Beige Book Sentiment Rises, But Still Historically Low

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 20 April showed the first increase in sentiment since the 8 September 2021 release (Chart 1), though sentiment still remains historically low.
• We also break down sentiment by regional district. We find Philadelphia is showing the most positive sentiment, San Francisco and Minneapolis are showing a neutral sentiment, and Dallas is showing the most negative sentiment (Chart 2).
• The increase in the sentiment index mirrors recently positive data surprises that likely reflect the impact of pre-Ukraine crisis supply improvements. These are unlikely to last as new negative supply shocks related to Ukraine and China work their way through global supply chains.
• We will update this report just after the release of the next Beige Book on 1 June.

What Is the Beige Book?

The Beige Book is a Fed publication released eight times a year before FOMC meetings. Federal Reserve Banks across 12 districts of the US gather anecdotal information on current economic conditions through interviews with ‘key business contacts, economists, market experts, and other sources. The Beige Book distils the economic views by district and provides a national summary.

Since the information contained in the Beige Book influences Fed decisions, it makes sense for investors to read reach report diligently. However, a typical Beige Book report contains over 10,000 words, which many find daunting. We therefore use machine learning to turn the report into a sentiment score.

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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