Key Takeaways:
Kalshi has launched Kalshi Research, a new division opening its internal prediction market data to academics and policy researchers, modeled loosely on the open research arms of major AI labs.
The unit’s debut study claims that over a 25-month period, Kalshi traders forecasted U.S. CPI inflation with 40% lower average error than Wall Street consensus, a gap that widened during volatile months.
By partnering with institutions like Harvard, Stanford, and Yale, Kalshi aims to validate prediction markets as legitimate “crisis alpha” tools for central banks and economic policymakers.
Kalshi is opening up its internal prediction-market data to the academic world. The U.S.-regulated exchange has announced the formation of Kalshi Research, a dedicated initiative designed to give economists and data scientists access to what the company describes as the world’s largest regulated prediction-market dataset.
The “Crisis Alpha” Study: Beating the Consensus
To demonstrate the value of its data, Kalshi Research released its inaugural paper, titled “Crisis Alpha: When Do Prediction Markets Outperform Expert Consensus?” The study tracked U.S. Consumer Price Index (CPI) forecasts on the platform between February 2023 and mid-2025, comparing them against the median forecasts of traditional economists.
The results suggest a significant edge for the crowd:
Routine Accuracy: Over the 25-month sample, Kalshi’s CPI markets delivered an average forecasting error 40% lower than the Wall Street consensus one week prior to the data release.
Outperformance in Volatility: The performance gap widened during months when inflation deviated sharply from expectations. In these “surprise” prints, the prediction market’s mean absolute error was up to 67% lower than the expert consensus.
Consistency: In roughly 85% of the monthly reports, the market either matched or beat the professional consensus.
The study also identified a critical signal for traders: “disagreement.” When the spread between Kalshi’s implied CPI and the economist survey exceeded 0.1 percentage points, the probability of a significant CPI surprise nearly doubled.
Kalshi is opening up its internal prediction‑market data to the academic world
Structuring “OpenAI-Style” Access for Academics
Kalshi Research is structured as an internal research arm and data program. While not building LLMs, the unit is modeled loosely on the applied research divisions of labs like OpenAI or Anthropic, providing de-identified data, tooling, and documentation to outside experts.
The dataset reportedly covers more than $23 billion in traded volume across hundreds of markets, offering empirical evidence on how crowds process information in real-time. Researchers from top-tier institutions – including Harvard, Stanford, Yale, and the University of Chicago – have already expressed interest or begun collaborating with the program.
Key areas of study include market microstructure (how order books behave around news events), calibration (how well probabilities match realized outcomes), and information aggregation (how quickly markets price in new data compared to surveys).
From Betting Venue to Policy Tool
For Kalshi, this is a strategic pivot from pure retail trading to institutional utility. By formalizing its relationship with academia, the company is making a bid to have prediction markets treated as serious policy tools rather than gambling venues.
If CPI markets consistently lead consensus during volatile periods, the argument goes, central banks and policymakers should treat them as a vital input – alongside bond breakevens and survey data – when setting monetary policy. To further this narrative, Kalshi plans to host an annual Prediction Market Conference, inviting regulators and scholars to debate the data.
However, the initiative also serves a clear commercial purpose. If independent academic research confirms that Kalshi’s pricing is more accurate than Wall Street, it provides a powerful marketing hook for attracting institutional capital and sophisticated traders looking for an edge in macro markets.
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