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Factor-Based Quantile Forecasting With Textual Data
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AI
DOI：10.1002/jae.70047.png)
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Abstract
En 中文
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.
Keywords:
density forecast
factor model
portfolio analysis
quantile regression
textual data
Journal
J
IF:
3.1
Papers:
48
Citations:
8.0K
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