Resolution risk appears when traders are unsure whether a prediction market will settle cleanly based on its stated criteria. This can happen if the event’s definition is unclear, if data sources conflict, or if the platform delays confirming the result. Traders price this uncertainty into the market, which affects liquidity, confidence, and probability movements.
Platforms like Polymarket, Kalshi, Myriad, and Manifold all encounter resolution risk in different forms. It may arise when official data is released later than expected, when an event has multiple interpretations, or when users anticipate disputes. These conditions can create hesitation among traders, widen spreads, or slow down participation. The resulting prediction markets data often shows jittery movements or pauses around the expected resolution time.
Resolution risk matters because even a well-forecasted event can cause losses or confusion if the settlement process breaks down. Understanding this risk helps forecasters interpret probability behavior more accurately.
Resolution risk affects trader confidence, platform reliability, and the quality of prediction markets data. Addressing it leads to more transparent, trustworthy, and efficient forecasting systems.
Resolution risk appears when the rules for determining an outcome leave room for interpretation or depend on uncertain data sources. An event might rely on an announcement that gets delayed, or its criteria may be phrased vaguely. Traders then worry about whether the market will resolve as expected. This concern often becomes visible in prediction markets data as reduced liquidity or erratic price movements near resolution time.
When traders doubt the clarity or timing of a market’s resolution, probabilities may reflect both the likelihood of the event and the chance of misresolution. This can create unusual pricing patterns, including compressed probabilities, hesitation to trade, or late volatility. Analysts studying prediction markets data must account for the fact that part of the movement may be driven by resolution fears rather than event fundamentals.
Analysts can identify which types of markets consistently produce disputes or delays and evaluate how resolution uncertainty affects trader behavior. Studying these patterns helps refine resolution criteria, improve oracle processes, and strengthen platform trust. It also reveals where prediction markets data may contain noise due to non-event-related uncertainty.
On Kalshi, a market forecasting a specific government statistic showed unusual price swings on the day of expected resolution because the official release was delayed. Traders were unsure when the platform would receive the final data, causing temporary volatility driven by resolution risk rather than changes in event likelihood.
Monitoring resolution risk requires access to detailed outcome histories and probability data. FinFeed's Prediction Markets API provides structured prediction markets data—resolution timestamps, prices, OHLCV, and market metadata—that helps developers detect resolution-related uncertainty and build tools to analyze settlement behavior.
