Modeling Topic Trends in FOMC Meetings

Modeling Topic Trends in FOMC Meetings

In this article I show how topic modeling, an area of natural language processing (NLP), can help to analyze the content of FOMC meetings. I use Latent Dirichlet Allocation (LDA), a popular topic modeling approach, to identify the key themes, or topics, discussed in the meeting minutes.

The Federal Open Market Committee (FOMC) is an important part of the US financial system. It meets 8 times per year and the minutes from these meetings are scrutinized the world over. Using topic modeling, an area of natural language processing, you can analyze trends in FOMC minutes over time. In this article, I show you how.

The Federal Open Market Committee (FOMC) sets monetary policy in the US. It has 12 members who meet 8 times per year to discuss interest rates and other economic matters. Investors pay close attention to the outcomes from these meetings — they can have significant consequences for US and global financial markets.The minutes of FOMC meetings are released three weeks after each meeting. Through the minutes, investors can get a better understanding of the content of FOMC meetings. This helps with interpreting FOMC decisions and understanding the possible consequences for financial markets.In this article I show how topic modeling, an area of natural language processing (NLP), can help to analyze the content of FOMC meetings. I use Latent Dirichlet Allocation (LDA), a popular topic modeling approach, to identify the key themes, or topics, discussed in the meeting minutes.

Topic modeling and LDA

Topic modeling is a form of unsupervised learning that can be applied to unstructured text data. It identifies groups of words or phrases that have similar meaning — topics — using statistical techniques.LDA works by assuming that each document has a mix of underlying (latent) topics, and that each topic is made up of words from a specified dictionary. By observing the words within a set of documents, LDA infers the topics that fit with those words based on a probabilistic framework.The mix of topics in a chronological series of text documents, such as FOMC minutes, changes over time. With LDA, you can observe this changing mix. The changing mix of FOMC topics is of interest to investors and market observers as it indicates areas of relative focus in FOMC discussions.To learn more about topic modeling and LDA, including a hands-on example, see this [introductory article_](https://highdemandskills.com/topic-modeling-lda/)._

fed-fomc text-analytics data-science topic-modeling

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