From Narratives to Numbers: Measuring Belief with Hivemind
Hivemind is our trend identification and company exposure engine. It measures discussion to discover market relevant trends and scores trend impact, with direction and magnitude, for every company in your universe. All processes and outputs are customizable and point-in-time. Built for institutional quants.
Market-moving themes like GenAI, geopolitical conflict, inflation, and tariffs are poorly captured by risk and alpha models.
If you ran a large book through 2025's volatility, you felt it. You'd be forgiven for forgetting that the same forces that have always driven markets and valuations remain intact: expectations of future cash flows.
Anyone who has built a discounted cash flow (DCF) model would know that intimately. Assuming a typical discount rate, the majority of implied company value is based on assumptions and narratives about what the world will look like in 4-20 years.
And those narratives/expectations, about what the world will look like in 4-20 years, have been changing faster than ever before. Unfortunately, systematically quantifying point-in-time changes in market expectations, and the direction and magnitude of companies impacted, is impossible… or is it?
More on that below RE: ForecastOS Hivemind. It's certainly been impossible until recently.
Given the challenge of measuring changes in aggregate long-term expectations (which comprise the majority of implied company value), investors have focused on shorter-term sources of return, including:
- Event-driven forecasting; fundamental, macro, and other outcomes over the next 0-2 years
- Market abnormalities; exploited on aggregate via short-horizon strategies
- Structural and thematic analysis; trade imbalance, regime change, etc.
- Fundamental analysis; better understanding of business models or near-term headwinds/tailwinds and inflection points
But none of the above measure shifts in longer term expectations/narratives, which have driven recent volatility and returns. Most of what drives prices remains poorly modelled by institutional investors, or not modelled at all.
Fortunately, thanks to recent advances in data availability, embedding models, and GenAI, you can now measure shifts in market narratives and reason through which companies are positively and negatively impacted using forward-looking perceived causality, not backward-looking correlation.
We do that. We call our solution Hivemind.
Hivemind is our trend and exposure engine. It takes messy, unstructured information (filings, podcasts, any text/financial data - even your own proprietary inputs) and turns it into clean, point-in-time exposures/factors. And because Hivemind is built to track narratives, it also continuously surfaces and ranks market-relevant trends, both daily...
and grouped across time...
... so you can see what’s emerging, what’s fading, and what’s quietly becoming a persistent factor.
Describe a concept once and Hivemind will score every company in your universe with direction and magnitude, at each point-in-time - so you can separate helped by vs hurt by, how much, and when.
Hivemind doesn't use a static taxonomy. You can tune the “recipe,” swap datasets in/out, and dial the scoring logic up or down (the “knobs”), then export it in the result schema you want - via UI or API - in minutes.
We built Hivemind specifically for institutional workflows. It’s already been used in the wild for neutralizing portfolio themes (e.g. GenAI, inflation, tariffs) against a benchmark, applying event-driven thematic exposures, and more.
Charlie Reese
But don't just take our word for it. See what Jonathan Briggs, CIO of hedge fund Tc43 and ex-Head of the Alpha Generation Lab at CPPIB, has to say:
"Traditional risk models remain necessary but are no longer sufficient. Market narratives and investor behavior now generate disruptive price dynamics that conventional frameworks weren't designed to capture. Tracking sell-side thematic baskets - often just a handful of names whose relationships are statistically unstable and decay quickly - provides little value for systematic managers trading thousands of instruments across multiple asset classes. Hivemind bridges this gap. By transforming complex unstructured and structured data into dynamic, point-in-time thematic exposures - calibrated with investor insight and updated systematically - it gives quantitative investors the tools to manage risks that legacy models simply can't see. This is the first real step toward next-generation risk control."
To demonstrate one of many potential applications, check out the below Hivemind S&P 500 macro overlay for a long-only portfolio with:
- 2.5% excess return annualized
- 1.0x information ratio
- 3.4x one-way turnover
Needless to say, we're excited! To those we've spoken or worked with thus far: thank you. We're a small team, but we'll continue to do our best to get Hivemind into more of your hands over the coming months.
To learn more about Hivemind, email us at [email protected] to schedule a call. We'd love to show you what we've built and how you can integrate it into your investment process.
More on Hivemind:
Charlie Reese
Charlie Reese
Callum Baker