The Prattle Primer

Contents

Introduction

The Prattle Process, Part 1: Why Robots Are Reading

The Prattle Process, Part 2: How Central Banks Move Markets

The Prattle Process, Part 3: Under the Hood

Conclusion

Introduction

What does Prattle do—and why? We put together this primer

to give short, clear answers to those two questions. A tale

littered with reading robots, incomprehensible bureaucrats, and

a financial crisis, the following pages will take the reader on an

interesting—and hopefully helpful—journey towards an

understanding of our company, its vision, and the technological

approach we believe is part of the future of finance.

The Prattle Process, Part 1:

Why Robots Are Reading

In large part, the economy is shaped by words. The attitudes

of investors, bankers, politicians, and the general public drive

the market—a linguistic and monetary reality that has only

become more apparent since the ascendency of the internet in

global trade.

Because of this phenomenon, a methodology of systematic

language interpretation was developed. This process, known as

sentiment analysis, had several forms. Basic sentiment analysis

used a fairly straightforward method: first, two separate

dictionaries of positive and negative buzzwords were created;

then, the sum total of the negative buzzwords in a given

document was subtracted from the sum total of the positive

words in that document; and this simple mathematical

operation produced the document’s score.

Including positive and negative phrases into their term

dictionaries, the most sophisticated iterations of sentiment

analysis bolstered accuracy by taking this higher degree of

complexity into account.

The advantages of such an automated process were numerous:

programs could consume far more information far more quickly

than a human analyst—or even a team of analysts—ever could;

programs could produce evaluations nearly instantaneously;

and, perhaps most importantly, programs did not fall prey to the

various biases that plague human interpretation.

Yet for all its advantages, classic sentiment analysis could not

reproduce the sophistication of human evaluation. To truly

assess a piece of communication, an interpretation must

account for far more than individual words and phrases:

meaning develops over sentences, paragraphs, and a piece as

a whole, and credible evaluations take such breadth into

account.

In addition, these techniques often operated with dictionaries of

words assembled by programmers—not analysts with domain

expertise. Since the accuracy and inclusiveness of the term

dictionaries is vital to the credibility of the automated

evaluations, the failure of programmers to incorporate domain

expertise into the foundation of the automated process counted

as a significant strike against classic sentiment analysis.

Such shortcomings have inspired the founders of Prattle to

develop a new generation of sentiment analysis. Leveraging the

inclusivity, speed, and objectivity of classic sentiment analysis

while benefiting from the sophistication and depth of domain

expertise, this propriety process puts the best of both under

one hood.

The Prattle Process, Part 2:

How Central Banks Move Markets

While Prattle’s language interpreting tech has broad financial

applications, our process was originally built to deal with a

very specific set of institutions: central banks. Because of this,

the story of how Prattle built its machine doesn’t begin with the

particulars of our innovations…it begins with the history of these

institutions.

Many modern central banks are practically engines of content.

The Federal Reserve, for instance, produces press releases and

meeting minutes, gives speeches and congressional testimony,

and arranges press conferences and interviews. Outside of the

People’s Bank of China, this level of dedication is standard

among the world’s leading central banks. While transparency

may be the status quo these days, turn back the pages of

central banking history 30 years, and you’ll find institutions with

an altogether different approach to communication. Back in the

early nineties, central banks were the black boxes of finance.

When it came to opaque communication, former Fed Chairman

Alan Greenspan was a particularly gifted practitioner. He and his

predecessor, Paul Volcker, had developed a rhetoric so

renowned for its incomprehensibility that it even earned its own

moniker: Fedspeak. For Greenspan, Fed communications

should serve as a buffer between the delicate inner workings of

the Fed and the prying eyes and (perhaps) contaminating

influence of those outside, and so, when Greenspan did speak,

his language was so esoteric, so convoluted that there seemed

little point to even listen.

Slowly, Greenspan began to see Fed communications in a

different light, and, by the end of his time as chairman,

Fedspeak was no longer used as a tool of obfuscation. Instead,

Greenspan used such opportunities to give politicians and the

public insight into the Fed’s thinking—a policy of open

communication that has not only blossomed under the reign of

Bernanke and Yellen, but in central banks all over the world.1

These days central banks use communication not only as a

means of explaining their policy decisions, but as policy in its

own right. As transparency began to be embraced, it became

clear that communication may have more utility than was first

thought. While undoubtedly useful for encouraging trust in

central banks, communications actually powerfully influence

markets, and, given the power central banks wield over the

respective markets they oversee, this makes perfect sense.

And this power has only grown since the financial crisis.

Despite this development, the connection between central

bank communications and the market has yet to receive the

1 It should be noted that the Fed did not introduce transparency as a central banking tool. That distinction is the Bank of England’s, who began implementing transparency measures in the eighties. Their forward-thinking strategies eventually influenced the rest of Europe and America to adopt the tool.

kind of attention due its significance: academic literature and

private research publications have hardly touched the topic.

Why? We’ll save the answer to that question and (now that the

stage is set) finally dive into the gears of Prattle’s machine in the

third and final section of this book.

The Prattle Process, Part 3:

Under the Hood

Why have the connections between central bank

communications and the market been largely untouched? 2

Because much of central bank watching has been absorbed in

an interpretive practice lifted straight from your university

English classes.

2 The communications we're referring to here are the text (not data) releases.

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.

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 incrementally through

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

immediate effects of the policy of words currently at work.

4. The type of conclusions this method produces 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 the 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 financial

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.

Conclusion

What does Prattle do—and why? We hope this primer has

given short, clear answers to those two questions. If you’re

interesting in our solutions—or would simply like to learn

more—our team is always ready to help. Contacting us is easy.

Just visit prattle.co and click the “contact” button, and one of our

team members will reply as soon as possible. Thanks for your

time, and we hope to hear from you soon.

The Prattle Team