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Google Trends and the forecasting performance of exchange rate models


  • Levent Bulut


In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out†of†sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends†based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends†based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.

Suggested Citation

  • Levent Bulut, 2018. "Google Trends and the forecasting performance of exchange rate models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 303-315, April.
  • Handle: RePEc:wly:jforec:v:37:y:2018:i:3:p:303-315
    DOI: 10.1002/for.2500

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    References listed on IDEAS

    1. Motilal Bicchal & S. Raja Sethu Durai, 2019. "Rationality of inflation expectations: an interpretation of Google Trends data," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 12(3), pages 229-239, September.
    2. Emmanuel Sirimal Silva & Hossein Hassani & Dag Øivind Madsen & Liz Gee, 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends," Social Sciences, MDPI, vol. 8(4), pages 1-23, April.
    3. Vilma Deltuvaitė & Svatopluk Kapounek & Petr Koráb, 2019. "Impact of Behavioural Attention on the Households Foreign Currency Savings as a Response to the External Macroeconomic Shocks," Prague Economic Papers, Prague University of Economics and Business, vol. 2019(2), pages 155-177.
    4. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    5. Bulut Levent & Dogan Can, 2018. "Google Trends and Structural Exchange Rate Models for Turkish Lira–US Dollar Exchange Rate," Review of Middle East Economics and Finance, De Gruyter, vol. 14(2), pages 1-12, August.
    6. Qadan, Mahmoud & Zoua’bi, Maher, 2019. "Financial attention and the demand for information," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 82(C).
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