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A Study of MEV Extraction Techniques on a First-Come-First-Served Blockchain

Burak Öz, Filip Rezabek, Jonas Gebele, Felix Hoops, Florian Matthes

TL;DR

The study investigates MEV extraction on Algorand, an FCFS blockchain, to assess whether transaction-ordering techniques from fee-based blockchains apply. By building a large on-chain dataset and applying heuristics to detect arbitrages and batch issuance, the authors show network-state backruns dominate MEV with a uniform block-position distribution and no clear latency advantage between searchers and proposers. They identify substantial BTI activity and a few top MEV actors, with one leader accounting for a large share of arbitrages and profits, and they propose a novel congestion-based strategy to push the network toward fee-based ordering and enable frontrunning techniques. The findings highlight distinct extraction dynamics on FCFS networks, raise concerns about network usability under congestion, and point to future work on latency experiments and more robust detection methods across different blockchain architectures.

Abstract

Maximal Extractable Value (MEV) has become a significant incentive on blockchain networks, referring to the value captured through the manipulation of transaction execution order and strategic issuance of profit-generation transactions. We argue that transaction ordering techniques used for MEV extraction in blockchains where fees can influence the execution order do not directly apply to blockchains where the order is determined based on transactions' arrival times. Such blockchains' First-Come-First-Served (FCFS) nature can yield different optimization strategies for entities seeking MEV, known as searchers, requiring further study. This paper explores the applicability of MEV extraction techniques observed on Ethereum, a fee-based blockchain, to Algorand, an FCFS blockchain. Our results show the prevalence of arbitrage MEV getting extracted through backruns on pending transactions in the network, uniformly distributed to block positions. However, on-chain data do not reveal latency optimizations between specific MEV searchers and Algorand block proposers. We also study network clogging attacks and argue how searchers can exploit them as a viable ordering technique for MEV extraction in FCFS networks.

A Study of MEV Extraction Techniques on a First-Come-First-Served Blockchain

TL;DR

The study investigates MEV extraction on Algorand, an FCFS blockchain, to assess whether transaction-ordering techniques from fee-based blockchains apply. By building a large on-chain dataset and applying heuristics to detect arbitrages and batch issuance, the authors show network-state backruns dominate MEV with a uniform block-position distribution and no clear latency advantage between searchers and proposers. They identify substantial BTI activity and a few top MEV actors, with one leader accounting for a large share of arbitrages and profits, and they propose a novel congestion-based strategy to push the network toward fee-based ordering and enable frontrunning techniques. The findings highlight distinct extraction dynamics on FCFS networks, raise concerns about network usability under congestion, and point to future work on latency experiments and more robust detection methods across different blockchain architectures.

Abstract

Maximal Extractable Value (MEV) has become a significant incentive on blockchain networks, referring to the value captured through the manipulation of transaction execution order and strategic issuance of profit-generation transactions. We argue that transaction ordering techniques used for MEV extraction in blockchains where fees can influence the execution order do not directly apply to blockchains where the order is determined based on transactions' arrival times. Such blockchains' First-Come-First-Served (FCFS) nature can yield different optimization strategies for entities seeking MEV, known as searchers, requiring further study. This paper explores the applicability of MEV extraction techniques observed on Ethereum, a fee-based blockchain, to Algorand, an FCFS blockchain. Our results show the prevalence of arbitrage MEV getting extracted through backruns on pending transactions in the network, uniformly distributed to block positions. However, on-chain data do not reveal latency optimizations between specific MEV searchers and Algorand block proposers. We also study network clogging attacks and argue how searchers can exploit them as a viable ordering technique for MEV extraction in FCFS networks.
Paper Structure (25 sections, 4 figures, 7 tables)

This paper contains 25 sections, 4 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Related Work
  4. Applicability of Transaction Ordering Techniques
  5. Data Collection and Processing
  6. Detecting Arbitrages
  7. Detecting Batch Transaction Issuance
  8. Validation and Limitations
  9. Analysis
  10. Overview
  11. MEV Searchers
  12. Top Players
  13. Arbitrage Strategies
  14. Transaction Positioning
  15. Latency Games
  16. ...and 10 more sections

Figures (4)

  • Figure 1: Monthly arbitrage counts and profits by top searchers from 02-2022 to 06-2023.
  • Figure 2: Distribution of arbitrages across position octiles. The green bars reflect the cumulative profit in USD from arbitrages in each octile, while the blue bars represent the total count of arbitrages per octile.
  • Figure 3: Timeline of the number of arbitrages (orange plot), profits in USD (blue bars), and profits in ALGO (green bars) from 02-2022 to 06-2023.
  • Figure 4: Arbitrage execution types over time by MEV searcher AACC versus the rest of the network.