Beige Book Sentiment Falls Near March Lows

Summary

We use machine learning to convert the Fed’s text-heavy Beige Book into a sentiment score. The latest Beige Book report released on 13 July shows a decrease in sentiment from the last report on 1 June (Chart 1). The sentiment remains near the lows of the 2 March release. This marks the first decrease in sentiment since the 2 March release, which was followed by two increases in sentiment (20 April and 1 June reports).  

Here is a summary of the latest developments:

• Breaking down the sentiment by regional district, we find Minneapolis is the only one showing a positive sentiment (Chart 2).
• Philadelphia has flipped to negative sentiment after remaining positive for the last two releases.  
• Cleveland is showing the most negative sentiment, followed by Dallas and Chicago. Notably, Dallas has remained in the bottom two districts by sentiment for all reports since we released our sentiment index on 21 April.
• We will update this report just after the release of the next Beige Book on 7 September.

This small change in sentiment could reflect that the economy is currently going through an air pocket caused by fiscal consolidation, a strong dollar and a small inventory cycle that are likely to get resolved by end-year (see US Growth: Air Pocket Not Crash).

What Is the Macro Hive Beige Book Sentiment Score?

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.

Dalvir Mandara is a Quantitative Researcher at Macro Hive. Dalvir has a BSc Mathematics and Computer Science and an MSc Mathematical Finance both from the University of Birmingham. His areas of interest are in the applications of machine learning, deep learning and alternative data for predictive modelling of financial markets.

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