Do backrun auctions protect traders?

ArXiv ID: 2401.08302 “View on arXiv”

Authors: Unknown

Abstract

We study a new “laminated” queueing model for orders on batched trading venues such as decentralised exchanges. The model aims to capture and generalise transaction queueing infrastructure that has arisen to organise MEV activity on public blockchains such as Ethereum, providing convenient channels for sophisticated agents to extract value by acting on end-user order flow by performing arbitrage and related HFT activities. In our model, market orders are interspersed with orders created by arbitrageurs that under idealised conditions reset the marginal price to a global equilibrium between each trade, improving predictability of execution for liquidity traders. If an arbitrageur has a chance to land multiple opportunities in a row, he may attempt to manipulate the execution price of the intervening market order by a probabilistic blind sandwiching strategy. To study how bad this manipulation can get, we introduce and bound a price manipulation coefficient that measures the deviation from global equilibrium of local pricing quoted by a rational arbitrageur. We exhibit cases in which this coefficient is well approximated by a “zeta value’ with interpretable and empirically measurable parameters.

Keywords: Laminated Queueing Model, MEV Extraction, Blind Sandwiching, Price Manipulation Coefficient, Decentralised Exchanges, Crypto

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Lab Rats
  • Why: The paper presents a formal queueing model with probabilistic analysis and a derived ‘zeta value’ for price manipulation, indicating high mathematical complexity. However, it lacks backtesting, datasets, or code, focusing on theoretical bounds and conceptual models rather than implementation-heavy empirical validation.
  flowchart TD
    A["Research Goal<br>Do backrun auctions protect traders?<br>Assess price manipulation in laminated queues"] --> B["Key Methodology<br>Introduce Laminated Queueing Model<br>Arbitrageurs reset price to equilibrium<br>Market orders interspersed"]
    B --> C["Data/Inputs<br>Batched trading venues<br>Decentralized exchanges<br>MEV activity & order flow data"]
    C --> D["Computational Processes<br>Define Blind Sandwiching Strategy<br>Bound Price Manipulation Coefficient<br>Approximate with Zeta Value"]
    D --> E["Key Findings/Outcomes<br>Manipulation bounded by 'zeta value'<br>Arbitrageurs can manipulate local prices<br>Improves liquidity trader predictability"]