Forecast Bias
Forecast bias happens when errors are not random. Instead of sometimes being too high and sometimes too low, forecasts miss in the same direction over and over. This can come from psychology, incentives, market design, or repeated assumptions that go unchallenged.
In prediction markets, bias can appear as persistent optimism, pessimism, or slow adjustment to new information. Traders may anchor to early prices, overweight popular narratives, or underreact to unlikely but important risks. On platforms like Polymarket, Kalshi, Myriad, and Manifold, these patterns become visible in prediction markets data when similar markets resolve with the same type of error again and again.
Bias does not mean markets are useless. It means they have tendencies that can be measured, corrected, and learned from.
Why It Matters
Forecast bias affects trust and decision-making. Identifying bias helps analysts interpret prediction markets data more accurately and avoid false confidence.
What causes forecast bias in prediction markets?
Bias can come from anchoring, herding, base rate neglect, or uneven participation. Structural factors like low liquidity or poorly defined outcomes can also contribute. These influences shape how prediction markets data evolves before resolution.
How can forecast bias be detected?
Bias is detected by comparing predicted probabilities with actual outcomes across many markets. If markets repeatedly overestimate or underestimate similar events, a bias is present. This requires historical prediction markets data, not single examples.
Can forecast bias be reduced over time?
Yes. Awareness, better market design, and informed trading can reduce bias. As traders learn from past errors, probabilities often become better calibrated. This improvement shows up gradually in prediction markets data.
Real-World Example
An analyst notices that a series of regulatory markets on Kalshi consistently price approvals higher than what ultimately occurs. By measuring this pattern across many resolved markets, the analyst identifies an optimism bias in that category.
Forecast Bias and FinFeedAPI
Analyzing forecast bias requires large samples of resolved markets. FinFeed's Prediction Markets API provides structured prediction markets data that developers and analysts can use to detect bias, measure calibration, and adjust forecasting models.