The Global Macro Data Conundrum: Long History, Short Data
- BCMstrategy, Inc.
- Jan 29
- 5 min read
Amid great change, every portfolio manager becomes a little bit of a global macro expert. Significant geopolitical shifts will impact every asset class and portfolio, albeit with different levels of intensity. Initial forays into the field deliver quick results for a simple reason: historical context is both plentiful and easy to access. From generative AI research assistants to internet searches to in-house experts, best-selling authors, and think tanks, it would seem everyone has access to knowledge and an opinion about it.
Implementing a disciplined, data-driven approach is harder. It does not take long to discover the great global macro data conundrum: long history, short data. Just because facts regarding the great sweep of history are easily accessible does NOT mean that robust quantitative data exists for deployment in AI-powered quantitative finance models. This post explains why the mismatch occurs and recommends strategies for addressing them.
Global Macro Data Conundrum: Long History, Short Data
Analysts seeking to study interest‑rate cycles, inflation regimes, or commodity shocks across centuries confront the reality that available financial datasets limit inference. Robust financial and economic datasets are literally scarce commodities. Where longer series exist—archival bond yields, reconstructed GDP time series, or pre‑war inflation historical data—they often depend on methodologies that differ from modern standards. Those differences can render the data of limited utility even if it arrives in machine-readable format.
Even for major markets, robust cross‑asset historical data often begins in the 1970s, which coincides with the period during which quantitative finance and options pricing theory became stables of financial analysis. Useable historical economic and financial data from many emerging markets only begin in the 1990s, coinciding with the end of the Cold War and the great globalization that followed the collapse of the Soviet Union. In other words: getting the most out of traditional time series data requires a parallel extension providing perspective on the geopolitical drivers for the observed global macro economic and financial trends.
For more details, please see our 2025 guide to global macro economic data (AI Training Data for Global Macro Economics: A Guide) as well as our favorite official sector time series data (AI Training Data for Global Macro Economics: 5 Key Statistic Series).
Finding historical language data for use within Generative AI is more daunting. Most text-based historical data prior to 2001 literally exists only on paper or static images. The content pre-dates machine-readable .pdfs. This situation helps explain why generative AI deployments beyond retail and marketing use cases can be very disappointing: the training data for foundation language models skews to 21st century content which is machine-readable. Considerable gaps in input data (e,g, equity analyst reports, earnings call transcripts) constrain the available outputs.
If additionally your foundation models failed to train on the full scope of economic theory, analytical output could be deeply subpar. For example, asking an LLM to apply options pricing theory to a portfolio without having trained the system on published works by Myron Scholes and Fischer Black render the LLM literally incapable of delivery a robust answer.
The trade‑off is constant and real: depth versus reliability. Long history builds intuition, but anchoring portfolio construction to reconstructed or low‑quality inputs risks false precision that may not deliver meaningful insight into potential future activity and decisions. Turning to AI to generate synthetic data creates its own risks, as we noted in this blogpost: AI Training Data 101 Guide: Synthetic Data.
The Reaction Function Complication
Current geopolitical re-alignments intensify the challenge.
Policymakers prioritizing national security make decisions without regard to traditional frameworks that prioritized cross-border and multilateral economic engagement. This was true in Europe during the Euro Area sovereign bond crisis, it was true globally in the early 1970s when the first Bretton Woods system collapsed, and it is true today as policymakers race to craft new bilateral trade and investment deals. The utility and predictive value of historical economic and financial data decays dramatically as policymakers and markets enter into new reaction function paradigms.
Markets also evolve their reaction functions, rendering previous financial time series data of limited utility at least with respect to velocity considerations. Capital markets have always reacted to the news cycle; sophisticated market participants have always been able to anticipate policy decisions and market impacts by hiring experts. But asset market correlations and pricing velocity today are very different from the 1970s. Sophisticated market participants deploy machine readers and agentic AI systems to rebalance portfolios at the speed of light in response to institutional news feeds. Markets have more access to a wider range of hedging strategies.
Finally, and most importantly, economic and financial time series data is profoundly backward-looking. When the foundation for economic decisions shifts, forward-looking portfolio analysis requires at a minimum a mechanism to augment or supplement traditional analysis with data inputs that more closely reflect current conditions.
The Alternative Data Solution --
Language-derived Quantitative and Structured Text Data
PolicyScope data uniquely helps global macro investors separate the signal from the noise during this period of global geopolitical transition. Our award-winning, patented technology brings within reach a new way to detect and measure tradeable signals from the language of public policy, helping to offset some of the persistent shortcomings associated with traditional global macro datafeeds.
The patented measurement process illuminates verbal signals hiding in plain sight, but which may have been obscured by a noisy news cycle. Pairing daily from the policy process with daily market data facilitates more dynamic data-driven decisions that move with the flow of the public policy process while aligning with traditional quantitative analysis.
Consider as an example the advance notice of price action associated with one policy topic (Natural Gas) in both US Treasury fixed income markets (ETF: GOVI) and non-EU sovereign fixed income markets: during 2024 and 2025:
It will take a few years for the geopolitical system to reach a new post-Bretton Woods equilibrium. During that period, global macro portfolio managers will need additional data streams to provide perspective on near-term policy shifts as they occur. PolicyScope Data is your plug-and-play solution, with .tickerized csv feeds twice daily and an historical data archive that measures policy volatility starting in January 2006.
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
