Paper: arXiv 2501.07828

Abstract

To trade tokens in cryptoeconomic systems, automated market makers (AMMs) typically rely on liquidity providers (LPs) that deposit tokens in exchange for rewards. To profit from such rewards, LPs must use effective liquidity provisioning strategies. However, LPs lack guidance for developing such strategies, which often leads them to financial losses. We developed a measurement model based on impermanent loss to analyze the influences of key parameters (i.e., liquidity pool type, position duration, position range size, and position size) of liquidity provisioning strategies on LPs’ returns. To reveal the influences of those key parameters on LPs’ profits, we used the measurement model to analyze 700 days of historical liquidity provision data of Uniswap v3. By uncovering the influences of key parameters of liquidity provisioning strategies on profitability, this work supports LPs in developing more profitable strategies.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 6.0/10 (capped from 7.0: no out-of-sample test; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper uses mathematical models (impermanent loss, adverse selection) and statistical analysis of 700 days of historical Uniswap data, indicating moderate math and high empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Determine how AMM parameters affect LP profitability"] --> B["Data Input: 700 days of Uniswap v3 historical data"]
  B --> C["Methodology: Measurement model based on Impermanent Loss"]
  C --> D["Key Parameters Analyzed:<br/>Pool Type, Duration, Range Size, Position Size"]
  D --> E["Computation: Quantify impact of each parameter on returns"]
  E --> F["Key Findings: Identified optimal configurations<br/>for more profitable liquidity strategies"]

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3 of 73 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.