Similarity analysis of federal reserve statements using document embeddings: the Great Recession vs. COVID-19

Luis Felipe Gutiérrez (), Neda Tavakoli (), Sima Siami-Namini and Akbar Siami Namin ()
Additional contact information
Luis Felipe Gutiérrez: Texas Tech University
Neda Tavakoli: Georgia Institute of Technology
Akbar Siami Namin: Texas Tech University

SN Business & Economics, 2022, vol. 2, issue 7, 1-28

Abstract: Abstract The coronavirus pandemic has already caused plenty of severe problems for humanity and the economy. The exact impact of the COVID-19 pandemic is still unknown, and economists and financial advisers are exploring all possible scenarios to mitigate the risks arising from the pandemic. An intriguing question is whether this pandemic and its impacts are similar, and to what extent, to any other catastrophic events that occurred in the past, such as the 2009 Great Recession. This paper intends to address this problem by analyzing official public announcements and statements issued by federal authorities such as the Federal Reserve. More specifically, we measure similarities of consecutive statements issued by the Federal Reserve during the 2009 Great Recession and the COVID-19 pandemic using natural language processing techniques. Furthermore, we explore the usage of document embedding representations of the statements in a more complex task: clustering. Our analysis shows that, using an advanced NLP technique in document embedding such as Doc2Vec, we can detect a difference of 10.8% in similarities of Federal Open Market Committee (FOMC) statements issued during the Great Recession (2007–2009) and the COVID-19 pandemic. Finally, the results of our clustering exercise show that the document embeddings representations of the statements are suitable for more complex tasks, which provides a basis for future applications of state-of-the-art natural language processing techniques using the FOMC post-meeting statements as the dataset.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s43546-022-00248-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:snbeco:v:2:y:2022:i:7:d:10.1007_s43546-022-00248-9

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43546

DOI: 10.1007/s43546-022-00248-9

Access Statistics for this article

SN Business & Economics is currently edited by Gino D'Oca

More articles in SN Business & Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().