Background
The goal is to leverage NLP to "watch" the Fed's commentary to understand key parameters in their policy decision making. We always read or listen to follow the Fed. However, we may miss part of the information especially how things change over time. We can leverage NLP to augment our tracking to "quantify" any important changes that we should be flagged.
Data/input:
You can use any public data you wish. Below is one of the sources to consider:
The Fed's site contains some of the key communicated materials. In this case, communications are stored under "Statement", "Press conference", and "Minutes" (PDF/HTML). Again, you don’t have to limit your data sources with this. For example, other regional Feds' websites may have useful information. You can certainly utilize other platforms such as Twitter.
Task:
Perform an analysis for 2019-current. You can be creative about the analysis. The goal is to identify key topics/mentioned/sentiment over time with an NLP related approach. The process should be systematic as possible from the data scraping step.
Output:
It includes two parts: coding scripts and a 1–3-page summary report.
For the coding and analysis part, Python is preferred. Please zip the files or save them on GitHub.
The report summary should contain but not limited to key changes identified in the Fed's mentioned/tone/key parameters in their policy reaction function – and anything you think that's relevant to your conclusion. Your conclusion can be indicating key changes over time that we should pay attention to, or indicating the Fed's becoming more hawkish or less, or indicating if the Fed's policy pivots towards the tighter or looser side (e.g., likely to hike or cut policy rates going forward). Also consider including key visualizations to support your conclusion.
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