Research

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.

Proposes economic measures for transaction ordering balancing price stability and inequality, introducing CLVR algorithm to approximate optimal ordering with minimal computation.

Introduces a permissionless market bootstrapping framework for Uniswap V4, featuring a Continuous Clearing Auction mechanism designed to balance price discovery with manipulation prevention.

Uses structural modeling to estimate how traders value blockchain security across Layer 2 networks, finding traders perceive 0.68% risk on Polygon and 3.29% on Optimism compared to Layer 1.

Analyzes how default slippage settings impact DEX trader losses, demonstrating Uniswap's dynamic default reduced losses by approximately 54.7%.

Identifies blockchain, token pair, and pool-level factors predicting market spreads on Uniswap v3 through concentration and total value locked mechanisms.

Compares V2 model predictions against V3 data, finding 97.1% accuracy with less than 0.1% deviation in active pools.

Curates dataset of 50+ million Uniswap transactions across L1 and L2 networks, creating daily indices measuring adoption, scalability, and wealth distribution.

Examines gamified leaderboards in economics coursework, finding the treatment group submitted assignments significantly earlier than controls.

Studies correlation between college basketball game schedules and local sexual assault reports, finding away games correlate with 14% increase in reports.