Moral hazard arises when someone can profit from a prediction market while also having the ability—or incentive—to affect the real-world outcome. Instead of forecasting, the trader may act in ways that change the event itself, creating a conflict between honest prediction and personal gain. This can distort probabilities and weaken the reliability of the market.
On platforms like Polymarket, Kalshi, Myriad, and Manifold, moral hazard concerns show up in markets where participants have influence over policy decisions, organizational votes, or technical upgrades. For example, someone with inside authority or operational control might bet on a specific outcome and then take actions to make it happen. These dynamics appear in prediction markets data as sharp, sudden moves that don't align with public information.
Preventing moral hazard keeps markets focused on forecasting rather than manipulation, ensuring that probabilities reflect genuine expectations instead of strategic behavior.
Moral hazard can damage forecast accuracy and trust. When traders can influence outcomes they are betting on, prediction markets data becomes less reliable and may no longer represent true collective belief.
It appears when participants have both the ability to trade and the ability to affect the outcome—directly or indirectly. This creates incentives for strategic manipulation rather than honest forecasting. Such behavior can distort prediction markets data and undermine the purpose of the market.
It introduces signals that reflect strategic behavior instead of real probability. Markets may price outcomes based on what influential participants might do, rather than on external information. This reduces the clarity and integrity of prediction markets data, especially for governance- or decision-linked events.
Analysts can identify which markets are vulnerable because influential actors are involved, and they can track distortions that arise when insider incentives override traditional forecasting behavior. These insights help determine when prediction markets data must be interpreted cautiously.
A DAO running a market on whether a protocol change will pass notices unusual probability movements. A few large traders—who also have significant voting power—buy heavily into one outcome before promoting the change internally. The distorted pricing reflects moral hazard rather than organic forecasting.
Understanding moral hazard requires tracking how probabilities move relative to insider-controlled events. FinFeed's Prediction Markets API provides structured prediction markets data that analysts can use to identify suspicious patterns and measure the impact of strategic, non-informational behavior.
