Predictability analysis of the Pound’s Brexit exchange rates based on Google Trends data

Journal of Big Data

Konstantinos P. Tsagarakis¹  Konstantinos Gkillas²  Amaryllis Mavragani³ 

[1] Business and Environmental Technology Economics Lab, Department of Environmental Engineering, Democritus University of Thrace;Department of Business Administration, University of Patras;Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling;

关键词: Big data;    Dollar;    Euro;    Exchange rates;    Google Trends;    Internet behavior;   

DOI  :  10.1186/s40537-020-00337-2

来源: DOAJ

【 摘 要 】

Abstract During the last decade, the use of online search traffic data is becoming popular in examining, analyzing, and predicting human behavior, with Google Trends being a popular tool in monitoring and analyzing the users' online search patterns in several research areas, like health, medicine, politics, economics, and finance. Towards the direction of exploring the Sterling Pound’s predictability, we employ Google Trends data from the last 5 years (March 1st, 2015 to February 29th, 2020) and perform predictability analysis on the Pound’s exchange rates to Euro and Dollar. The period selected includes the 2016 UK referendum as well as the actual Brexit day (January 31st, 2020), with the analysis aiming at analyzing the Pound’s relationships with Google query data on Pound-related keywords and topics. A quantile dependence method is employed, i.e., cross-quantilograms, to test for directional predictability from Google Trends data to the Pound’s exchange rates for lags from zero to 30 (in weeks). The results indicate that statistically significant quantile dependencies exist between Google query data and the Pound’s exchange rates, which point to the direction of one of the main implications in this field, that is to examine whether the movements in one economic variable can cause reactions in other economic variables.

【 授权许可】

Unknown