Information latency appears when markets do not instantly adjust to new data. Traders may take time to notice an update, verify a report, interpret its meaning, or gain confidence to trade on it. During this window, the market probability may lag behind reality, creating a visible delay in prediction markets data.
On platforms like Polymarket, Kalshi, Myriad, and Manifold, information latency often shows up around breaking news, data releases, or complex updates that require interpretation. Early movers may react immediately, while others join later, causing the probability to adjust in waves instead of a single jump. These staggered reactions highlight how information flows unevenly through the trading community.
Latency can result from technical limitations, slow information discovery, cautious traders, or simple inattention. Understanding it helps analysts distinguish genuine forecasting inefficiencies from normal market processing time.
Information latency affects how accurately prediction markets reflect real-world developments. Recognizing latency helps analysts interpret probability movements and understand how quickly markets absorb new information.
It occurs because traders receive, digest, and act on information at different speeds. Some watch events closely; others react only after confirmation. Limited liquidity or low participation can also slow the adjustment process. These variations create delays that become visible in prediction markets data when the market probability trails the actual information landscape.
High latency can temporarily distort forecasts, making probabilities appear stale or inconsistent with current realities. Skilled traders often exploit these moments by correcting mispriced markets. Once enough participants respond, the probability realigns with the new information. This process leaves a signature in prediction markets data that analysts can study to understand market efficiency.
Analysts can measure how fast markets react to different types of events—such as economic releases, political developments, or corporate announcements. They can identify which markets consistently lag and whether latency improves with higher liquidity or more engaged traders. These insights help refine forecasting models and deepen understanding of how prediction markets data evolves.
On Polymarket, a market forecasting the approval of a major policy update showed a noticeable delay after a key statement from government officials was published midday. A few traders reacted quickly, but the broader market adjusted gradually over the next hour—demonstrating clear information latency as participants absorbed the news at different speeds.
Evaluating information latency requires granular, time-stamped probability data to track how quickly markets adjust after new signals appear. FinFeed's Prediction Markets API provides the detailed prediction markets data needed to measure reaction times, study market responsiveness, and build tools that detect lag across different event types.
