IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2508.03217.html
   My bibliography  Save this paper

Measuring DEX Efficiency and The Effect of an Enhanced Routing Method on Both DEX Efficiency and Stakeholders' Benefits

Author

Listed:
  • Yu Zhang
  • Claudio J. Tessone

Abstract

The efficiency of decentralized exchanges (DEXs) and the influence of token routing algorithms on market performance and stakeholder outcomes remain underexplored. This paper introduces the concept of Standardized Total Arbitrage Profit (STAP), computed via convex optimization, as a systematic measure of DEX efficiency. We prove that executing the trade order maximizing STAP and reintegrating the resulting transaction fees eliminates all arbitrage opportunities-both cyclic arbitrage within DEXs and between DEXs and centralized exchanges (CEXs). In a fully efficient DEX (i.e., STAP = 0), the monetary value of target tokens received must not exceed that of the source tokens, regardless of the routing algorithm. Any violation indicates arbitrage potential, making STAP a reliable metric for arbitrage detection. Using a token graph comprising 11 tokens and 18 liquidity pools based on Uniswap V2 data, we observe a decline in DEX efficiency between June 21 and November 8, 2024. Simulations comparing two routing algorithms-Yu Zhang et al.'s line-graph-based method and the depth-first search (DFS) algorithm-show that employing more profitable routing improves DEX efficiency and trader returns over time. Moreover, while total value locked (TVL) remains stable with the line-graph method, it increases under the DFS algorithm, indicating greater aggregate benefits for liquidity providers.

Suggested Citation

  • Yu Zhang & Claudio J. Tessone, 2025. "Measuring DEX Efficiency and The Effect of an Enhanced Routing Method on Both DEX Efficiency and Stakeholders' Benefits," Papers 2508.03217, arXiv.org.
    • Handle: RePEc:arx:papers:2508.03217
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2508.03217
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu Zhang & Yafei Li & Claudio Tessone, 2025. "A Line Graph-Based Framework for Identifying Optimal Routing Paths in Decentralized Exchanges," Papers 2504.15809, arXiv.org.
    2. Jan Arvid Berg & Robin Fritsch & Lioba Heimbach & Roger Wattenhofer, 2022. "An Empirical Study of Market Inefficiencies in Uniswap and SushiSwap," Papers 2203.07774, arXiv.org, revised May 2022.
    3. Sornette, Didier & Zhang, Yu, 2025. "Transaction flows and holding time scaling laws of bitcoin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 658(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu Zhang & Tao Yan & Jianhong Lin & Benjamin Kraner & Claudio Tessone, 2024. "An Improved Algorithm to Identify More Arbitrage Opportunities on Decentralized Exchanges," Papers 2406.16573, arXiv.org.
    2. Deborah Miori & Mihai Cucuringu, 2022. "DeFi: data-driven characterisation of Uniswap v3 ecosystem & an ideal crypto law for liquidity pools," Papers 2301.13009, arXiv.org, revised Jan 2023.
    3. Lioba Heimbach & Eric Schertenleib & Roger Wattenhofer, 2022. "Exploring Price Accuracy on Uniswap V3 in Times of Distress," Papers 2208.09642, arXiv.org, revised Nov 2022.
    4. Yu Zhang & Zichen Li & Tao Yan & Qianyu Liu & Nicolo Vallarano & Claudio Tessone, 2024. "Profit Maximization In Arbitrage Loops," Papers 2406.16600, arXiv.org.
    5. Lucas Mussoi Almeida & Fernanda Maria Müller & Marcelo Scherer Perlin, 2025. "Risk Forecasting Comparisons in Decentralized Finance: An Approach in Constant Product Market Makers," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 395-428, January.
    6. Deborah Miori & Mihai Cucuringu, 2024. "Clustering Uniswap v3 traders from their activity on multiple liquidity pools, via novel graph embeddings," Digital Finance, Springer, vol. 6(1), pages 113-143, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2508.03217. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.