FLAIR: A Metric for Liquidity Provider Competitiveness in Automated Market Makers

ArXiv ID: 2306.09421 “View on arXiv”

Authors: Unknown

Abstract

This paper aims to enhance the understanding of liquidity provider (LP) returns in automated market makers (AMMs). LPs face market risk as well as adverse selection due to risky asset holdings in the pool that they provide liquidity to and the informational asymmetry between informed traders (arbitrageurs) and AMMs. Loss-versus-rebalancing (LVR) quantifies the adverse selection cost (Milionis et al., 2022a), and is a popular metric to evaluate the flow toxicity to an AMM. However, individual LP returns are critically affected by another factor orthogonal to the above: the competitiveness among LPs. This work introduces a novel metric for LP competitiveness, called FLAIR (short for fee liquidity-adjusted instantaneous returns), that aims to supplement LVR in assessments of LP performance to capture the dynamic behavior of LPs in a pool. Our metric reflects the characteristics of fee return-on-capital, and differentiates active liquidity provisioning strategies in AMMs. To illustrate how both flow toxicity, accounting for the sophistication of the counterparty of LPs, as well as LP competitiveness, accounting for the sophistication of the competition among LPs, affect individual LP returns, we propose a quadrant interpretation where all of these characteristics may be readily visualized. We examine LP competitiveness in an ex-post fashion, and show example cases in all of which our metric confirms the expected nuances and intuition of competitiveness among LPs. FLAIR has particular merit in empirical analyses, and is able to better inform practical assessments of AMM pools.

Keywords: automated market makers (AMMs), liquidity providers (LPs), loss-versus-rebalancing (LVR), adverse selection, fee liquidity-adjusted instantaneous returns (FLAIR), Cryptocurrency / DeFi

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail
  • Why: The paper introduces a new metric (FLAIR) with accompanying formulas and a quadrant interpretation framework, requiring moderate mathematical formalism. It is heavily data-driven, analyzing real on-chain AMM pool data to validate the metric against observed behaviors like JIT liquidity, indicating strong empirical implementation potential.
  flowchart TD
    R["Research Goal<br>Develop a metric for LP<br>competitiveness in AMMs"] --> I["Inputs:<br>LP positions, fee data,<br>rebalancing events"]
    I --> C["Computation:<br>FLAIR metric calculation<br>Fee-Adjusted Instantaneous Returns"]
    C --> A["Analysis:<br>Compare FLAIR vs LVR<br>Quadrant interpretation"]
    A --> F["Key Findings:<br>1. FLAIR captures LP competition<br>2. Supplements LVR analysis<br>3. Differentiates active strategies<br>4. Improves pool assessment"]