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Unveiling news impact on exchange rates: a hybrid model using NLP and LDA techniques

Teona Shugliashvili & Erekle Pirveli

Applied Economic Analysis2025https://doi.org/10.1108/aea-04-2024-0183article
AJG 1ABDC B
Weight
0.37

Abstract

Purpose This study aims to investigate the influence of U.S. dollar-related news on EUR/US$ exchange rate using a novel hybrid news-fundamentals-based VAR model applied to 18 years of monthly data. Design/methodology/approach Leveraging Latent Dirichlet Allocation (LDA), the authors identify the top 5 U.S. dollar-related news topics, quantify the attention they receive over time using Shannon’s entropy, and integrate these news-generated metrics with news-constructed economic uncertainty indices and Taylor rule fundamentals into the VAR model. Through impulse-response analysis and forecast error decomposition, the authors examine how exchange rates react to shocks from the identified US$-related news topics and economic uncertainty captured by the news. Findings The findings reveal that news related to the US dollar and economic uncertainty account for 29% of long-term EUR/US$ variation. These results are robust, validated through robustness checks, Granger causality tests, sensitivity analysis and applying the same model to the GBP/USD exchange rate. Originality/value Combining news attention metrics with macroeconomic fundamentals enhances exchange rate identification, outperforming the models that rely solely on the Taylor rule or news variables.

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https://doi.org/https://doi.org/10.1108/aea-04-2024-0183

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@article{teona2025,
  title        = {{Unveiling news impact on exchange rates: a hybrid model using NLP and LDA techniques}},
  author       = {Teona Shugliashvili & Erekle Pirveli},
  journal      = {Applied Economic Analysis},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1108/aea-04-2024-0183},
}

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Evidence weight

0.37

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

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
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.