Part 3: Decoding Central Bank Speeches with NLP (Fed Meetings)

Introduction Central bank communications have become increasingly important in modern monetary policy. The Federal Reserve, in particular, has evolved from a secretive institution to one that actively uses language as a policy tool. Every word uttered by Fed officials is scrutinized by markets, and the ability to systematically decode these signals can provide significant trading insights. In the previous episodes, we explored sentiment analysis of financial news using FinBERT and mapped market volatility to global headlines. Now, we turn our attention to a more structured form of communication: Federal Reserve speeches and FOMC (Federal Open Market Committee) meeting minutes. These documents contain carefully crafted language that signals future policy directions—often called “forward guidance.” This tutorial will demonstrate how to build an NLP pipeline to analyze Fed communications, classify hawkish versus dovish sentiment, track policy language evolution over time, and construct a Fed Sentiment Index that correlates with market movements. Data Sources and Acquisition Federal Reserve Economic Data (FRED) The recommended Kaggle dataset for this analysis is the

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