According to a Kalshi prediction market study, prediction market traders always outperform professionals in predicting inflation, especially when data deviates greatly from estimates.
By comparing inflation forecasts on his platform with Wall Street consensus estimates, Kalshi found that market 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-on-year changes in the Consumer Price Index (CPI) showed an average error 40% lower than consensus forecasts between February 2023 and mid-2025, according to the study. The difference was more pronounced when the figure deviated sharply from expectations. In those cases, Kalshi’s forecasts exceeded consensus by up to 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 the consensus by more than 0.1 percentage point a week before its release, the chance of a significant deviation in the actual CPI reading rose to around 80%, compared with a base 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 major Phantom crypto wallet. The company raised $1 billion at a valuation of $11 billion earlier this month as bets on prediction markets continue to grow. In October, Polymarket was said to be in talks to raise funds at a valuation of up to $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 forecasts as part of broader policy and risk planning tools.
“Although 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 forecast environment becomes more challenging, the markets’ advantage in information aggregation becomes more valuable,” the study reads.
Earlier this year, research by a data scientist showed that Polymarket is 90% accurate in predicting how events will occur a month from now, and 94% accurate just hours before the actual event occurs. Still, acquiescence bias, herd mentality, and low liquidity can lead to overestimating the probabilities of events.
Why prediction markets outperform consensus in times of stress may be due to how they aggregate information. Traditional forecasts often rely on similar data and models across institutions, which can limit their ability to respond when economic conditions change, the study suggests.
Prediction market platforms, by contrast, reflect the opinions of a diverse set of traders based on a variety of inputs, from sector-specific trends to alternative data sets, creating what the study describes as a “wisdom of the crowd” effect.
Incentives also differ. Institutional forecasters face organizational and reputational limitations that can discourage bold predictions. However, prediction market traders have money at stake and are rewarded or penalized solely for their performance.
The continuous 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 is released.
“Rather than largely replacing traditional forecasting methods, institutional decision makers could consider incorporating market-based signals as complementary information sources with particular value during periods of structural uncertainty,” the study suggests.
