Kalshi Prediction Markets Match or Beat Traditional Forecasting Tools For Macro Indicators, NBER Study Finds

Source: Slashdot

Study Overview

A new NBER working paper authored by researchers at the Federal Reserve, Northwestern’s Kellogg School, and Johns Hopkins examines the forecasting performance of Kalshi, the largest federally regulated prediction market in the United States (overseen by the CFTC). The paper compares Kalshi‑implied forecasts for major macroeconomic indicators with traditional tools such as the New York Fed’s Survey of Market Expectations and Bloomberg consensus forecasts.

Key Findings

• Federal Funds Rate

  • Kalshi’s modal forecast correctly predicted the federal funds rate on the day before every FOMC meeting since 2022.
  • Neither the NY Fed survey nor fed funds futures achieved this level of accuracy.

• Headline CPI Inflation

  • Kalshi’s median and mode forecasts delivered a statistically significant improvement over Bloomberg consensus estimates.

• Real‑Time Probability Distributions

  • Kalshi provides real‑time probability distributions for GDP growth, core CPI, unemployment, and payrolls—data not available from other financial markets.
  • The study shows that these distributions shift markedly in response to macro news; for example, positive CPI surprises moved the mean of the fed funds rate distribution four times more than negative surprises.

• Market Activity

  • Trading volumes on Kalshi have grown to nearly 100 million contracts for a single FOMC meeting, supported by liquidity providers such as Susquehanna, Citadel, and Two Sigma.

Implications

The findings suggest that prediction markets like Kalshi can match or outperform traditional forecasting tools for macroeconomic indicators, offering both higher accuracy and richer probabilistic information for policymakers, investors, and researchers.

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