A mispricing signal appears when market prices diverge from what informed analysis, available data, or event fundamentals would suggest. This often happens when traders are slow to react to new information, overreact to noise, or fail to incorporate subtle developments. Skilled participants watch for these signals because they may indicate profitable opportunities or forecasting errors within the crowd.
On platforms like Polymarket, Kalshi, Myriad, and Manifold, mispricing signals show up in many forms—stagnant probabilities despite major updates, sudden moves without corresponding news, or probabilities that conflict with well-known benchmarks. These discrepancies become visible in prediction markets data, offering insights into how efficiently or inefficiently the market is aggregating information.
Mispricing signals help analysts understand where the crowd may be biased or delayed in processing information. They highlight moments when the market forecast may be temporarily incorrect until new trades push it back toward equilibrium.
Mispricing signals help traders and analysts spot forecasting inefficiencies. Recognizing them leads to better interpretation of prediction markets data and deeper understanding of how markets react to information.
They occur because not all traders act instantly or rationally. Some may miss important news, while others may trade based on outdated information or emotional reactions. Markets with low liquidity can also misprice outcomes temporarily. These moments create gaps between true event likelihood and market probability, producing mispricing signals within the prediction markets data.
When traders detect a mispricing signal, they may enter the market to correct it—buying undervalued outcomes or selling overvalued ones. This activity helps push the probability closer to its true value. Skilled forecasters often rely on such signals to identify opportunities, and their trades contribute to cleaner, more accurate prediction markets data.
Analysts can identify event types where markets systematically misprice outcomes, detect information delays, or measure how quickly markets correct errors. These patterns reveal strengths and weaknesses in information flow and forecasting accuracy. Mispricing signals also help distinguish between noise-driven fluctuations and meaningful probability adjustments within prediction markets data.
On Polymarket, a market forecasting a corporate lawsuit outcome stayed unusually flat even after a major court filing was released. Only a few traders reacted initially, creating a clear mispricing signal. As more participants read the filing, the market probability corrected sharply, illustrating how delays in information processing create temporary forecasting inefficiencies.
Detecting mispricing signals requires fine-grained probability and trading data. FinFeed's Prediction Markets API provides structured prediction markets data—price paths, OHLCV and outcome histories—that developers can use to identify inefficiencies, monitor corrections, and build models that detect mispricing in real time.
