Paper 2023/1281

Leveraging Machine Learning for Bidding Strategies in Miner Extractable Value (MEV) Auctions

Christoffer Raun, ETH Zurich
Benjamin Estermann, ETH Zurich
Liyi Zhou, Imperial College London
Kaihua Qin, Imperial College London
Roger Wattenhofer, ETH Zurich
Arthur Gervais, University College London
Ye Wang, University of Macau

The emergence of blockchain technologies as central components of financial frameworks has amplified the extraction of market inefficiencies, such as arbitrage, through Miner Extractable Value (MEV) from Decentralized Finance smart contracts. Exploiting these opportunities often requires fee payment to miners and validators, colloquially termed as bribes. The recent development of centralized MEV relayers has led to these payments shifting from the public transaction pool to private channels, with the objective of mitigating information leakage and curtailing execution risk. This transition instigates highly competitive first-price auctions for MEV. However, effective bidding strategies for these auctions remain unclear. This paper examines the bidding behavior of MEV bots using Flashbots' private channels, shedding light on the opaque dynamics of these auctions. We gather and analyze transaction data for the entire operational period of Flashbots, providing an extensive view of the current Ethereum MEV extraction landscape. Additionally, we engineer machine learning models that forecast winning bids whilst increasing profitability, capitalizing on our comprehensive transaction data analysis. Given our unique status as an adaptive entity, the findings reveal that our machine learning models can secure victory in more than 50% of Flashbots auctions, consequently yielding superior returns in comparison to current bidding strategies in arbitrage MEV auctions. Furthermore, the study highlights the relative advantages of adaptive constant bidding strategies in sandwich MEV auctions.

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Publication info
Miner Extractable ValueDecentralized FinanceOnline Auction
Contact author(s)
christoffer raun @ inf ethz ch
estermann @ ethz ch
liyi zhou @ imperial ac uk
kaihua qin @ imperial ac uk
wattenhofer @ ethz ch
a gervais @ ucl ac uk
yewang ethz @ gmail com
2023-08-28: approved
2023-08-25: received
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Creative Commons Attribution-NonCommercial


      author = {Christoffer Raun and Benjamin Estermann and Liyi Zhou and Kaihua Qin and Roger Wattenhofer and Arthur Gervais and Ye Wang},
      title = {Leveraging Machine Learning for Bidding Strategies in Miner Extractable Value (MEV) Auctions},
      howpublished = {Cryptology ePrint Archive, Paper 2023/1281},
      year = {2023},
      note = {\url{}},
      url = {}
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