A Federal Reserve–authored study finds Kalshi’s prediction market outperforms traditional methods in forecasting headline CPI inflation and predicting Federal Reserve rate decisions, with Kalshi posting a perfect forecast record on the day before Fed meetings and showing lower forecast errors than Fed Funds futures.

The research positions Kalshi as a high-quality macroForecast source, reflecting its data-driven value and potential utility for policymakers.

Kalshi market data have tracked Federal Reserve monetary decisions since 2022, outperforming economist surveys and futures in several metrics.

The article notes ongoing public scrutiny of prediction markets, including insider trading risks and gambling concerns as lawmakers debate regulation.

Investor interest in forecasting tools is rising, with major institutions like Goldman Sachs considering expansion into prediction markets, signaling potential broader adoption.

Prediction markets are portrayed as gaining data-driven legitimacy and moving toward mainstream economic relevance, even as regulatory and calibration questions persist.

Kalshi CEO Tarek Mansour welcomed the study as incredible, while other industry voices offered limited comment.

Researchers note that prediction markets react quickly to data and central-bank signals, providing high-frequency pricing as a supplementary benchmark for policymakers.

The findings suggest growing academic and policy support for regulated prediction markets as real-time complements to surveys and futures pricing in macro forecasting.

The regulatory landscape remains central, with the CFTC asserting authority over prediction markets and states weighing restrictions; regulators emphasize protecting jurisdiction and consumer safeguards.

Some researchers identify a favorite-longshot bias in Kalshi markets, indicating calibration is not perfect across politics, entertainment, and economics.

The CFTC’s jurisdiction over Kalshi and similar platforms continues to shape policymakers’ views on market-based forecasting tools.