Employing Google Trends and Deep Learning in Forecasting Financial Market Turbulence

Anastasios Petropoulos, Vasileios Siakoulis, Evangelos Stavroulakis, Panagiotis Lazaris and Nikolaos Vlachogiannakis

Journal of Behavioral Finance, 2022, vol. 23, issue 3, 353-365

Abstract: In this paper we apply text mining methodologies on a set of 10,000 Central Bank speeches to construct a financial dictionary, based on which we use Google Trends indices to measure people’s interest in financial news. Particularly, we investigate the relationship between these indices and financial market turbulence leveraging on Deep Learning techniques, which are benchmarked against a variety of Machine Learning algorithms and traditional statistical techniques. Our main finding is that Google queries convey information able to predict future market turbulence in a short time period (one month), and that Deep Learning algorithms clearly outperform over benchmark techniques. Google Trends can provide useful input in the creation of crisis Early Warning Systems, as social data are more responsive compared to official financial indicators, which are usually available with a lag of several weeks or months. Thus, such an Early Warning System (EWS) that is continuously updated with current social data can be a valuable tool for policymakers, as it can immediately identify signs of whether a crisis is imminent or not.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1080/15427560.2021.1913160 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:hbhfxx:v:23:y:2022:i:3:p:353-365

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/hbhf20

DOI: 10.1080/15427560.2021.1913160

Access Statistics for this article

Journal of Behavioral Finance is currently edited by Brian Bruce

More articles in Journal of Behavioral Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().