Scenario markets let traders forecast outcomes that depend on a particular combination of circumstances—such as an election result plus a policy decision, or an economic indicator hitting a target after a related announcement. This allows markets to reflect complex real-world situations where multiple variables interact.
Platforms like Polymarket, Kalshi, Myriad, and Manifold often produce markets that behave like scenario markets, even when not labeled explicitly. These markets give forecasters a structured way to express beliefs about multi-step or conditional situations. Instead of guessing how separate markets relate, a scenario market brings everything together so traders can price a unified forecast. This creates prediction markets data that encodes layered expectations and cross-event dependencies.
Scenario markets are especially useful in politics, economics, crypto, and regulatory forecasting, where events rarely occur in isolation.
Scenario markets make forecasting more realistic by accounting for combinations of events. They generate prediction markets data that reflects correlations and dependencies, improving the accuracy and interpretability of forecasts.
They allow traders to price complex event chains directly. Instead of stitching together separate forecasts, scenario markets show how the crowd expects multiple parts of a situation to unfold together. This leads to clearer insights and richer prediction markets data, especially when event interactions matter more than individual outcomes.
By capturing dependencies, scenario markets reduce the guesswork involved in combining separate probabilities. They produce direct market-driven estimates of multi-step events. This prevents errors that occur when analysts assume independence or manually approximate correlations. The resulting prediction markets data is more aligned with real-world structures.
Analysts can observe how traders price interconnected developments, detect which conditions are viewed as pivotal, and identify scenario pathways the market sees as most plausible. They can also compare scenario pricing with individual markets to measure correlation strength. These insights deepen understanding of prediction markets data and help with scenario planning and risk assessment.
On Polymarket, analysts examine a scenario-style market where traders forecast the likelihood that a major regulatory decision will pass after a key political development. The market’s pricing reveals how closely participants link the two events and whether they consider one a prerequisite for the other.
Scenario markets require structured, high-resolution forecast data. FinFeed's Prediction Markets API provides detailed prediction markets data—probabilities, distributions, OHLCV and historical paths—that developers can use to model scenario outcomes, study dependencies, and build tools for multi-layer forecasting.
