Prediction Market Pricing Efficiency and Divergence: When Do Markets Fail?

Prediction market mispricing occurs due to four primary factors: (1) Liquidity constraints that prevent efficient price discovery, especially for low-probability events; (2) Behavioral biases including overconfidence, recency bias, and wishful thinking among traders; (3) Information cascades where early trades influence later participants regardless of private information; (4) Strategic manipulation in thin markets where coordinated trading can move prices. These factors create systematic divergence between market prices and true event probabilities.

Both Kalshi and Polymarket demonstrate strong calibration for high-liquidity events—when they predict 70% probability, outcomes occur approximately 70% of the time. However, accuracy degrades significantly for low-probability events (under 20%) and politically charged markets where partisan bias distorts prices. Kalshi, as a CFTC-regulated exchange, tends to have more conservative pricing, while Polymarket often shows higher volatility due to its crypto-native user base. Neither platform is consistently more accurate; their reliability depends on market liquidity and event type.

Yes, but with important caveats. Profitable opportunities exist when you can identify systematic biases—such as favorite-longshot bias, partisan overpricing, or liquidity-driven mispricing—before they correct. Successful strategies include: betting against extreme partisan sentiment, exploiting thin markets where prices deviate from poll aggregates, and arbitraging price differences between platforms. However, transaction costs, liquidity constraints, and the difficulty of consistently identifying mispricing make sustained profitability challenging for most traders.

The favorite-longshot bias is a well-documented phenomenon where prediction markets systematically overprice unlikely events (longshots) and underprice likely events (favorites). For example, an event with true 5% probability might trade at 8-10%, while an event with 90% true probability might trade at 85-87%. This bias creates opportunities for sophisticated traders to profit by betting on favorites and against longshots. The bias persists due to risk-seeking behavior among retail traders, the entertainment value of longshot bets, and limited arbitrage capital willing to tie up funds for small expected gains.

Prediction markets fail spectacularly when multiple failure modes compound: (1) Correlated beliefs—when most traders share the same information sources and biases, the "wisdom of crowds" breaks down; (2) Cascade dynamics—early mispricing attracts momentum traders who amplify the error; (3) Liquidity traps—correct prices cannot form when informed traders lack capital or market access; (4) Black swan blindness—markets systematically underweight unprecedented events. Notable failures include Brexit (markets showed 85% Remain probability on election day) and the 2016 US election, where correlated polling errors propagated through prediction markets.

Key divergence signals include: (1) Price-poll divergence—when market prices significantly deviate from polling aggregates or expert forecasts; (2) Liquidity anomalies—thin order books or unusual bid-ask spreads suggest unreliable prices; (3) Sentiment extremes—one-sided social media sentiment or partisan clustering indicates potential bias; (4) Cross-platform arbitrage—price differences between Kalshi, Polymarket, and PredictIt signal inefficiency; (5) Historical calibration failures—markets that have been poorly calibrated for similar past events deserve skepticism. Combining multiple signals improves detection accuracy.