Addressing Context and Comparability in Global Macro Historical Data
- BCMstrategy, Inc.
- Jan 29
- 5 min read
Global macro use cases are particularly exposed to data deficiency arising from lack of context and comparability. Shifts in monetary policy, regulatory policy, fiscal policy, and national security policy can fundamentally impact market reaction functions across asset classes and time horizons.
Data feeds to support global macro portfolio management face two related challenges: comparability and context.
Addressing Context and comparability in Global Macro Historical Data
The Comparability Challenge
Even when historical economic or financial data exists, it isn’t plug‑and‑play. Macroeconomic indicators—GDP, inflation, unemployment—vary in definition across time and countries. Methodological changes, rebasing, and index reweighting turn seemingly continuous series into stitched segments with different meanings.
Inflation indices evolve to account for substitution, quality adjustments, and housing—complicating macro model calibration.
GDP comparability challenges abound between developed and emerging markets, across base years, and through national statistical revisions.
Labor market statistics depend on survey design and definitions of “actively seeking,” affecting cross‑country comparisons.
Definitions of non-performing loans vary widely across jurisdictions.
The comparability challenge expands exponentially when text-based data inputs are included withing automated research deployments powered by Generative AI. Even when policymakers use the same language, they can be conveying considerably different messages. For example, a regulator fretting about financial stability issues in a financial system dominated by bank-based intermediation may be targeting a different kind of volatility than a regulator using the same terminology in a financial system dominated by capital markets and nonbank financial institutions.
For global macro research, forcing equivalence across incompatible time series can degrade signal quality. Many may be tempted to deploy AI-powered processes to generate "synthetic data" in order to solve the comparability challenge. Synthetic data can certainly augment backtesting, but it comes with its own risks.
At BCMstrategy, Inc. we caution against relying too heavily on synthetic data especially in the Generative AI context. We also strongly recommend that any language-related training data and input data incorporate deep engagement from subject matter experts using a consistent, value-neutral ontology. Firms seeking enhanced ROI as well as speed to market can accelerate their language model initiatives by using PolicyScope Data. Our award-winning, patented process automatically labels content daily using a value-neutral, expert-crafted ontology. PolicyScope Data is thus the first plug-and-play language dataset available for immediate deployment into the entire LLM tech stack, from vector databases and knowledge graphs to retrieval augmented generation processes.
The Context Challenge
The current geopolitical context creates an additional urgent need to incorporate context regarding the policy decisions that move markets and change peoples’ lives. Policymakers targeting medium-term geostrategic targets may prioritize different data points and are making different decisions compared to policy targets and priorities during periods of relative geopolitical stability. The policy process provides crucial context that add dimension to any given data point.
Some examples:
A 5% inflation print in 1975 (with wage‑price dynamics and commodity shocks) is not equivalent to a 5% print in a modern framework with anchored expectations, global supply chains, and independent central banks.
A doubling of FX reserves in a large surplus economy has different macro implications than the same move in a small, externally financed market.
A central bank that sees climate action as a core part of its mandate will make different decisions regarding balance sheet management, asset purchase priorities, and potentially access to collateral and liquidity support frameworks. These decisions can materially impact the liquidity and performance of qualifying assets.
Central bank and finance ministry assessments of sovereign fixed income structure stress points can be materially impacted by pro-stablecoin policies that create incentives for stablecoin issuers to hold at scale large amounts of short-dated sovereign fixed income securities, potentially altering bond market performance.
Failing to incorporate data components associated with public policy shifts means that even clean, well‑stitched economic and financial data can mislead when stripped of institutional and policy regime context. Incorporating risk factors regarding public policy traditionally has been more of an art than a science given that policy shifts present first in verbal format while markets measure and price risk in a quantitative format.
Effective global macro portfolio management traditionally requires a complex blend of quantitative research paired with deep qualitative context—policy reaction functions, geopolitical dynamics, demographic trends, and market structure. The goal is to minimize exposure to headline risk (headline-induced short-term market volatility) by adjusting the quantitative data to reflect realistic economic trajectories that can impact investments.
The traditional research process introduces a range of frictions, inefficiencies, and embedded biases. Large firms hire a number of subject matter experts to provide guidance regarding likely policy trajectories; each of these experts chases news flow and other indicators of policy activity in parallel with portfolio managers. They convey analysis in text, which is subsequently read by portfolio managers who then take time to translate the overall analysis into a quantitative country risk factor, a portfolio risk variable and/or a market price.
Each of these steps takes time and create inefficiencies which are offset by the context provided by the expert analysis. But firms tolerate the inefficiencies in order to increase the accuracy of their risk pricing models and in order to increase the effectiveness of their trading strategies.
Advanced technology now makes it possible to incorporate objective context-based policy risk measurements. Specifically, the patented PolicyScope process by intentional design converts the information content of the policy process into something AI processes can understand: objective numerical data that aligns policy risk measurement with quantitative risk measurement. PolicyScope data is thus the crucial missing piece providing context to global macro datasets at a point when it matters most.
Increased reliance on language-derived data for use within generative AI contributes additional and necessary contextual insight to augment global macro analytical processes. For example:
AI processes applied to earnings call transcripts, press conference transcripts, and news broadcasts deliver additional depth that can extend global macro portfolio analysis.
The patented PolicyScope notional volume measurements illuminate daily, sometimes incremental, shifts in policy volatility that can trigger market reaction functions and create the foundation for analyzing policy and market behavioral dynamics using a common quantitative language;
PolicyScope structured language data in .json format provides superior inputs for automated research assistants for firms seeking to achieve increased accuracy, constrain hallucination risk, and improve ROI with the first and only dataset designed for machine readers.
The PolicyScope process contributes important volume-based data and signals drawn directly from the public policy process. Global macro investing at its core involves paying attention to what policymakers are saying and doing so that solid portfolio decisions can be made. In addition, we are addressing context and comparability in global macro historical data by scoring and storing public policy language going back to 2006 (twenty years).
For the first time, markets can measure with precision market reaction functions related to public policy shifts separate from the news cycle. They can identify inflection points and opportunities to increase efficient market pricing by anticipating headline risk proactively.
Advanced technology brings within reach a new way of detecting tradeable signals from the public policy process, helping to offset some of the persistent shortcomings associated with traditional global macro datafeeds.
BCMstrategy, Inc. uses award-winning patented technology to generate data from the public policy process for use in a broad range of AI-powered processes from predictive analytics to automated research assistants. The company automatically generates multivariate, tickerized time series data (notional volumes) and related signals from the language of public policy. The company also automatically labels and saves official sector language for use in generative AI, deploying expert-crafted ontologies. Current datafeeds cover the following thematical verticals:
