https://doi.org/https://doi.org/10.1002/jae.70047

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@article{jie2026,
  title        = {{Factor‐Based Quantile Forecasting With Textual Data}},
  author       = {Jie Wei et al.},
  journal      = {Journal of Applied Econometrics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/jae.70047},
}

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Factor‐Based Quantile Forecasting With Textual Data

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.