Deciphering Monetary Policy Board Minutes through Text Mining Approach: The Case of Korea

We quantify the Monetary Policy Board (MPB) minutes of the Bank of Korea (BOK) using text mining. We propose a novel approach using a field-specific Korean dictionary and contiguous sequences of words (n-grams) to better capture the subtlety of central bank communications. We find that our lexicon-based indicators help explain the current and future BOK monetary policy decisions when considering an augmented Taylor rule, suggesting that they contain additional information beyond the currently available macroeconomic variables. Our indicators remarkably outperform English-based textual classifications, a media-based measure of economic policy uncertainty, and a data-based measure of macroeconomic uncertainty. Our empirical results also emphasize the importance of using a field-specific dictionary and the original Korean text.