Google Trends and the forecasting performance of exchange rate models

• Date: 01 April 2018
• Published date: 01 April 2018
• DOI: http://doi.org/10.1002/for.2500

RESEARCH ARTICLE

Google Trends and the forecasting performance of exchange

rate models

Levent Bulut

Department of Economics and Finance,

Harley Langdale, Jr. College of Business

Administration, Valdosta State University,

Valdosta, GA, USA

Correspondence

Department of Economics and Finance,

Harley Langdale, Jr. College of Business

Administration, Valdosta State University,

Valdosta, GA 31698, USA.

Email: lbulut@valdosta.edu

Funding information

The Scientific and Technological Research

Council of Turkey (TUBITAK), Grant/

Award Number: 115C089

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 fun-

damentals could beat the random walk null only in one out of 11 currency

pairs, with Google Trends predictors we found evidence of better perfor-

mance for five currency pairs. We believe that these findings necessitate fur-

ther research in this area to investigate the extravalue one can get from

Google search query data.

KEYWORDS

foreign exchange,out‐of‐sample exchange rate predictability, model evaluation,Google Trends,

nowcasting

1|INTRODUCTION

Is it possible to use Internet search query data to proxy

some of the exchange rate fundamentals before the offi-

cial data releases and use that information to forecast

the monthly exchange rate returns accordingly? In this

paper, we aim to find an answer to this question by

using the Google Trends search query data for a sample

of 11 currency pairs. By using Internet search data, we

aim to get a timely description of the state of the

economy long before the official data are released to

the market participants. Government data releases in

all countries follow a lag and the market has access to

these mostly monthly data in the middle of the follow-

ing month or later. It is worth noting that whether we

use the real‐time or revised official data, the lag in the

availability of the official data will always be a problem.

The only difference would be that the revised official

data will be available to the market participants with

even more lags. On the other hand, with the help of

Received: 6 August 2015 Revised: 29 September 2017 Accepted: 11 October 2017

DOI: 10.1002/for.2500

Journal of Forecasting. 2018;37:303–315. Copyright © 2017 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/for 303