Measuring Active and Passive Liquidity Provider Returns in Uniswap
Summary
The study examines how returns differ between liquidity providers who reposition capital and those who leave it in place. It introduces two ways to estimate passive performance: one identifies brief liquidity positions and separates their contribution from swap outcomes, while the other models a marginal provider that remains continuously within the active price range. These estimates are compared with pool-wide results across Uniswap versions and several blockchain networks.
The findings indicate that pool averages can conceal meaningful differences in who bears adverse trade selection. In broadly distributed pools, passive estimates are close to overall performance. Concentrated-liquidity pools show a larger shortfall for passive providers, especially on Ethereum; the authors suggest that faster reactions to trading flow may help active providers. Higher fee pools are generally more favorable to passive liquidity. The two estimation approaches mostly agree in direction, lending support to the pattern. The analysis is limited to the examined protocols, pools, and chains, and the reported associations do not establish why the gaps occur. The results also motivate closer consideration of fee design and how decentralized exchange quality is measured.
Key ideas
- Pool-level returns can mask differences between active and passive liquidity providers.
- Two complementary markout approaches estimate passive performance from trading and price data.
- Passive and aggregate outcomes are similar in pools with broadly distributed liquidity.
- Concentrated-liquidity pools show passive underperformance, with a wider gap on Ethereum.
- Higher fee pools tend to offer better outcomes for passive providers.
- The comparison across methods supports the direction of the findings, while remaining specific to the sampled settings.
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Full text
# Not All LPs Are Equal: The Active-Passive Gap in Automated Market Maker Liquidity Provision # Not All LPs Are Equal: The Active-Passive Gap in Automated Market Maker Liquidity Provision Liquidity provision in automated market makers is typically analyzed at the pool level, implicitly assuming LP homogeneity. This aggregate view can hide how liquidity provision outcomes differ between LP strategies, particularly as concentrated liquidity AMM designs operating on high-performance blockchains allow liquidity to be actively repositioned around trades. We develop a markout-based framework to decompose Uniswap LP profitability into active and passive components using two complementary methods: a LIFO subtraction method that matches short-lived mint-burn positions and attributes swap-level markouts by liquidity share; and an infinitesimal LP benchmark that estimates the performance of a fully passive, always-in-range marginal LP directly from the AMM price path. We apply these methods to Uniswap v2, v3, and v4 pools on Ethereum, Arbitrum, and Base chains, and find passive profitability can materially differ from aggregate pool profitability.In Uniswap v2, with liquidity distributed evenly and active LP behavior nearly absent, the overall and passive markouts are almost the same. In contrast, concentrated-liquidity pools have a systematic active-passive gap: passive LPs tend to underperform aggregate pool-level measures. The gap is wider on Ethereum than on L2s, consistent with active liquidity provision being more useful when block times and ordering conditions allow LPs to react to incoming flow. In general, passive LPs perform better on higher fee pools. The LIFO and infinitesimal estimates are generally consistent directionally across most pools, providing evidence of the robustness of the decomposition. The results suggest that adverse selection in AMMs is not evenly distributed among LPs, with important implications for LP strategy, fee-tier design and measurement of DEX market quality.
Shown in full with attribution under the source's licence. Licence: abstract CC0
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