Paper: arXiv 2404.05803

Abstract

This paper presents the results of a comprehensive empirical study of losses to arbitrageurs (following the formalization of loss-versus-rebalancing by [“Milionis et al., 2022”]) incurred by liquidity providers on automated market makers (AMMs). We show that those losses exceed the fees earned by liquidity providers across many of the largest AMM liquidity pools (on Uniswap). Remarkably, we also find that the Uniswap v2 pools are more profitable for passive LPs than their Uniswap v3 counterparts. We also investigate how arbitrage losses change with block times. As expected, arbitrage losses decrease when block production is faster. However, the rate of the decline varies significantly across different trading pairs. For instance, when comparing 100ms block times to Ethereum’s current 12-second block times, the decrease in losses to arbitrageurs ranges between 20% to 70%, depending on the specific trading pair.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper builds upon a formal mathematical framework (Loss-versus-Rebalancing/LVR) requiring discrete-time modeling and numerical simulation, indicating substantial math, while its reliance on extensive historical trade data, Binance price feeds, and simulation of liquidity positions across multiple pools makes it highly data- and implementation-heavy.

Research Flowchart

  flowchart TD
  A["Research Question:<br>Do AMM liquidity providers<br>earn net positive returns?"]
  
  B["Data Collection<br>Uniswap v2/v3 Pool Historical Data<br>Block timestamps & Prices"]
  
  C["Computational Process<br>Calculate Loss-versus-Rebalancing<br>& Compare to LP Fees"]
  
  D{"Analysis Scenarios<br>Arbitrage Loss vs. Block Time<br>Current vs. Faster Block Production"}
  
  E["Key Findings<br>1. Arbitrage losses > Fees<br>2. v2 more profitable than v3<br>3. Faster blocks reduce losses 20-70%"]
  
  A --> B
  B --> C
  C --> D
  D --> E

Cited References (20)

  1. Automated Market Making and Loss-Versus-Rebalancing (2022) — link
  2. Automated Market Making and Arbitrage Profits in the Presence of Fees (2023) — link
  3. Arbitrageurs’ profits, LVR, and sandwich attacks: batch trading as an AMM design response (2023) — link
  4. Batching Trades on Automated Market Makers (2023) — link
  5. Liquidity Provider Returns in Geometric Mean Markets (2021)
  6. The replicating portfolio of a constant product market (2020)
  7. The replicating portfolio of a constant product market with bounded liquidity (2021)
  8. Risks and Returns of Uniswap V3 Liquidity Providers (2023) — link
and 12 more
  1. Behavior of Liquidity Providers in Decentralized Exchanges (2021)
  2. Concentrated Liquidity in Automated Market Makers (2021) — link
  3. Impermanent Loss in Uniswap v3 (2021) — link
  4. Uniswap: A good deal for liquidity providers? (2019) — link
  5. Usage of Markout to Calculate LP Profitability in Uniswap V3 (2022) — link
  6. Uniswap v2 Core (2020)
  7. Uniswap v3 Core (2021)
  8. Let’s run on-chain decentralized exchanges the way we run prediction markets (2016) — link
  9. An Analysis of Uniswap markets (2021)
  10. Diamonds are forever, loss-versus-rebalancing is not (2022)
  11. MEV capturing AMM (McAMM) (2022) — link
  12. Decentralised Finance and Automated Market Making: Predictable Loss and Optimal Liquidity Provision (2023) — link

0 of 20 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.