International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 7 Issue 6
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Big-Data Sentiment in Exchange Rate Forecasting : Evidence from Two Million Models

Author(s) Mr. Khatai Abbasov, Prof. Dr. Gelengül Koçaslan
Country Poland
Abstract This study investigates the predictability of the GBP/USD exchange rate using an exceptionally large and unrestricted set of Google search-volume indicators. An automated forecasting framework estimates approximately two million sentiment-based models and evaluates performance using strictly out-of-sample cross-validated RMSE. The study tackles three central research questions: 1) whether forecast gains from search-volume indicators are confined to a small set of finely tuned specifications; 2) whether successful models rely on similar or distinct combinations of predictors; and 3) whether the resulting model clusters correspond to economically interpretable regimes. The results show that sheer number of models outperform the benchmarks. Text-similarity analysis further reveals that these gains arise from a diverse model space rather than minor variations of a single specification. Using expert-based keyword classification, the study uncovers two consistent predictor regimes: a) slow-moving macroeconomic fundamentals; b) fast-moving financial-market sentiments. This mirrors the dual structure of exchange-rate determination emphasized in both macroeconomic and microstructure theories.
Keywords Exchange Rate Forecasting, Statistical Learning, Google Trends, Search-Volume Data, Model Clustering, Text Similarity, Jaccard Distance, Hierarchical Clustering, Silhouette Analysis
Field Sociology > Economics
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-28
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.61380
Short DOI https://doi.org/hbdrhs
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