Prediction market inefficiencies appear when prices drift away from realistic probabilities. This can happen because of low liquidity, slow reactions to news, emotional trading, or behavioral biases. When the market misprices an outcome, it creates an opportunity for traders who notice the gap early.
Harvesting inefficiencies means consistently spotting these gaps and acting on them. Traders buy undervalued outcomes or sell overvalued ones, expecting prices to move back toward fair value. On platforms like Polymarket, Kalshi, Myriad, and Manifold, these opportunities often emerge around breaking news, complex events, or markets with less participation. In prediction markets data, inefficiencies show up as stretched probabilities, delayed reactions, or repeated reversals.
Over time, this behavior improves market quality. As inefficiencies are harvested, prices become more accurate and better reflect collective knowledge.
Harvesting inefficiencies helps correct market prices and improve forecast accuracy. It plays a key role in producing high-quality prediction markets data.
They exist because markets are made up of humans with limited attention and imperfect information. Some traders react late, others overreact, and some markets lack enough liquidity. These conditions create temporary gaps between price and reality, which appear clearly in prediction markets data.
When informed traders act on mispricing, they push probabilities back toward fair value. This process filters out noise and reduces the impact of bias or speculation. As a result, prediction markets data becomes more calibrated and reliable over time.
Analysts can identify which markets are slow to react, which event types attract emotional trading, and where structural weaknesses exist. Patterns of repeated inefficiencies also reveal how information flows through markets. These insights help improve forecasting models built on prediction markets data.
A trader notices that a Polymarket regulatory market hasn’t adjusted after a clear official statement is released. By buying the undervalued outcome early, they profit as the probability quickly corrects once the broader market catches up.
Identifying inefficiencies requires detailed, time-stamped market data. FinFeed's Prediction Markets API provides structured prediction markets data- trades, orderbooks, OHLCV —that developers can use to detect mispricing, study corrections, and analyze how inefficiencies are harvested.
