Forecast Change
In prediction markets, forecasts are expressed as probabilities that update as trading occurs. Forecast change measures how much those probabilities move between two points in time.
The change can be small or large, gradual or sudden. Its size reflects the strength of new information, trading pressure, or behavioral reaction. Forecast change does not explain cause on its own. The same change may come from genuine information, low liquidity, or concentrated trading activity. Timing matters. Early forecast changes often reflect uncertainty, while late changes usually signal confirmation, reversal, or overreaction.
For analysts, forecast change is a basic unit of prediction markets data. It enables tracking belief updates, reaction speed, and market sensitivity.
Across many events, patterns in forecast change reveal how efficiently markets absorb information and adjust expectations.
Why It Matters
Forecast change shows when and how beliefs evolve. It helps users distinguish meaningful updates from routine or noisy movement in prediction markets.
What causes forecast changes in prediction markets?
Forecast changes are caused by new information, trades, or shifts in sentiment. News releases, data updates, and large orders often trigger changes. In thin markets, even small trades can produce noticeable movement. Analysts examine liquidity and volume to understand the cause.
How is forecast change different from price movement in prediction markets?
Forecast change measures the amount of change between two points, while price movement describes the broader path over time. Change is a discrete comparison; movement is continuous behavior. Analysts use forecast change to quantify reactions. Both are complementary in prediction markets data analysis.
How is forecast change used in prediction markets data analysis?
Analysts use forecast change to detect reactions, reversals, and volatility. Repeated changes help identify persistence or instability. Comparing changes across events supports responsiveness and efficiency studies. This improves interpretation and model calibration.
Real-World Example
On Polymarket, an outcome moving from 0.40 to 0.55 after a major announcement represents a significant forecast change. Analysts check whether the change persists or reverses as more traders respond.
Forecast Change and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides time-stamped prediction markets data needed to calculate forecast change. Analysts can measure changes across custom intervals and align them with volume, liquidity, and event timelines. This supports reaction analysis, volatility tracking, and forecast evaluation. The API enables consistent forecast change analysis across prediction markets.