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

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  • Teona Shugliashvili
  • Erekle Pirveli

Abstract

PurposeThis 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/approachLeveraging 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$-related news topics and economic uncertainty captured by the news.FindingsThe findings reveal that news related to the US dollar and economic uncertainty account for 29% of long-term EUR/US$ 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/valueCombining news attention metrics with macroeconomic fundamentals enhances exchange rate identification, outperforming the models that rely solely on the Taylor rule or news variables.

Suggested Citation

  • Teona Shugliashvili & Erekle Pirveli, 2025. "Unveiling news impact on exchange rates: a hybrid model using NLP and LDA techniques," Applied Economic Analysis, Emerald Group Publishing Limited, vol. 33(99), pages 184-205, April.
    • Handle: RePEc:eme:aeapps:aea-04-2024-0183
    DOI: 10.1108/AEA-04-2024-0183
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