yes bias

The systematic tendency for YES shares in prediction markets to be overpriced relative to true probabilities, potentially explaining what was previously attributed to favorite-longshot bias.

Cluster: Information Theory

Related Concepts

Articles about yes bias

Concepts/yes bias

yes bias

Information Theory

The systematic tendency for YES shares in prediction markets to be overpriced relative to true probabilities, potentially explaining what was previously attributed to favorite-longshot bias.

Referenced in 3 articles

Articles

The Yes Bias Might Not Exist
functionSPACE·Mar 27, 2026·II·Microstructure

Analyzes 7,292 resolved Polymarket markets and 28,793 on-chain trades to test whether yes bias exists in trade data. Finds that traders buy whichever token is cheaper, not whichever is labeled YES, and that the apparent bias is a compound effect of longshot preference channeled through Polymarket's "Will X happen?" question framing, which systematically assigns the longshot to the YES token.

How Wise Is the Crowd? Bias and Edge in Prediction Markets
Avaneesh Deleep, John Lee, Jenny Bai, Dhruv Suresh, Harsh Dhawan·Feb 28, 2026·III·Microstructure

Analyzes tick-level order flow across Polymarket and Kalshi to decompose market bias by trader type. Finds that the classic favorite-longshot bias may be a statistical artifact masking a pervasive "yes bias" driven by temporal volatility and incomplete controls for contract lifecycle. Also shows that whales are not the sharpest participants: heavily capitalized traders systematically bleed expected value to small-order traders, likely driven by ideological conviction rather than informational edge.

The Microstructure of Wealth Transfer in Prediction Markets
Jonathan Becker·Jan 18, 2026·III·Microstructure

Analyzes 72.1 million trades ($18.26B volume) on Kalshi and documents a systematic wealth transfer from takers to makers averaging 1.12% excess returns on each side. Takers disproportionately buy YES longshots, accepting returns 64 percentage points lower than equivalent NO positions. Shows this transfer only emerged after Kalshi's October 2024 legal victory attracted professional algorithmic market makers, and that market efficiency varies sharply by category: finance markets are near-efficient while entertainment and media markets show gaps exceeding 7 percentage points.