Beige Book Sentiment: Turning Point?

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 1 June has shown another increase in sentiment following the increase from the last release on 20 April (Chart 1). So far, the sentiment has bottomed out on the 2 March release. Could a turning point for the sentiment be under way? Perhaps, but it still remains historically low nonetheless.

That said, an increase in sentiment would be consistent with the still strong momentum in the US economy, with consumption growth accelerating, NFP exceeding expectations, and PMIs remaining well above 50.

Here is a summary of the latest developments:

• Breaking down the sentiment by regional district we find Philadelphia is again showing the most positive sentiment, followed by Minneapolis. Meanwhile, Dallas is showing the most negative sentiment, followed by Atlanta and New York (Chart 2).
• We will update this report just after the release of the next Beige Book on 13 July.

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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