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Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest

Authors Fei Wu , Danning Sui , Thomas Thiery , Mallesh Pai

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ACM Subject Classification
  • Applied computing → Electronic commerce
Keywords
  • Decentralized Finance
  • Maximal Extractable Value
  • CEX-DEX arbitrages

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Abstract

This paper provides a comprehensive empirical analysis of the economics and dynamics behind arbitrages between centralized and decentralized exchanges (CEX-DEX) on Ethereum. We refine heuristics to identify arbitrage transactions from on-chain data and introduce a robust empirical framework to estimate arbitrage revenue without knowing traders' actual behaviors on CEX. Leveraging an extensive dataset spanning 19 months from August 2023 to March 2025, we estimate a total of 233.8M USD extracted by 19 major CEX-DEX searchers from 7,203,560 identified CEX-DEX arbitrages. Our analysis reveals increasing centralization trends as three searchers captured three-quarters of both volume and extracted value. We also demonstrate that searchers' profitability is tied to their integration level with block builders and uncover exclusive searcher-builder relationships and their market impact. Finally, we correct the previously underestimated profitability of block builders who vertically integrate with a searcher. These insights illuminate the darkest corner of the MEV landscape and highlight the critical implications for Ethereum’s decentralization.

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Fei Wu, Danning Sui, Thomas Thiery, and Mallesh Pai. Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 26:1-26:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.AFT.2025.26

Author Details

Fei Wu
  • King’s College London, UK
Danning Sui
  • Flashbots, San Francisco, CA, USA
Thomas Thiery
  • Ethereum Foundation, Lisbon, Portugal
Mallesh Pai
  • Rice University, Houston, TX, USA
  • Tempo Labs, Houston, TX, USA
  • Paradigm, Houston, TX, USA

Funding

The work is supported by The Latest in DeFi Research (TLDR) Fellowship 2024 funded by Uniswap Foundation.

Acknowledgements

We appreciate Gaussian Process, Kevin Pang, Bill Zhang, Tom Zhao, Romain Butteaud, Burak Öz and Max Resnick for for helpful discussions and comments. We also thank Alexander Tesfamichael from Ultra Sound Money and Žan Knafelc from Eden Network for their help with data collection.

Supplementary Materials

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