How Market Traders Beat Wall Street on Inflation Forecasting

A comprehensive study by prediction market platform Kalshi demonstrates that independent traders operating within market-based systems consistently outperform Wall Street consensus in forecasting inflation. The findings challenge traditional assumptions about expert forecasting and reveal the competitive advantages of decentralized information aggregation, positioning traders and markets as increasingly valuable tools for institutional decision-makers navigating economic uncertainty.

Traders Deliver Superior Accuracy: The Data

Over a 25-month period from February 2023 through mid-2025, traders using Kalshi’s prediction market platform achieved a 40% lower average error rate compared to conventional Wall Street consensus estimates when forecasting year-over-year Consumer Price Index (CPI) changes. The accuracy advantage became even more pronounced during periods of significant economic volatility. When CPI readings deviated sharply from expectations, Kalshi’s traders outperformed consensus forecasts by as much as 67%, according to the study titled “Crisis Alpha: When Do Prediction Markets Outperform Expert Consensus?”

The research also uncovered a predictive indicator with potential policy implications: when traders’ CPI estimates diverged from consensus by more than 0.1 percentage point one week before data release, the probability of a significant deviation in actual CPI readings jumped to approximately 80%, compared with a 40% baseline when forecasts aligned closely.

The Traders’ Information Edge: Market-Based Aggregation

The superiority of traders’ forecasts stems from fundamentally different mechanisms for collecting and synthesizing information. Unlike traditional consensus estimates that often rely on standardized models and shared analytical frameworks across institutions, prediction market platforms like Kalshi and Polymarket pool forecasts from diverse traders who operate with direct financial incentives. Each trader brings different data sources, alternative methodologies, and specialized sector knowledge to their predictions.

This structure creates what researchers describe as a “wisdom of the crowd” effect—a natural information advantage that emerges when independent participants contribute their distinct perspectives toward a shared objective. Traditional forecasting systems, by contrast, can become homogenized, with multiple institutions unknowingly incorporating similar assumptions and methodologies, limiting their ability to detect emerging shifts in economic conditions.

Why Traders Adapt Faster Than Expert Consensus

Institutional forecasters operate under constraints that traders typically avoid. Professional analysts and economists face reputational risks and organizational politics that can discourage bold or contrarian predictions, even when available data suggests unconventional forecasts might be more accurate. In contrast, traders in prediction markets operate under pure performance-based incentives—they profit when correct and lose when wrong, creating an environment where accuracy trumps institutional conservatism.

Additionally, prediction market pricing updates continuously in real time, adjusting immediately as new information enters the ecosystem. Consensus estimates, by comparison, are typically finalized several days before official data releases, creating an inherent lag that can disadvantage traditional forecasters during rapidly changing economic environments.

Market Adoption Accelerates Amid Growing Recognition

The market for prediction-based forecasting is experiencing rapid institutional expansion. Kalshi raised $1 billion at an $11 billion valuation in December 2025, while Polymarket was reportedly exploring funding rounds at valuations reaching as high as $15 billion. The expansion of traders’ access to prediction markets has intensified following Kalshi’s integration into Phantom, a major cryptocurrency wallet with approximately 20 million users, signaling growing mainstream recognition of market-based forecasting capabilities.

Supporting this trend, independent research from earlier this year found that Polymarket traders achieved 90% accuracy in predicting major events one month in advance, and 94% accuracy in the hours immediately preceding actual events.

Market-Based Forecasting as Institutional Tool

While acknowledging that shock events remain inherently rare and difficult to predict consistently, Kalshi’s research authors emphasize that the data strongly suggests a role for traders and market-based systems as complementary components within broader institutional risk management and policy planning frameworks. The study notes: “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.”

Rather than wholesale replacement of established forecasting methodologies, institutional decision-makers may find that incorporating signals from prediction markets—where traders compete to predict outcomes accurately—provides particular value during periods of structural economic uncertainty, when traditional consensus approaches often struggle to capture emerging market dynamics.