[Paper Review] Don't Let MEV Slip: The Costs of Swapping on the Uniswap Protocol

Austin Adams, Benjamin Y Chan+2 more|arXiv (Cornell University)|Sep 24, 2023

Blockchain Technology Applications and SecurityComputer Science3 citations

TL;DR

This paper presents a comprehensive empirical analysis of transaction costs on Uniswap v3, introducing a novel decomposition of slippage into benign and adversarial components—particularly reordering slippage—using real trade data. It finds that for large trades, slippage dominates costs, with adversarial reordering increasing by 80% for memecoins like PEPE compared to stablecoins like USDC.

ABSTRACT

We present the first in-depth empirical characterization of the costs of trading on a decentralized exchange (DEX). Using quoted prices from the Uniswap Labs interface for two pools -- USDC-ETH (5bps) and PEPE-ETH (30bps) -- we evaluate the efficiency of trading on DEXs. Our main tool is slippage -- the difference between the realized execution price of a trade, and its quoted price -- which we breakdown into its benign and adversarial components. We also present an alternative way to quantify and identify slippage due to adversarial reordering of transactions, which we call reordering slippage, that does not require quoted prices or mempool data to calculate. We find that the composition of transaction costs varies tremendously with the trade's characteristics. Specifically, while for small swaps, gas costs dominate costs, for large swaps price-impact and slippage account for the majority of it. Moreover, when trading PEPE, a popular 'memecoin', the probability of adversarial slippage is about 80% higher than when trading a mature asset like USDC. Overall, our results provide preliminary evidence that DEXs offer a compelling trust-less alternative to centralized exchanges for trading digital assets.

Motivation & Objective

• To provide the first in-depth empirical characterization of total transaction costs on a decentralized exchange (DEX), specifically Uniswap v3.
• To decompose slippage into benign and adversarial components, with a focus on adversarial reordering as a major cost driver.
• To develop a new metric—reordering slippage—that identifies adversarial transaction reordering without requiring quoted prices or mempool data.
• To quantify how trade size, asset type (e.g., stablecoin vs. memecoin), gas prices, and market momentum affect transaction costs.
• To evaluate the trustworthiness of private RPCs and builders in the MEV ecosystem using observable transaction behavior.

Proposed method

• Uses real trade data from 534,198 swaps via the Uniswap Labs interface on two pools: USDC-ETH (5bps) and PEPE-ETH (30bps) from Jan–Aug 2023.
• Defines slippage as the difference between the realized execution price and the quoted price, decomposing it into price impact and adversarial reordering effects.
• Introduces reordering slippage as a novel, data-agnostic metric that detects adversarial reordering by analyzing execution price deviations without relying on mempool or quoted price data.
• Employs regression models to analyze the impact of trade size, gas price, market momentum, and pool characteristics on slippage, controlling for market variables.
• Uses public transaction data and private RPC data to compare behavior and assess trust in builders within the mev-boost ecosystem.
• Validates the reliability of private RPCs by comparing public and private transaction outcomes and assessing builder behavior via reordering slippage.

Figure 1 : Transaction Cost Composition For Different Transaction Types

Experimental results

Research questions

• RQ1 How do transaction costs on Uniswap v3 break down between gas fees, slippage, and liquidity provider fees?
• RQ2 To what extent is slippage driven by adversarial transaction reordering, especially for large trades and volatile assets?
• RQ3 How does the probability of adversarial reordering slippage differ between stablecoins (e.g., USDC) and memecoins (e.g., PEPE)?
• RQ4 Can reordering slippage be measured without access to quoted prices or mempool data, and how effective is it as a monitoring tool?
• RQ5 How trustworthy are private RPCs and block builders in the MEV ecosystem, based on observable transaction behavior?

Key findings

• For small trades, gas costs dominate transaction costs, while for large trades, slippage becomes the primary cost component.
• On average, transaction costs per dollar transacted are 22 bps on the USDC-ETH pool and 140 bps on the PEPE-ETH pool.
• Adversarial reordering slippage is 80% higher when trading PEPE compared to USDC, indicating significantly greater MEV exposure for speculative assets.
• Larger trade sizes significantly increase adversarial slippage, with most of the effect attributable to strategic reordering by block proposers and searchers.
• Higher gas prices and market momentum exacerbate slippage, likely due to increased competition and transaction collisions.
• No conclusive evidence of misbehavior among the top five builders in the mev-boost ecosystem, suggesting strong incentives or penalties may deter malicious behavior.

(b) WETH-USDC Swaps