Monetary policy decisions may be data-dependent, but market reaction functions are driven by words. New patented technology now makes it possible to bridge the gap between public policy risks (which are expressed verbally) and market risks (which are expressed quantitatively).
Markets spend millions in the race to extend the efficient frontier. They acquire the fastest, best access to information flows and analysis regarding what advanced economy central bankers did in order to power decision intelligence and risk pricing. For over a century, they have focused on the news to deliver the most reliable information about monetary policy.
The need for speed is real because it delivers operational efficiencies that drive alpha generation.
From the telegraph and the tickertape to the Bloomberg Terminal and the Blackberry, the financial industry has been an early adopter of information technology that speeds access to the news cycle.
Wall Street was among the first to deploy automated readers to parse through institutional news feeds in order to accelerate signal detection from news and headlines.
The arms race to automate signal detection from the news cycle is now over. The technology has been widely deployed in the markets for over a decade. The capacity to deploy computers to read the news is now "table stakes" for the industry.
Markets measure informational advantages in milliseconds as information travels at the speed of light.
The efficient frontier had settled into stasis. Then GenerativeAI burst onto the scene, holding the potential for increased efficiency in the retrieval of information important to support decision intelligence.
But look closely at those images. What is missing? The actual language from policymakers.
Two Inefficient Risks
Technology has extended the efficient frontier. But as noted above, gaps exist in the input process, which generates inefficiencies. Current technology also generates some risks. Managing those risks introduces inefficiencies.
Concentration Risk, in stereo: Instant access to the newsflow that becomes interoperable with trade execution means that firms are exposed to concentration risk. All market participants share the same inputs, which means their automated systems will react automatically to the same news at a high rate of velocity.
Markets control for that risk by hiring subject matter experts. But this risk control initiative concentrates signal detection back on humans who remain mired in hunter-gatherer mode. They waste time gathering information for analysis. Information overload is common.
Finally, markets have concentrated their data input into one kind of data (language regarding monetary policy) but they need to translate that data into a market price. The language does not line up with the kinds of data that markets use to measure risks and set prices for those risks.
Until recently (see below) it has not been possible to compare momentum and volatility dynamics for public policy on a par with tradeable assets.
GenerativeAI/Data Governance Risk: Deploying generative AI to operate as an automated research assistant is an obvious initial use case to address concentration risk. But deployment requires careful attention to the training data. Your outputs will only be as good as the inputs used to train the model.
This is particularly problematic in the monetary policy context, which is a highly specialized field. Many people may publish opinions regarding inflation and interest rates, but that does not mean that those opinions provide meaningful insight regarding the trajectory of monetary policy. In addition, even highly trained experts are often wrong.
If a language model was trained on all language from the entire internet, it will include content from individuals that have no particulate knowledge about the underlying subject matter. If you have not implemented governance standards regarding which types of monetary policy data (quantitative and verbal) are used within RAG and Knowledge Graph processes, your outputs will at a minimum display significant skews and substandard outcomes.
PolicyScope data addresses both of these inefficiencies with a ground-breaking, patented approach that creates objective, momentum-based data and signals for use as training data by strategic analysts in the capital markets, advocacy, and strategy consulting professions.
One Efficient Solution -- PolicyScope Data
Our patented process makes it possible for the first time to bridge the divide between public policy risks (which are expressed verbally) and market risks (which are expressed quantitatively).
Backtests show that quantitative PolicyScope data regarding monetary policy consistently delivers an average of 24-36 hours advance notice of volatility in at least the FX and VIX markets.
Pairing the quantitative data with the structured, tokenized language data also helps ground the generativeAI training process while making it more efficient, saving deployment costs.
Outlook for 2025-26
The monetary policy cycle is poised to enter into a period of particular volatility as the United States attempts to engineer a soft landing amid a dramatic election. Downstream reaction functions among emerging market central banks are inevitable given the global role of the dollar.
All advanced economy central banks share the challenge of setting monetary policy among significant paradigm shifts in supply chain and labor dynamics.
The combined impact of
rising geopolitical tensions;
intensifying trade policy tensions;
accelerating climate transition policies (subsidy allocations, carbon taxes, energy efficiency requirements, etc.) which impact inflation and other monetary policy aggregates; and
shifts in fiscal policy from newly elected governments (including six G7 members!)
ensure that 2025-26 will deliver some of the most challenging global macro environments in a decade.
Contact us today to learn how you can mobilize language-derived alternative data to spot monetary policy signals hiding in plain sight and achieve alpha gains.
BCMstrategy, Inc. delivers industry-defining quantitative and language data to help power advanced decision intelligence in capital markets, advocacy, and strategy consulting. The award-winning patented process provides multifactor time series data and structured language data designed from the beginning to support a wide range of AI-powered predictive analytics solutions.
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