Analysing the response of U.S. financial market to the Federal Open Market Committee statements and minutes based on computational linguistic approaches

I conduct content analysis and extent the existing models of analysing the reaction of the stock market and foreign currency markets to the release of Federal Open Market Committee (FOMC) statements and meeting minutes. The tone changes and uncertainty level of the monetary policy communication are constructed using the dictionary-based word-count approach at the whole document level. I further apply the Latent Dirichlet Allocation (LDA) algorithm to investigate the different impacts of topics in the meeting minutes. High-frequency data is used as the analysis is an event study. I find that the tone change and uncertainty level have limited explanation power on the magnitude of the effect of the release of FOMC documents especially statements on the financial market. The communication from FOMC is more informative for the market during the zero lower bound period, compared to the whole sample period.