Key facts
- The Roosevelt Institute claims retail traders have lost $583.5 million on Kalshi since its 2018 launch.
- The think tank alleges users are unknowingly betting against professional traders with sophisticated methods.
- Kalshi disputes the study's findings, calling them based on a misunderstanding of financial exchange operations.
- Kalshi argues the study miscategorizes trades, counting institutional market maker activity as ordinary user trades.
- The think tank's report suggests a lack of transparency regarding who users are betting against on the platform.
Kalshi is engaged in a dispute with the Roosevelt Institute, a think tank that claims users of the prediction market platform have lost nearly $600 million. The think tank's study, co-authored by Brad Lipton, suggests that regular users are unknowingly betting against professional traders employing sophisticated strategies, leading to these significant losses since Kalshi's launch in 2018.
Kalshi has strongly refuted these claims, asserting that the study demonstrates a fundamental misunderstanding of how financial exchanges operate. The company stated that there is no 'hidden house' on its platform and that it functions by matching orders, similar to other exchanges. Kalshi argues that the study incorrectly equates a skill gap among users with a difference in market structure and misinterprets trade data.
Lipton, however, maintains that everyday users are at a disadvantage due to a lack of transparency about who they are trading against. He expressed skepticism about the enforcement of Kalshi's rules against insider activity. The think tank's findings echo a Wall Street Journal investigation that also found a majority of users on prediction markets like Kalshi and its rival Polymarket lose money, with profits concentrated among a small number of accounts.
Kalshi has pushed back against similar criticisms previously, aiming to distinguish prediction markets from gambling. The company spokesperson told Business Insider that the Roosevelt Institute's calculation error involves counting high-frequency trades from institutional market makers as activity from 'ordinary users' and casual user trades as 'professional users.'