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http://dx.doi.org/10.5351/KJAS.2020.33.3.321

An analysis of the signaling effect of FOMC statements  

Woo, Shinwook (The Bank of Korea)
Chang, Youngjae (Department of Data Science and Statistics, Korea National Open University)
Publication Information
The Korean Journal of Applied Statistics / v.33, no.3, 2020 , pp. 321-334 More about this Journal
Abstract
The US Federal Reserve (Fed) has decided to cut interest rates. When we look at the expression of the FOMC statements at the time of policy change period we can understand that Fed has been communicating with markets through a change of word selection. However, there is a criticism that the method of analyzing the expression of the decision sentence through the context can be subjective and limited in qualitative analysis. In this paper, we evaluate the signaling effect of FOMC statements based on previous research. We analyze decision making characteristics from the viewpoint of text mining and try to predict future policy trend changes by capturing changes in expressions between statements. For this purpose, a decision tree and neural network models are used. As a result of the analysis, it can be judged that the discrepancy indicators between statements could be used to predict the policy change in the future and that the US Federal Reserve has systematically implemented policy signaling through the policy statements.
Keywords
decision tree; FOMC statement; neural network; signaling effect; US Federal Reserve;
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