A two-stage game model of probabilistic price manipulation in decentralized exchanges

This study investigates price manipulation in decentralized exchanges (DEXs) — blockchain-based, peer-to-peer trading platforms operating without central intermediaries — where manipulators reorder transactions to profit from artificial price changes. Prior research typically assumes that they always exploit the maximum price slippage, but this overlooks how uncertainty in transaction ordering affects overall profits. We develop a two-stage, game-theoretic model that incorporates unpredictable state transitions and strategic fee competition. Analyzing 4,810 swap transactions, we find that manipulators frequently choose smaller trading volumes rather than maximizing slippage, indicating a trade-off between transaction costs and profit uncertainty. A behavioral extension, which accounts for investors’ risk-seeking tendencies, further supports these findings. Overall, this work contributes to the literature on manipulative trading in decentralized markets and offers actionable insights for policymakers and platform designers aiming to enhance market fairness.