The Economics of the Kalshi Prediction Market

Why It Matters

The bias and negative returns undermine the promise of prediction markets as reliable information aggregators, limiting their utility for policymakers and businesses seeking accurate forecasts.

Key Takeaways

• Kalshi’s prices exhibit favourite‑longshot bias across categories.
• Takers lose ~32% on average; makers lose ~10%.
• Average pre‑fee return on contracts is –20%.
• Accuracy improves as expiry approaches but remains imperfect.
• Regulatory clearance enabled larger stakes, yet market inefficiencies persist.

Pulse Analysis

Prediction markets have long been hailed for their ability to synthesize dispersed information, but regulatory hurdles kept U.S. platforms small until the CFTC granted designated contract market status to Kalshi in 2020. By allowing uncapped stake sizes and a transparent order‑book mechanism, Kalshi expanded the scope of tradable events, from political outcomes to macro‑economic releases. This regulatory shift attracted a broader participant base, creating the conditions for higher liquidity and more frequent price updates, which are essential for any market that aims to reflect collective expectations.

The empirical study of Kalshi’s contract data reveals a persistent favourite‑longshot bias: low‑priced contracts win less often than their quoted probabilities suggest, while high‑priced contracts slightly exceed expectations. This distortion translates into stark asymmetries between market roles—takers, who accept existing offers, incur average losses of roughly 32%, whereas makers, who set prices, lose about 10%. Even before fees, the market’s aggregate return is negative, indicating that participants are, on average, overpaying for perceived upside. Such inefficiencies challenge the notion that prediction markets automatically produce unbiased probability forecasts.

For businesses and policymakers, these findings signal caution. While Kalshi’s platform offers a richer data source than traditional polls, the observed biases mean that raw contract prices should be adjusted before being used for strategic decisions. Future research may focus on designing incentive structures that mitigate optimism bias among takers or incorporating calibrated probability distortions into pricing models. As regulatory environments continue to evolve, the next generation of prediction markets must address these inefficiencies to fulfill their promise as robust decision‑support tools.