Addressing Structural Breaks in Global Macro Historical Time Series Data
- BCMstrategy, Inc.
- Jan 29
- 3 min read
Neither financial markets nor government policies are static. They operate amid shifting parameters and technological advances. Election outcomes, regulatory shifts, monetary policy regimes, and market microstructure changes introduce structural breaks that alter both policy dynamics and market reaction functions.
Global macro portfolio managers understand well that significant paradigm shifts will cause some correlations to break down. For example, pre‑Volcker and post‑Volcker bond markets, pre‑ and post‑decimalization equity markets, and the rise of electronic trading all changed market behavior in ways that backtests cannot capture.
Market reaction functions in response to public policy shifts have accelerated exponentially since the 1980s. Automated trade execution now occurs at the speed of light in response to institutional news feeds read by computers. Automated sentiment analysis from social media commentary – data points not available to portfolio managers thirty years ago -- delivers additional context to trading decisions.
For macro investing strategies, the dilemma is acute: include old data and risk contaminating parameters with outdated regimes, or exclude old data and face small‑sample fragility.
The current geopoiltical context underscores the importance of applying effective structural break analysis—combining statistical tests with institutional knowledge—to identify which eras are out‑of‑sample for today’s risk management and which still provide robust indicators that support predictive analytics.
Rapid trade and investment realignments based on overt national security considerations continue to proliferate from Vietnam and India to Brussels, from the UK and Canada to China, and from the United States to a large number of countries. These moves coincide with a global race to secure access to critical minerals in order to power both the AI revolution and the energy transition. Few reference the WTO or the Bretton Woods institutions as they cut bilateral trade and investment deals.
Global macro portfolio managers seeking to make data-driven decisions in this context face a considerable quandry. No comparable period exists within existing historical data sets. Addressing current and ongoing structural breaks in global macro historical time series data will require new inputs that can provide forward-looking signals regarding potential future policy decisions. In addition, public policy risks materialize in verbal format while markets measure risk in quantitative format.
PolicyScope Data meets the moment with the first and only patented process to convert the information content of the policy process into a language that machine readers and AI processes can understand: volume-based measures of policy velocity, volatility, directionality and momentum. With tickerized, time series data going back to 2006, the award-winning, patented process complements traditional historical economic and financial time series data by illuminating blind spots and providing perspective on policymaker priorities that mov markets.
From equities to commodities, from sovereign fixed income to FX, policy-derived quantitative data consistently delivers advance notice of market price action even during the volatile 2024-2025 period. In the charts below, market prices appear in the blue areas; PolicyScope Data appears as the green/yellow spikes.
Global macro portfolio managers seeking to address breaks in traditional time series data triggered by geopolitics can thus turn to PolicyScope Data to deliver reliable signals of price action. Because the dataset updates twice daily, it also delivers immediate signals regarding potential future policy shifts that will impact trading markets.
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
