Prediction markets beat Wall Street in forecasting inflation, Kalshi says

• A study by Kalshi found that prediction markets outperformed Wall Street consensus estimates in forecasting inflation, with a 40% lower average error over 25 months.
• Kalshi's markets aggregate information from diverse traders with financial incentives, creating a "wisdom of the crowd" effect that's more responsive to changing conditions.
• The findings suggest that market-based forecasting can be a valuable complementary tool for institutional decision-makers, especially during periods of uncertainty.

Prediction market traders consistently beat professionals in forecasting inflation, especially when the readings deviate from estimates by a greater amount, according to a study by prediction market Kalshi.

Comparing inflation forecasts on its platform with Wall Street consensus estimates, Kalshi found that market-based traders were more accurate than conventional economists and analysts over a 25-month period, particularly during periods of economic volatility, according to a report shared with CoinDesk.

Market-based estimates of year-over-year changes in the Consumer Price Index (CPI) showed a 40% lower average error than consensus forecasts between February 2023 and mid-2025, the study found. The difference was more pronounced when the figure deviated sharply from expectations. In those cases, Kalshi’s forecasts outperformed consensus by as much as 67%.

The study, called “Crisis Alpha: When Do Prediction Markets Outperform Expert Consensus?,” also examined the relationship between the size of forecast disagreement and the likelihood of a surprise.

When Kalshi’s CPI estimate differed from consensus by more than 0.1 percentage point one week before release, the chance of a significant deviation in the actual CPI reading rose to about 80%, compared with a 40% baseline.

Unlike traditional forecasting, which often reflects a shared set of models and assumptions, prediction markets like Kalshi and Polymarket aggregate forecasts from individual traders with financial incentives to predict outcomes accurately.

Kalshi’s user base has recently grown with the integration of the prediction market into major crypto wallet Phantom. The company raised $1 billion at an $11 billion valuation earlier this month as bets on prediction markets keep growing. In October, Polymarket was said to be in talks to raise funds at a valuation as high as $15 billion.

The report’s authors note that while the sample of large shocks is relatively small, the data points to a potential role for market-based forecasting as part of broader risk and policy planning tools.

“Though the sample size of shocks is small (as it should be in a world where they are largely unexpected), the pattern is clear - when the forecasting environment becomes most challenging, the information aggregation advantage of markets becomes most valuable,” the study reads.

Earlier this year, research by a data scientist showed that Polymarket is 90% accurate in predicting how events will occur one month out, and 94% just hours before the actual event occurs. Still, acquiescence bias, herd mentality and low liquidity can lead to overestimated event probabilities.

Why prediction markets outperform consensus during times of stress may come down to how they aggregate information. Traditional forecasts often rely on similar data and models across institutions, which can limit their responsiveness when economic conditions shift, the study suggests.

Prediction market platforms, in contrast, reflects the views of a diverse set of traders drawing on a range of inputs, from sector-specific trends to alternative datasets, creating what the study describes as a “wisdom of the crowd” effect.

Incentives also differ. Institutional forecasters face reputational and organizational constraints that can discourage bold predictions. Traders on prediction markets, however, have money at stake and are rewarded or penalized purely on performance.

The continuous nature of market pricing, which updates in real time, also avoids the lag built into consensus estimates, which are typically fixed several days before data releases.

“Rather than wholesale replacement of traditional forecasting methods, institutional decision-makers might consider incorporating market-based signals as complementary information sources with particular value during periods of structural uncertainty,” the study suggests.

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