A Concern Analysis of FOMC Statements Comparing The Great Recession and The COVID-19 Pandemic
Luis Felipe Guti\'errez, Sima Siami-Namini, Neda Tavakoli, Akbar Siami, Namin

TL;DR
This paper compares Federal Reserve statements during the Great Recession and the COVID-19 pandemic using NLP to identify shared and unique concerns, revealing notable similarities in their expressed monetary policy considerations.
Contribution
It introduces a novel NLP-based concern analysis of Federal Reserve statements across two major crises, highlighting both commonalities and differences in monetary policy focus.
Findings
Similar concerns in Fed statements during both crises
Distinct concerns emerging in COVID-19 period
Trend analysis of concern evolution over time
Abstract
It is important and informative to compare and contrast major economic crises in order to confront novel and unknown cases such as the COVID-19 pandemic. The 2006 Great Recession and then the 2019 pandemic have a lot to share in terms of unemployment rate, consumption expenditures, and interest rates set by Federal Reserve. In addition to quantitative historical data, it is also interesting to compare the contents of Federal Reserve statements for the period of these two crises and find out whether Federal Reserve cares about similar concerns or there are some other issues that demand separate and unique monetary policies. This paper conducts an analysis to explore the Federal Reserve concerns as expressed in their statements for the period of 2005 to 2020. The concern analysis is performed using natural language processing (NLP) algorithms and a trend analysis of concern is also…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMonetary Policy and Economic Impact · Stock Market Forecasting Methods · Sentiment Analysis and Opinion Mining
