Kalshi Research: Prediction Market "Inflation Prediction Accuracy" Greatly Surpasses Wall Street Analysts

Kalshi today announced the establishment of a research department and published a report indicating that market traders not only surpassed Wall Street analysts in predicting US inflation data but also demonstrated a 50% error advantage during severe market fluctuations, signaling that the prediction market is moving towards mainstream and institutional adoption.

Kalshi Research is established, and the prediction market enters the academic and corporate landscape.

Kalshi announced the establishment of a new academic research department, Kalshi Research, similar to the research departments of OpenAI and Anthropic, which will provide global researchers with the largest and highest quality prediction market data and platform data.

At the same time, Kalshi will hold its first prediction market conference, aimed at bringing together researchers, forecasters, traders, and community members to showcase the latest findings of prediction markets in economic models, behavioral finance, and market structure.

The officials stated that scholars from institutions such as Harvard, Stanford, Yale, and the University of Chicago have signed a cooperation agreement with Kalshi. Previously, partners such as Phantom, Coinbase, CNN, Google Finance, and Grok AI have successively become collaborators through front-end integration or data integration.

Kalshi Research: Wall Street's Prediction on “Inflation Data” Loses to Prediction Market

Kalshi Research's first internal study publicly compared the CPI forecast errors over 25 months, showing that Kalshi's average forecast error is 40% lower than the Wall Street consensus, and the predictions from a week ago were even 85% the same or more accurate than the consensus:

The prices in the prediction market are continuously updated before an event occurs, reflecting the real market sentiment and information absorption rate, giving it a natural advantage in data with highly sensitive signals like inflation.

At the same time, in the case of a “shock” where inflation deviates significantly from expectations, the accuracy advantage of the prediction market is even more evident.

When Kalshi's CPI prediction deviates from Wall Street by more than 0.1 percentage points, the probability of a significant actual deviation is as high as 80%, much higher than the historical benchmark of 40%. More importantly, during these high uncertainty periods, Kalshi's average error is 50% lower than that of professional analysts.

Three traders outperform one institutional analyst? “Crowd wisdom” is the key.

The team pointed out that, unlike institutional analysts who rely on specific models, the prediction market is composed of thousands of traders motivated by money, who place bets from different angles based on the industry information, data, economic signals, and more that they possess, resulting in a faster aggregation of information: “This kind of 'crowd wisdom with financial incentives' is the key to being ahead.”

(Is it a gamble or the truth? Polymarket prediction market is accused of being manipulated, and the “UMA whale” has triggered a crisis of trust)

In addition, Wall Street analysts often have reputations and institutional pressures that prevent them from making overly aggressive estimates; on the other hand, market traders consider profit as their only concern and will make real-time adjustments when data changes, making the results closer to reality.

Prediction market as an institutional-level decision support tool

With Kalshi completing its fundraising this month at a valuation of $11 billion and Polymarket reportedly reaching a valuation of $15 billion, the prediction market is attracting unprecedented amounts of capital, research, and user growth.

The research concludes that prediction markets will not replace traditional models, but will instead become an important supplementary tool for policymakers, risk teams, and asset management institutions during periods of high uncertainty. They can play a role in various scenarios, from inflation and elections to geopolitical events.

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