Beige Book Sentiment Increases

Summary

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 (18 October) increased from the sentiment (-0.29) of the last report (6 September).

Here are the key points from the overall summary of economic activity:

• Most Districts indicated little to no change in economic activity since the September report.
• Consumer spending was mixed, especially among general retailers and auto dealers, due to differences in prices and product offerings.
• Tourism activity continued to improve, although some Districts reported slight slowing in consumer travel, and a few Districts noted an uptick in business travel.
• Banking contacts reported slight to modest declines in loan demand.
• Consumer credit quality was generally described as stable or healthy, with delinquency rates still historically low but slightly increasing.
• Real estate conditions were little changed and the inventory of homes for sale remained low.
• Manufacturing activity was mixed, although contacts across multiple Districts noted an improving outlook for the sector.
• The near-term outlook for the economy was generally described as stable or having slightly weaker growth.
• Expectations of firms for which the holiday shopping season is an important driver of sales were mixed.

Here is a summary of the latest developments from our sentiment index:

• St Louis (-0.73, previously: 0.05) has seen the largest drop in sentiment compared to the last report.
• Chicago (-0.23, previously: -0.64) has seen the largest increase in sentiment compared to the last report.
• St Louis, Kansas City, and Atlanta have the lowest sentiment.
• The National Summary, Philadelphia, and Minneapolis have the highest sentiment.

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 Book and 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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