AI Training Data for Global Macro Economics: A Guide
- BCMstrategy, Inc.
- 7 days ago
- 3 min read
Updated: 19 hours ago
**** July 2025 Blog Series: Your Guide to Global Macro Data for AI Agents******
Economics Statistics -- Read the July 9 Blogpost
The second half of 2025 ushers in the next phase of global macro and geopolitical risks. During the first half of 2025, new governments around the world (not just the US!) put their team into place and began the process of shifting policy trajectories as their voters had requested. Headline risk, volatility, and uncertainty ensued.
The economic consequences of decisions made during 1H2025 will begin appearing during summer and winter in 2025. Those consequences will create amplifiers or limitations for all governments as they strive to implement the remainder of their agendas. Cross-border reaction functions and heightened investment risks are inevitable.
Decisions in one country will have consequences around the world politically as well as economically. Measuring reaction functions has never been so important for investors seeking to make solid data-driven capital allocation choices.
It will be a challenge for the first generation of enterprise AI models to keep up for three reasons:
Structural Challenges: Most AI is just a turbo-charged pattern matching mechanism with a predilection for mean reversions. AI models have not been trained on data that anticipated or even hinted at the kind of geopolitical repositioning underway right now, which will play out over the next few years.
Detection Challenges: The next phase of geopolitical shifts that impact global macro will be subtle and often technical. Large language models trained on kitchen sink inputs from around the world wide web will struggle to "read" the language data. Hallucination risks are real. They will increase given the mismatch between the training data and real world data.
Data Challenges: If you are not yet thinking about language as if it were data, the time has come to shift your perspective. Global macro risks present first in verbal format. Language-derived data is the only way to accelerate event detection and insight formation regarding policy trends as they emerge.
Global macro strategists and investors are going to need more, and better, data to feed their agentic AI. But most strategists are not also data scientists. They will lose valuable time and waste compute resources as they experiment and teach automated processes.
PolicyScope Data is here to help.
Our patented, award-winning process for measuring volume, velocity, and volatility in the public policy process, and our risk signals, provide the best for predictive analytics and generative AI regarding trade policy, energy policy, climate policy, digital currency policy, and monetary policy. But we are not the only data you can use to measure, manage, and profit from, geopolitical shifts. Pairing our data with a range of other types of data delivers exponential increases in actionable intelligence, as indicated in this blogpost from 2024.
During July, this blog will provide to global macro investors a guide to the different kinds of data they can acquire in order to increase the effectiveness, accuracy, and operational efficiency of their AI processes.
From trend projection and market correlation analysis to generative AI document summaries to highly stylized signal data, this blog series will help subject matter experts identify with greater precision exactly which data they need to support their analysis. Increased precision in communication with data scouts will increase the ability to source solid decision-intelligence data feeds that fit a portfolio thesis or trading strategy.
BCMstrategy, Inc. helps portfolio managers, advocates, and strategists anticipate and manage public policy risks by providing them with industry-defining AI training data. The company's award-winning, patented process delivers fully tickerized notional volume data from the public policy cycle as well as signals based on volatility and momentum. Training data in .csv format supports policy trend projection and market correlation/trading AI processes. Training data in .json format is richly structured and supports generative AI deployments.
