The Shiny app created in this project monitors US Inflation. The monitor includes the latest available inflation print, Breakeven Inflation expectations, various inflation drivers and some Text Mining on FOMC Minutes. Shiny app can be found here. The code for the app is available on github. Methodology The main purpose of this app is to give a comprehensive picture of US Inflation. To be able to do this, a couple of diffrent data sources are in use.
The Shiny app created in this project monitors US Fixed Income market, focusing on Treasury Yields. The app will give a summary for some of the most important curves and indices on the US Treasury market as well as Breakeven Inflation expectations. Shiny app can be found here. Code available on github. Methodology Rates are parsed from US Treasury on a daily basis. However, the app will only parse the latest data once every day (of those days the app is used).
Economic projections are collected from each member of the Board of Governors and each Federal Reserve Bank president four times a year. This project extracts and plots the projections in combination with the latest data. Latest report can be found here. Code available on github. Below summarizes the steps taken in this project. Methodology The approach of this project is pretty straight forward - at least in theory. Extract all the FOMC Projections -> plot it.
Text mining FOMC Minutes 2021-03-17 Sentiment has clearly picked up during the fall. One interesting point is that there is a positive net sentiment index, but still below average intensifier index which indicates that the negative words are more connected with intensifier words then what positive words are. Mapping Feds mind gives further juice on this as topics/words like strong, expansion are up and coming while decline, weak are well below its short and long term average.
FOMC Minutes are usually a hot topic on the financial markets. There are plenty of observeres trying to break down what they said, and what they really meant.This project aims to give colour on both the actual context but also (hopefully) the underlying meaning and sentiment. With the field of text mining, this projects breaks down the Minutes into multiple areas making plots out of words. Latest report can be found here.