Predictability analysis of the Pound’s Brexit exchange rates based on Google Trends data
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, medici...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-09-01
|
| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40537-020-00337-2 |
| _version_ | 1819049019834368000 |
|---|---|
| author | Amaryllis Mavragani Konstantinos Gkillas Konstantinos P. Tsagarakis |
| author_facet | Amaryllis Mavragani Konstantinos Gkillas Konstantinos P. Tsagarakis |
| author_sort | Amaryllis Mavragani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T11:25:30Z |
| format | Article |
| id | doaj.art-cc41fa4004894847b0ae33334eab819a |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-21T11:25:30Z |
| publishDate | 2020-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-cc41fa4004894847b0ae33334eab819a2022-12-21T19:05:40ZengSpringerOpenJournal of Big Data2196-11152020-09-017111910.1186/s40537-020-00337-2Predictability analysis of the Pound’s Brexit exchange rates based on Google Trends dataAmaryllis Mavragani0Konstantinos Gkillas1Konstantinos P. Tsagarakis2Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of StirlingDepartment of Business Administration, University of PatrasBusiness and Environmental Technology Economics Lab, Department of Environmental Engineering, Democritus University of ThraceAbstract 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.http://link.springer.com/article/10.1186/s40537-020-00337-2Big dataDollarEuroExchange ratesGoogle TrendsInternet behavior |
| spellingShingle | Amaryllis Mavragani Konstantinos Gkillas Konstantinos P. Tsagarakis Predictability analysis of the Pound’s Brexit exchange rates based on Google Trends data Journal of Big Data Big data Dollar Euro Exchange rates Google Trends Internet behavior |
| title | Predictability analysis of the Pound’s Brexit exchange rates based on Google Trends data |
| title_full | Predictability analysis of the Pound’s Brexit exchange rates based on Google Trends data |
| title_fullStr | Predictability analysis of the Pound’s Brexit exchange rates based on Google Trends data |
| title_full_unstemmed | Predictability analysis of the Pound’s Brexit exchange rates based on Google Trends data |
| title_short | Predictability analysis of the Pound’s Brexit exchange rates based on Google Trends data |
| title_sort | predictability analysis of the pound s brexit exchange rates based on google trends data |
| topic | Big data Dollar Euro Exchange rates Google Trends Internet behavior |
| url | http://link.springer.com/article/10.1186/s40537-020-00337-2 |
| work_keys_str_mv | AT amaryllismavragani predictabilityanalysisofthepoundsbrexitexchangeratesbasedongoogletrendsdata AT konstantinosgkillas predictabilityanalysisofthepoundsbrexitexchangeratesbasedongoogletrendsdata AT konstantinosptsagarakis predictabilityanalysisofthepoundsbrexitexchangeratesbasedongoogletrendsdata |