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Prediction markets are proving more accurate than Wall Street in forecasting inflation, according to a new study by U.S.-regulated platform Kalshi, raising questions for investors and policymakers about how economic expectations should be formed.
The findings, covering a 25-month period between early 2023 and mid-2025, suggest that prediction markets delivered significantly lower forecasting errors during times of economic uncertainty, when traditional consensus estimates struggled most.
Kalshi’s analysis compared inflation forecasts from its prediction markets with Wall Street consensus estimates ahead of monthly U.S. Consumer Price Index (CPI) releases. The study found that traders on prediction markets recorded a 40% lower average error rate than conventional forecasters over the period examined.
According to the report, the gap widened sharply when inflation data diverged meaningfully from expectations. In cases where CPI readings deviated strongly from forecasts, prediction markets outperformed consensus estimates by as much as 67%.
The findings were shared and detailed in a study titled “Crisis Alpha: When Do Prediction Markets Outperform Expert Consensus?”
The research also examined how disagreement between market-based and traditional forecasts correlates with the likelihood of surprises.
When Kalshi’s CPI estimate differed from Wall Street consensus by more than 0.1 percentage point one week before release, the probability of a significant inflation surprise rose to roughly 80%, compared with a 40% baseline.
The study attributes the stronger performance of prediction markets to how they aggregate information. Unlike institutional forecasts, which often rely on similar datasets and shared economic models, prediction markets pool views from a diverse set of traders with direct financial incentives to be accurate.
This structure creates what Kalshi describes as a “wisdom of the crowd” effect. Traders incorporate signals ranging from sector-specific trends to alternative datasets, allowing prediction markets to adjust more rapidly as conditions change. Prices on these platforms update continuously, reflecting new information in real time, rather than being fixed days ahead of official data releases.
The study notes that institutional forecasters face reputational and organizational constraints that may discourage bold deviations from consensus. In contrast, participants in prediction markets are rewarded or penalized purely based on performance, encouraging sharper reactions during periods of stress.
“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,” — Kalshi study, “Crisis Alpha.”
The findings arrive as prediction markets expand rapidly in scale and visibility. Kalshi’s user base has grown following its integration into the Phantom crypto wallet, and the company recently raised $1 billion at an $11 billion valuation. Separately, Polymarket has been reported to be in talks for funding at valuations as high as $15 billion, underscoring rising interest in market-based forecasting tools.
Previous independent research cited in the story suggests Polymarket achieved 90% accuracy in predicting events one month out, rising to 94% accuracy just hours before outcomes occur.
However, the article also notes persistent risks, including acquiescence bias, herd behavior and low liquidity, which can distort probabilities within prediction markets.
Despite these limitations, the Kalshi study argues that prediction market can serve as a valuable complement to traditional forecasting, particularly during periods of structural uncertainty when standard models may lag reality.
“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,” — Kalshi study.
