Google Trends and the forecasting performance of exchange rate models

Abstract

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.

Uncertainty, Perception and the Internet

“…Sirotkin (2012) claims that since users are unlikely to be experts in traditional information retrieval systems and query language, Web search engines target the average Internet user, or to be more precise, any Internet user, whether new to the web or a seasoned Usenet veteran. Remarkably, the literature has recently started to use Internet search data with different aims and interpretations: either as predictors in forecasting (Vosen and Schmidt, 2011, Carrière-Swallow and Labbé, 2013, D'Amuri and Marcucci, 2017, Bulut, 2018, Gotz and Knetsch, 2019, as an index of well-being (Algan et al, 2016), as an index of job search activity (Baker and Fradkin, 2017), or as a measure of individual moods (the investors' sentiment in Da et al, , 2015, the interest that the municipal balance sheet generates among voters in Repetto, 2018, and investors' need for information about earnings announcements in Drake et al, 2012).…”

Use of Neural Networks to Accommodate Seasonal Fluctuations When Equalizing Time Series for the CZK/RMB Exchange Rate

“…New patterns in intraday currency trading were revealed by Khademalomoom and Narayan (2020); and a currency trading strategy that took into account the predictive power of currency implied volatility was presented by Ornelas and Mauad (2019) and Accominotti et al (2019). Bulut (2018) successfully used Google Trends to predict exchange rates. Amo Baffour et al (2019) dealt with the integration of an asymmetric model into an artificial neural network for the prediction of the exchange rates of five currencies.…”

Can One Improve Now-Casts of Crop Prices in Africa? Google Can.

“…First, its search engine market share exceeds 90% in most European countries (The Economist 2017). Second, Google's global market share ranges at 59% and its dominance is even larger in the mobile and tablet devices market, where the market share is 90.8% (Bulut 2017). Android-based smartphones and tablets, i.e.…”

“…GSQ data holds promising potential for the now-casting and inter-period forecasting of a variety of indicators, since Google releases its query data on a weekly basis and, hence, earlier than standard reports and data used for crises forecasting. The use of GSQ data has found wide applications during the last decade: from understanding the spread of epidemics (Ginsberg et al 2009;Lazer et al 2014), to political attitudes (Stephens-Davidowitz 2013; Marthews and Tucker 2014) and human behavior (Stephens-Davidowitz 2017), as well as in the field of economics, to now-casting and forecasting private consumption (Vosen and Schmidt 2011), inflation expectations (Guzmán 2011), stock market volatility (Hamid and Heiden 2015), developments on financial markets (Preis, Moat, and Stanley 2013), exchange rates (Bulut 2017), and unemployment rates (Askitas and Zimmermann 2015;Suhoy 2009). These studies, however, share one aspect: the use of GSQ data in the context of industrialized countries, where high Internet-adoption rates prevail.…”