Economic forecasting with non-specific Google Trends sentiments: Insights from US data
Publication date
2024
Document type
Konferenzbeitrag
Author
Diaf, Sami
Organisational unit
Conference
6th International Conference on Advanced Research Methods and Analytics (CARMA 2024) ; Valencia, Spain ; June 26–28, 2024
Publisher
Universitat Politècnica de València
Book title
roceedings of the 6th International Conference on Advanced Research Methods and Analytics (CARMA 2024)
First page
10
Last page
17
Part of the university bibliography
✅
Language
English
Abstract
The influence of specific Google Trends search queries measuring various sentiments on economic performance and stock markets has been extensively documented and used for many purposes. This paper examines the predictive power of queries measuring non-specific sentiment on key macroeconomic variables when linked to a comprehensive sentiment dictionary. The analysis shows that non-specific sentiments do not improve the forecasting quality of the US economy as a whole, except for unemployment, which was found to be predictable for all sentiments. Consequently, the authors suggest that economic-related sentiments with carefully selected words should be used in Google Trends search queries to improve predictive performance. However, if a socio-cultural analysis is to be performed, non-specific sentiments would be suggested, as they can be predicted by the real economic time series of unemployment.
Description
This work is licensed under a Creative Commons License CC BY-NC-SA 4.0.
Version
Published version
Access right on openHSU
Metadata only access