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Measuring Monetary Policy Surprises Using Text Mining: The Case of Korea

Youngjoon Lee (), Soohyon Kim and Ki Young Park ()
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Youngjoon Lee: School of Business, Yonsei University
Soohyon Kim: Economic Research Institute, Bank of Korea
Ki Young Park: School of Economics, Yonsei University

No 2019-11, Working Papers from Economic Research Institute, Bank of Korea

Abstract: We propose a novel approach to measure monetary policy shocks using sentiment analysis. We quantify the tones of 24,079 news articles around 152 dates of Monetary Policy Board (MPB) meetings of the Bank of Korea (BOK) from March 2005 to November 2017. We then measure monetary policy surprises using the changes of those tones following monetary policy announcements and estimate the impact of monetary policy surprises on asset prices. Our measure of monetary policy surprises better explains changes in long-term rates, while changes in the Bank of Korea's base rate are more closely associated with changes in short-term rates (maturity of one year less). Our results strongly suggest that using a text mining approach to measure monetary policy surprises sheds light on information related to forward guidance and market expectations on future monetary policy.

Keywords: Monetary policy; Text mining; Central banking; Bank of Korea (search for similar items in EconPapers)
JEL-codes: E43 E52 E58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cba, nep-mac and nep-mon
Date: 2019-03-06
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