Key Points
- The importance of understanding the Federal Open Market Committee (FOMC) outlook has increased due to the correlation between fixed-income assets and equities.
- Machine learning (ML) and natural language processing (NLP) techniques can be applied to FOMC statements to analyze sentiment and anticipate changes in the federal funds rate.
- Loughran-McDonald sentiment word lists analyze FOMC statements, categorizing words as positive/hawkish or negative/dovish.
- ML models like BERT and XLNet can be trained to analyze FOMC statements on a sentence-by-sentence basis to provide more accurate sentiment scores.
- Combining ML techniques with human judgment can offer valuable insights for investment management.
The US Federal Reserve began raising the federal funds rate in March 2022, leading to poor performance across asset classes. This has highlighted the need for a better understanding of the FOMC’s outlook. Machine learning (ML) and natural language processing (NLP) techniques can aid in achieving this objective.
The analysis of sentiment in FOMC statements is usually done using Loughran-McDonald sentiment word lists. These lists categorize words as positive/hawkish or negative/dovish. However, this approach fails to capture the sentiment at the sentence level. To address this limitation, ML models like BERT and XLNet can be trained to analyze FOMC statements sentence-by-sentence.
By applying these ML techniques, it is possible to gain a better understanding of the FOMC’s policy perspective and improve interpretations of central bank communications. Furthermore, there is a correlation between changes in the sentiment of FOMC statements and US stock market returns, indicating the potential usefulness of these techniques in investment management.
While ML and AI models may be considered black boxes, the interpretation of their results is crucial. Local Interpretable Model-Agnostic Explanations (LIME) can be used to explain complex models using a simpler model. This allows for a deeper understanding of the sentiment analysis results.
In conclusion, machine learning techniques have the potential to enhance investment management by providing a more accurate understanding of the FOMC’s outlook. However, the ultimate value of these techniques depends on how they are combined with human judgment.
