How Generative AI Helps Mitigate Tariff Policy Investment Risk
BCMstrategy, Inc.- Feb 5
- 8 min read
Yesterday's post (How To Spot Tariff Policy Trends) provided perspective on how PolicyScope quantitative data can help portfolio managers and advocates spot trade-related policy risks before they generate headlines using data generated by our patented process. Today's post focuses on how best to deploy PolicyScope data within Generative AI applications to achieve superior investment insights and spot alpha generation opportunities regarding tariff policy.
The Colombia, Canada, and Mexico tariff truce takes the issues off the front pages for the moment. Geopolitical realignment is real. It occurs more incrementally than you might think. The U.S. tariff policy will continue to generate ripple effects throughout the global economy for the foreseeable future with direct consequences for equity, fixed income, and FX markets. But this is a marathon, not a sprint. The time to configure your data, AI, and risk management systems is now.
The Portfolio Manager Challenge -- Time
Measuring risks from media inputs may provide perspective on near-immediate market reaction functions. But those seeking strategic alpha gains or strategic advocacy priorities require more nimble mechanisms. Their challenge – asynchronous policy reaction functions.
The small, quiet, strategically significant policy shifts often fail to generate media attention. Markets are efficient, but they are not immediately efficient; it takes time to connect the dots between a small policy shift and a market opportunity. Policy risks do not always align with (and often are asynchronous with, market movements. In addition, the time to forge a strategy often occurs during non-emergency periods when it looks like nothing is going on.
This is why we patented a data generation mechanism that is tuned precisely to the frequency and rhythm of the policy process. We quantified the policy process (and then tickerized it) in order to support alignment with existing capital market technical analysis and automated anomaly detection using any model and any computer system.
Because our patented award-winning data generation mechanism is grounded in language, it is particularly well-suited for deployment in the generative AI context.
The PolicyScope Solution
Foundation Language Data for Foundation Language Models
You would not ask a high school student to sort out how geopolitical reaction functions work. It is true that if you give AI enough language data sooner or later it will sort out correlation coefficients for nearly any combination of words you could request. But that learning process takes time and it is not cheap.
Generative AI may augment knowledge workers, but the obverse is also true. Subject matter experts augment and make more efficient the generative AI compute-intensive learning process.
The measurement metric matters as well.
AI anticipates risks based on historical data. But history tells us that geopolitical advantage accrues to asymmetric actors. Merely feeding a machine a great deal of language and hoping for the best using chain-of-thought or other advanced automated reasoning tools may be superior to slower human-based processes, but that does not mean it is the optimal way to use generative AI.
You have to know what question to ask. And you have to know the right metric for measuring risk. Public policy risks come from momentum and volatility, which is what we measure daily, objectively, and automatically. In particular, we generate foundation language data regarding a wide range of trade policy issues. The outcome: generative AI helps mitigate tariff policy investment risks.
One of the many advantages associated with PolicyScope data is a deep knowledge base and training data compiled by people who know public policy processes from the inside out and the top down. The patented process detects both the verbal and nonverbal signals (not emotion!) policymakers send use when they talk to the media, to their constituents, and to each other. The outcome: more realistic scenario analysis powered by the subtle and hard-to-find signals policymakers send daily.
Here is how we can help you and your generative AI agents make sense of tariff-related geopolitical chaos.
Generative AI Helps Mitigate Tariff Policy Investment Risk
How to use PolicyScope Data in Generative AI to Achieve
Superior Analytics, Scenario Analysis & Equity Research
Step 1: Choose your issues and countries
We support three layers of inquiry:
high level themes (Climate/Energy, Trade & Economics, Digital Currency),
targeted topics (e.g., Trade Flows, Carbon Emissions Reduction, Stablecoins, GDP Growth), and
granular keywords…including of course tariffs.
All the data is tickerized to the Russell 3000 so you can immediately compare market data against policy data for any given day in order to identify optimal trading and strategic opportunities. Customization is possible.
Extensive objective language structuring using our patented process delivers significant precision across both entity and semantic searches.
Step 2: Identify Relationships
Financial markets were some of the first to understand that Generative AI solutions augment and enhance the human analytical process rather than replace it. Automated research assistants are now here to stay.
Because our patented process structures the data across multiple vectors based on expert instruction, your LLMs have a fast track (less compute cost!) to identifying relationships and crafting risk scenarios that matter to you. The quantitative data provides grounding to the language model, accelerating its capacity to identify context and deliver answers accurately. Your research assistant thinks and writes like a senior government official.
Firms deploying PolicyScope language data within their own LLMs must abide by specific instructions and limitations that we use internally in order to maximize precision and minimize data leakage. In particular: LLM analysis must be limited to the retrieved content, they must cite their sources for written answers, and they must disclose if they do not have an answer to a specific question.
Step 3: Deploy Familiar Metrics & Anomaly Detection Tools
By design, we structured a data set to support the kinds of questions risk analysts would ask about public policy shifts. This feature also facilitates more accurate automated summaries and report generation, further increasing the operational efficiency of the risk analysis and investment research process.
Generative AI can read charts and graphs and it can describe data. Delivering to Generative AI applications PolicyScope momentum data and signal data in addition to the language data accelerates analytical discovery regarding temporal trends and reaction functions that can create risk/reward opportunities in the capital markets.
PolicyScope Data was designed from the beginning to help portfolio managers measure public policy risks on a par with all existing market risk measurement tools. We do not need to construct new kinds of exposure risk matrices. Measuring momentum objectively, as with a stock market close data point, eliminates the need to create new measurement tools. Market participants instead apply existing, familiar quantitative risk measurement processes which decreases the learning curve for humans and increases operational efficiency.
For example, we have found MACD analysis aligned to internal portfolio parameters and investment approaches yield solid anomaly detection mechanisms. The MACD of Inflation public policy (below) can be compared with the MACD for tariff policy and the MACD for any tradeable instrument to identify correlations and covariances daily.
Our Momentum Index provides perspective on policy directionality and volatility as well. See this blog post for more details: Anomaly Detection in Public Policy Made Easy -- MACD Analysis
Step 4: Define Your Exposure
Every firm’s portfolio is structured differently. The exact same policy action (e.g., a tariff on lumber) can hit a company hard but if you anticipated the policy shift, your portfolio will either be hedged against that risk or it will be positioned to generate alpha from the market downdraft. The policy shift may be normatively undesirable or it may adversely impact a company’s bottom line, but that does NOT mean that the shift will adversely impact portfolio performance in a uniform manner across the market.
BCMstrategy, Inc. is committed to delivering objective data. Therefore, we do not provide estimates regarding which firms will be impacted by policy volatility, much less by how much. Instead, PolicyScope data delivers to firms maximum autonomy to define exposure precisely for the portfolio context and parameterization in which they operate. We will never ask for your proprietary settings or portfolio positions. We just give you the data to help you make better decisions about exposure to public policy risks. Tickerization delivers automated and immediate information for expressing public policy risks in relation to policy movements. It also accelerates the capacity to construct precise correlation matrices between policy shifts and market movements.
BCMstrategy, Inc. has also architected a dataframe that preserves the integrity of our inputs and, thus, minimizes client exposure to LLM hallucination. We do not generate synthetic data (see this post for more detail: AI Training Data 101 Guide: Synthetic Data). Nor do we ingest extraneous noise language from social media, the news cycle, or other commentary. PolicyScope data is thus pure signal even before we generate momentum-based signal data. Finally, we require that LLMs restrict their analysis and summarization only the retrieved content.
Step 5: Additional Data – Institutional News Feeds, Signal Data
Backtests show that PolicyScope data anticipates market volatility with a high degree of correlation for a simple reason: the sequence of events. Policymakers act. Journalists report. Markets react to the news.
Customers with institutional news feeds additionally can measure the delta and dynamics between the policy process and the news cycle in order to identify with precision when market reaction functions to headlines will occur. We align to your journalism inputs and respect all copyrights. If you have a data mining license, we can score your news feed.
Consequently, your generative AI automated research assistant can immediately compare what policymakers are actually saying and doing against what the news cycle is reporting. We literally know from experience as policymakers that significant deltas exist. Don't believe us? Consider the PolicyScope times series data for the last time that trade war dramas were front page news:
PolicyScope data shows you where the gaps might be so that you can measure it, Advanced generative AI can read the chart, describe it, and include it in a draft report for you.
The rapid acceleration of AI-powered analytics makes it possible to combine a broad range of data sources in new ways to acquire unique insights. We encourage our customers to think broadly about the potential pairings that could add context and dimension to the policy risk measurement process in a way that aligns with their investment parameters.
It has never been more important to measure, monitor and manage public policy risks. We are entering a volatile period of significant geoeconomic rebalancing that will take years to unfold. AI shines brightest and delivers the most value when you feed it the best data. We are pleased to partner with third parties to augment their data offerings and deliver that first slice of policy data that you cannot get anywhere else: award-winning, patented PolicyScope data.
If you want to learn more about PolicyScope Data or see a demo, get in touch!
BCMstrategy, Inc. generates AI training data from the language of public policy using award-winning, patented processes so that investors and advocates can make better data-driven decisions regarding policy trajectories.
