Prediction Markets Underperform Simple Baselines For Infectious Disease Forecasting

TL;DR

This study evaluates prediction markets for infectious disease forecasting and finds they underperform compared to simple benchmarks and established models, due to market inefficiencies and low trading volume.

Contribution

It provides the first systematic evaluation of prediction markets in infectious disease forecasting, highlighting their limitations and inefficiencies.

Findings

Markets do not outperform standard benchmarks in influenza and measles forecasting.

Market inefficiencies include probability mass placement on impossible outcomes.

Low trading volume contributes to market underperformance.

Abstract

Prediction markets (e.g., Polymarket, Kalshi) allow participants to bet on future events, producing real-time forecasts based on collective judgment. In domains such as elections and finance, markets have been effective at aggregating information, often rivaling or outperforming expert forecasters or polls. Whether this performance extends to infectious disease dynamics is unclear. Participants are self-selected and typically lack epidemiological expertise. However, markets can respond in real time to emerging news and unstructured signals in ways that standard forecasting pipelines cannot. Also, substantial financial stakes encourage participants to make an effort to be accurate. We evaluate Polymarket forecasts during 2025 and 2026 for two settings: weekly cumulative influenza hospitalizations in the US, which have an established expert-curated forecasting ensemble (CDC FluSight), and monthly measles cases, which do not. Across both settings, prediction markets fail to outperform standard benchmarks. For influenza, markets are competitive with low-performing individual FluSight models but are dominated by the FluSight ensemble: even when we combine market forecasts with the ensemble, the best combination puts zero weight on the markets. For measles, markets are outperformed by simple statistical baselines. We diagnose two sources of market inefficiency: placement of probability mass on impossible outcomes (e.g., decreasing values in cumulative forecasts) and low trading volume. These results suggest that current prediction markets are not reliable forecasters of infectious disease dynamics on their own or useful as complementary features for existing forecasting systems.