Paper: arXiv 2611.02616

Authors: Yichong Bai, Dingyue Kan

Abstract

Most of the autocorrelation in trade signs on decentralized exchanges comes from pairs of trades sent by different wallets. For Uniswap v3, this has been read as herding dominating order splitting – the reverse of what equity markets show. We show that this herding share mixes two quantities: the probability $p$ that consecutive orders come from the same account, which is small when thousands of wallets are trading, and the ratio $κ$ of cross-account to same-account sign correlation. For any $p$, $κ$ equals one under the public-signal benchmark of Tóth et al. and zero under independent order splitting. On Uniswap v3 USDC-WETH (one week each in 2023 and 2024), the herding share is 0.65-0.86, but $κ$ is only 0.05-0.10. On a fully on-chain EVM limit order book, the herding share is similar (0.61-0.87), but $κ$ is 0.72-0.76; among Uniswap swaps above USD 100k, it is 0.34-0.52. Where $κ$ is high, cross-account co-movement is mostly a race for the price on a centralized exchange. It is concentrated in orders a few blocks apart, and a cross-fitted gradient-boosted filter that sees only Binance data removes about three quarters or more of it. This race flow accounts for most of the losses of liquidity providers. We recommend reporting $κ$ alongside the herding share and modeling on-chain order flow as splitting traders plus arbitrageurs who respond to the centralized-exchange price.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a novel decomposition of order-flow persistence, particularly the kappa ratio, which significantly refines the understanding of herding vs. racing in on-chain markets. It employs robust empirical analysis on real DEX data, supported by machine learning techniques for signal filtering. The findings offer critical insights for liquidity providers and market microstructure modeling in DeFi.

Research Flowchart

  flowchart TD
    A[Research Goal: Decompose Cross-Account Order-Flow Persistence] --> B{Key Methodology: Decompose Herding Share into p and κ};
    B --> C[Data Input: Uniswap v3 (USDC-WETH, 2023/2024), On-chain EVM LOB, Uniswap Swaps >$100k];
    C --> D{Computational Process: Calculate p, κ, Herding Share, Cross-fitted gradient-boosted filter (Binance data)};
    D --> E[Key Finding 1: Herding share high (0.61-0.87), but κ low (0.05-0.10) for Uniswap v3];
    E --> F[Key Finding 2: κ high (0.72-0.76) for EVM LOB, (0.34-0.52) for large Uniswap swaps];
    F --> G[Key Finding 3: High κ = mostly race for CEX price, concentrated in short blocks, removed by CEX filter];
    G --> H[Outcome: κ should be reported with herding share, model on-chain flow as splitting traders + CEX arbitrageurs];