
Monetary Policy & Inflation | US
Monetary Policy & Inflation | US
We use machine learning to convert the Federal Reserve’s (Fed) text-heavy Beige Book into a sentiment score.
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We use machine learning to convert the Federal Reserve’s (Fed) text-heavy Beige Book into a sentiment score. The sentiment (-0.28) of the latest Beige Book report (19 April) showed a slight decrease from the sentiment (-0.26) of the last report (8 March; Chart 1). Notably, the latest report said ‘overall economic activity was little changed in recent weeks’.
Here are the key points from the overall summary of economic activity:
Here is a summary of the latest developments from our sentiment index:
In machine learning, one way to navigate a sea of text and audio-based information is with natural language processing (NLP) techniques. The goal of NLP is to understand textual data to contextualise and extract useful information within it. One application of NLP is sentiment analysis. Sentiment analysis aims to classify whether the opinion expressed in a text is positive or negative (or neutral).
We focus on the Beige Bookand derive a sentiment score by looking at the proportion of positive and negative words in each report. We calculate a raw sentiment score at a district level. Then we aggregate (equally weighted district level average followed by smoothing and detrending) these into an overall sentiment index. We can do this in real-time as soon as the report is released. Charts 4 and 5 show a comparison of our sentiment score to the US ISM PMI and the University of Michigan Consumer Sentiment Index for the US.
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