Prediction Markets Beat Wall Street on Inflation, Kalshi
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New research by Kalshi on prediction markets suggests that those platforms can outpace traditional Wall Street estimates when it comes to forecasting US inflation. Kalshi study finds prediction markets outperform Wall Street on inflation data According to a new study by Kalshi, prediction markets beat Wall Street consensus estimates on US inflation with a 40% lower average error over a 25-month period. Moreover, the analysis shows that these markets were particularly accurate when inflation deviated sharply from economists’ expectations. Comparing inflation forecasts on its platform with professional consensus, Kalshi found that market-based traders outperformed conventional economists and analysts throughout the 25 months reviewed. The performance edge was most visible during periods of heightened economic volatility, when traditional models tend to struggle. Market-based estimates of year-over-year changes in the Consumer Price Index (CPI) recorded a 40% lower average error than consensus forecasts between February 2023 and mid-2025, the study reports. However, the gap widened further when actual CPI readings diverged strongly from expectations, with Kalshi’s forecasts beating consensus by as much as 67% in those instances. Crisis Alpha and the value of disagreement The study, titled “Crisis Alpha: When Do Prediction Markets Outperform Expert Consensus?”, also examined how disagreement itself can signal upcoming surprises. Specifically, it looked at the relationship between the size of the gap between Kalshi’s CPI estimate and Wall Street consensus, and the likelihood of a shock. When Kalshi’s CPI estimate differed from the consensus by more than 0.1 percentage point one week before the official release, the probability of a significant deviation in the actual CPI reading rose to about 80%, versus a 40% baseline. That said, the paper cautions that the sample of large shocks over the period is still relatively small. The authors argue that this pattern highlights the potential for market-based forecasting to serve…
