Paper: arXiv 2610.21510

Authors: Aleksei Adadurov, Eduard Airapetian, Anton Chtepine, Antero Eloranta, Boris Pletenev, Andrei Sebyakin, Arsenii Valitov

Abstract

We study the spread a liquidity provider needs to break even against counterparties that can exploit a stale quote, and we place two defenses on a single scale: repricing sooner and screening informed flow. A break-even condition pins down the exact screening rate at which a slower maker matches a faster unscreened venue: the selection that buys back the extra staleness. The result requires only an integrable price innovation and therefore holds under jumps, atoms, and infinite variance. Predictable drift is a centering problem: it widens a spot-centered quote, but its cost vanishes once the quote is recentered on the expected reference. In four USDC-WETH pools benchmarked against Binance ETH-USDT from June to August 2026, the one-second benchmark innovation is sharply non-Gaussian: 73% of seconds show no price change. Cutting cadence from twelve seconds to one sheds 84.5% of the expected shock, so a twelve-second maker must screen at least 84.5% of informed flow to match a one-second venue, far above the 71.1% that a Gaussian model implies. In trending markets, the stale pool is picked off at execution: with-trend fills obtain an effective spread 11.1 bps lower than against-trend fills, with the same sign in all four pools.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a sophisticated theoretical model for liquidity provision under adverse selection, rigorously tested with real-world cryptocurrency data. Its novel approach to quantifying the trade-off between repricing speed and informed flow screening is a significant contribution.

Research Flowchart

  flowchart TD
    A[Research Goal: Break-even Spread for LPs] --> B{Key Methodology: Quantify Screening vs. Repricing};
    B --> C[Data/Inputs: USDC-WETH pools, Binance ETH-USDT (June-Aug 2026)];
    C --> D{Computational Process: Model Price Innovation, Break-even Condition, Empirical Analysis};
    D --> E[Key Finding 1: Non-Gaussian Price Innovation (73% no change in 1s)];
    E --> F[Key Finding 2: Slower Maker Needs High Screening (84.5% vs 71.1% Gaussian)];
    F --> G[Key Finding 3: Trending Markets = Execution Pick-off (11.1 bps lower effective spread with-trend)];

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2 of 69 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.