Event Impact Modeling
Event impact modeling focuses on cause and effect. It asks how a particular announcement, decision, or signal is expected to move a market. Instead of looking only at final outcomes, it analyzes the size and direction of belief changes triggered by events.
In prediction markets, impact is revealed through price movement, speed of reaction, and follow-through. Some events cause sharp, lasting shifts. Others create brief noise that fades quickly. On platforms like Polymarket, Kalshi, Myriad, and Manifold, these patterns appear clearly in prediction markets data as spikes, reversals, or smooth adjustments after specific moments.
Good event impact models separate meaningful signals from background noise. They focus on repeatable patterns across similar events, not one-off reactions.
Why It Matters
Event impact modeling helps explain why markets move. It improves how prediction markets data is interpreted and used for forecasting and decision-making.
How do analysts model event impact using prediction markets?
They compare probability levels before and after an event and measure the size, speed, and persistence of the change. Repeating this across many similar events reveals typical impact ranges. Using prediction markets data ensures the model reflects real, incentive-backed reactions.
Why do some events have large impact while others do not?
Impact depends on surprise, credibility, and relevance. Expected or already-priced events move markets less, while unexpected or decisive events move them more. Prediction markets data helps distinguish true surprises from information the market had already absorbed.
How can event impact modeling improve forecasts?
It helps anticipate how future events might move probabilities. By understanding typical reactions, analysts can better judge whether a market move is reasonable or excessive. This leads to more accurate use of prediction markets data in planning and risk assessment.
Real-World Example
A regulatory market on Kalshi shows small moves after routine updates but a large, lasting jump after an official approval vote. By comparing similar past votes, analysts model the typical impact of such decisions on market probabilities.
Event Impact Modeling and FinFeedAPI
Event impact modeling requires precise before-and-after probability tracking. FinFeed's Prediction Markets API provides structured prediction markets data—time-stamped probabilities, historical data, and metadata—so developers and analysts can measure event-driven moves, compare impacts across events, and build reliable impact models.