Measure what people collectively think and estimate how changes will impact investments. Customizable, AI-driven, and point-in-time.
Our narrative factor attempts to capture performance drivers similar to the momentum factor, but instead of chasing a symptom, it chases the root cause - growing market-relevant narratives driving changes in expectations and prices.
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 narratives drive equity returns yet remain largely absent from systematic factor models. This paper introduces a narrative factor built from ForecastOS Hivemind, which quantifies company-level exposures.
Now, more than ever, the stock market is driven by emergent themes. Let's explore how Hivemind both surfaces and creates exposures for point-in-time emergent trends.
Create a S&P 500 macro investment strategy in 100 lines of code. Can overlay on any US equities strategy. 2.5% annualized excess return, 1.0x information ratio, 3.4x one-way turnover.
If you ran a large book through 2025’s volatility, you probably spent more time on the phone than in the market. “Are you down? We’re off 3%.” Most of those calls were really a fishing expedition for: "what exposure did I miss?" and "does everyone have it, or just me?" The uncomfortable truth is that, outside of a handful of slow-moving style boxes, few managers can answer that question with conviction.
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.
1 import forecastos as fos
2
3 strategy = fos.portfolio.strategy.SPO(
4 actual_returns = actual_returns,
5 forecast_returns = forecast_returns
6 )
7 backtest_result = fos.portfolio.BacktestController(
8 strategy = strategy,
9 aum = 500_000_000
10 ).generate_positions()
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