How Kalshi Can Help the Federal Reserve

Why It Matters

Prediction markets give the Fed near‑real‑time sentiment on key macro variables, potentially sharpening inflation and growth forecasts and improving policy timing. This could usher in a more data‑rich, responsive approach to central banking.

Key Takeaways

• Kalshi and Polymarket hit $50 bn trading volume in 2025.
• Economic bets now represent 1‑2% of total market activity.
• Federal Reserve exploring prediction markets for real‑time data.
• Market forecasts could complement traditional economic indicators.
• Peer‑to‑peer platforms may influence future central‑bank tools.

Pulse Analysis

Prediction markets such as Kalshi and Polymarket have moved beyond niche betting platforms to become multi‑billion‑dollar venues for crowd‑sourced forecasts. In 2025, combined trading volume topped $50 bn, a more than three‑fold jump from the $16 bn recorded the year before. While sports wagers still dominate, a growing slice of activity—about one to two percent—targets macroeconomic variables like quarterly GDP, non‑farm payrolls and CPI expectations. Transparent order books let analysts trace sentiment shifts in real time. This shift reflects both sophisticated traders seeking hedges and a broader public appetite for quantifying economic outcomes.

The Federal Reserve’s recent outreach to these platforms signals a willingness to experiment with alternative data streams. By aggregating the collective expectations of thousands of participants, prediction markets can generate real‑time probability distributions that often anticipate official statistics. Such forward‑looking signals could sharpen the Fed’s inflation and growth forecasts, reducing reliance on lagging surveys and improving the timing of policy moves. Pilot studies abroad show market‑derived inflation expectations can precede official data by weeks. However, the Fed must navigate issues of market manipulation, sample bias, and regulatory oversight before integrating these signals into formal decision‑making frameworks.

If the Fed successfully pilots prediction‑market inputs, it could pave the way for a new class of monetary‑policy tools that blend traditional econometrics with crowd intelligence. Other central banks may follow, fostering an ecosystem where regulated prediction venues serve as public‑good data providers. Moreover, the presence of a trusted central‑bank participant could legitimize the markets, attracting higher‑quality participants and deeper liquidity. Regulators will need to balance openness with safeguards against manipulation. In the long run, this convergence may reshape how policymakers gauge economic sentiment, offering a more dynamic and transparent view of the economy’s trajectory.