Why have the connections between central bank communications and the market been largely untouched?* Because much of central bank watching has been absorbed in an interpretive practice lifted straight from your university English classes.
As we touched on earlier, central banks produce a wide variety of content. But, rather than being treated as the financial data that they are, these communications are approached with a very different mindset–almost as if they were poetry.
(Example of literary close reading)
In traditional literary studies, close reading is taught as a standard means of interpreting texts. In short, the method uses the subtle, granular details embedded within a given text as the main source of interpreted meaning. While close reading is adept at elaborating literary works, its merits as means of evaluating central banking communications aren’t nearly as obvious.
Despite this, close reading seems to be the precise method of choice for orthodox central bank watchers.
For example, a standard breakdown of a speech by Fed Chairman Janet Yellen would likely spend a sizable chunk of its print on a select few words and phrases. Such an analysis would use the smallest details to form an assertion of the central bank’s mood: the Fed is hawkish (for instance) because Yellen used “moderate” instead of “modest.”
The issues with this methodology are numerous, but there are at least four large problems worth discussing here:
1. Central banks produce too much material. If central banks only published press releases updated by track changes, then each minute change has a great deal of importance, and close reading could have some merit. But central banks produce a litany of content, and, therefore, proper evaluation necessitates a more comprehensive approach.
2. Human interpretation is simply too prone to cognitive errors. Each element of a standard close-reading analysis of central bank communications, whether it’s word choice or the particularly history used to evaluate those words, is subject to the innumerable biases and mistakes that plague human evaluation. When it comes to poetry, such drawbacks may be negligible, but, when it comes to producing objective analyses of communications that move billions of dollars, “negligible” is not a word that comes to mind.
3. The detail-centric nature of this approach–and the errors come along with it–hide what could be the most important question in central bank watching. While what central bank communications reveal about monetary policy is undoubtedly important, what is perhaps equally important is how communications themselves act as policy. Words move the markets, and the preoccupation with what a handful of expressions could mean about future policy obscures the actual, immediate effects of the policy of words currently at work.
4. The type of conclusions this method are difficult to operationalize. Investing has becoming a game of quantitative models–even the discretionary macro space is moving in that direction–and quantitative models need quantitative data. Qualitative assessments, like those that standard analyses produce, are hard to plug into multi-factor financial models. “Fairly dovish” just isn’t an ideal input.
It seems clear that close-reading is not a viable method of interpreting central bank communications, but, as we covered in an earlier section, classic automated interpretation (sentiment analysis) has equally fatal flaws.
It is in this context that Prattle developed its method of analysis. Taking the best from automated interpretation technology and domain expertise, Prattle has produced the world’s first unbiased, comprehensive, and quantitative evaluations of these institutional communications: the Prattle Central Bank Sentiment Indexes.
Prattle’s Central Bank Sentiment Indexes are rooted in reference texts. These texts are central bank communications that have led to particular, identifiable market reactions–the type and level of which allow the texts to be expertly scored.
For central banks, this score is an indication of the “hawkishness” or “dovishness” of a central bank’s position on the economy. A hawkish central bank views the economy as strong and growing and, because of this perception, will soon implement contractionary monetary policy–raising interest rates to ensure credit is less available–in an attempt to keep the market from overheating. A dovish central bank believes the economy is struggling and takes the corresponding strategy: lowering interest rates to encourage growth through a climate of easier credit. Directly connected to varying degrees of market reaction, these reference documents are firmly rooted in history–making them an excellent foundation of comparison.
Using these reference texts, Prattle has mathematically linked specific words, phrases, sentences, paragraphs and whole communications to specific market reactions. Expressions linked to hawkish policy and corresponding market reaction are awarded positive numbers based on the level of the response. Conversely, dovish terms are awarded negative numbers. This lexicon of hawkish and dovish expressions is the backbone of our methodology.
With a lexicon in place, it now becomes possible to accurately evaluate current central bank communications.
Aggregating text from all the streams of a given central bank’s communication within whatever timeframe is desired, our process then generates a score for the sample in light of the pool of hawkish and dovish communications. The score generated is the average rating of all the hawkish and dovish expressions embedded within the selected documents for a given time period.
This score represents the central bank’s “mood”–i.e. their inflation expectations. We call each bank’s mood their “Index,” and these signals are the only comprehensive, unbiased, and quantitative data on the economic outlook of central banks in existence.
These scores can be used by quantitative traders as a plug-in for their multi-factor models or by portfolio managers looking for a discrete, unbiased measure of economic performance. There are numerous applications for Prattle’s central bank data, but decoding these institutional communications is only the first application of our approach.
The future of our analytical technology is transforming all manner of market-moving texts into tradable data. Corporate communications, regulator documents, articles in the news media– these are only a few major currents in the ocean of content that informs price, and we at Prattle seek to understand and map the economic influence of these flows.
In the today’s global economy, that process begins with central banks, but it certainly doesn’t end there. The “meaning” of text in the information age is only beginning to be understood, and sentiment analysis technology is the key to progress in this endeavor. Prattle is building systems trained to draw from a flood of words the remarkable, untapped potential of these data streams, helping lead the way towards the future of finance.
*The communications we’re referring to here are the text (not data) releases.