According to a study by Kalshi Prediction Market, prediction market traders consistently beat professionals when it comes to forecasting inflation, especially when the numbers deviate further from estimates.
By 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 an average error 40% lower than consensus forecasts between February 2023 and mid-2025, the study found. The difference was even more pronounced when the figure deviated significantly from expectations. In these cases, Kalshi’s forecast outperformed the consensus by as much as 67%.
The study, titled “Crisis Alpha: When Do Prediction Markets Outperform Expert Consensus?” ”, also examined the relationship between the extent of forecast disagreement and the likelihood of a surprise.
When Kalshi’s CPI estimate differed from the consensus by more than 0.1 percentage points a week before its release, the probability of a significant deviation in the actual CPI reading rose to about 80%, compared to a baseline of 40%.
Unlike traditional forecasts, which often reflect a shared set of models and assumptions, prediction markets like Kalshi and Polymarket aggregate forecasts from individual traders with financial incentives to accurately predict outcomes.
Kalshi’s user base has recently grown with the integration of the prediction market into the leading Phantom crypto wallet. The company raised $1 billion at an $11 billion valuation earlier this month, as bets on prediction markets continue to grow. In October, Polymarket was reportedly in talks to raise funds at a valuation of up to $15 billion.
The report’s authors note that while the sample of major shocks is relatively small, the data suggests a potential role for market-based forecasts as part of broader risk and policy planning tools.
“Even if the sample size of shocks is small (as it should be in a world where they are largely unexpected), the trend is clear: when the forecasting environment becomes most difficult, the advantage of aggregating market information 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 play out a month later, and 94% accurate just hours before the actual event occurs. Yet acquiescence bias, herd mentality, and low liquidity can lead to overestimation of event probabilities.
Why prediction markets outperform consensus during times of stress may depend on how they aggregate information. Traditional forecasts often rely on similar data and models across institutions, which can limit their responsiveness when economic conditions change, the study suggests.
In contrast, prediction market platforms reflect the views of a diverse set of traders drawing on a range of inputs, from sector-specific trends to alternative data sets, creating what the study describes as a “wisdom of the crowd” effect.
The incentives also differ. Institutional forecasters face reputational and organizational constraints that can discourage bold forecasts. However, traders in prediction markets have money on the line and are rewarded or penalized based solely on their performance.
The rolling nature of market prices, which are updated in real time, also avoids the lag inherent in consensus estimates, which are typically set several days before data release.
“Rather than completely replacing traditional forecasting methods, institutional decision-makers might consider incorporating market-based signals as sources of complementary information that are particularly useful during times of structural uncertainty,” the study suggests.
