Words Matter: Effects of Semantic Similarity of Monetary Policy Committee's Decision on Financial Market Volatility

The objective of the paper is to study the effects of semantic similarity of the Bank of Thailand's press releases on volatility of financial markets in Thailand from 2010–2018. The Natural Language Processing (NLP) is employed to construct the semantic similarity from 72 press releases. The semantic similarity represents the public signal that the central bank delivers to the public in the framework of a Keynesian beauty contest game.