# Automated Market Making and Loss-Versus-Rebalancing

arXiv: https://arxiv.org/abs/2208.06046 · HTML: https://mlsanity.com/paper/2208.06046/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2022.

## Built on by (most cited first, 10 of 10)

- [Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning](https://mlsanity.com/paper/2309.10129/) (2023)
- [Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning](https://mlsanity.com/paper/2501.07508/) (2025)
- [Optimal Automated Market Makers: Differentiable Economics and Strong Duality](https://mlsanity.com/paper/2402.09129/) (2024)
- [Werracle: Sub-Cent Intra-Block AI Reflex Oracles and Flash-Loan Circuit Breakers for EVM Smart Contracts](https://mlsanity.com/paper/2609.30719/) (2026)
- [ZeroSwap: Data-driven Optimal Market Making in DeFi](https://mlsanity.com/paper/2310.09413/) (2023)
- [RAmmStein: Regime Adaptation in Mean-reverting Markets with Stein Thresholds -- Optimal Impulse Control in Concentrated AMMs](https://mlsanity.com/paper/2602.19419/) (2026)
- [Optimal Adaptive Market Making: A Theoretical Framework for High-Yield Liquidity Provision in Perpetual Futures Markets](https://mlsanity.com/paper/2607.11888/) (2026)
- [Concentrated Liquidity Provision: a Reinforcement Learning Perspective](https://mlsanity.com/paper/2608.19389/) (2026)
- [Artificial Intelligence in Equity and Crypto Markets: Progress, Profitability Evidence, and the Limits of Automated Investing](https://mlsanity.com/paper/2609.04917/) (2026)
- [Zero-Storage Procedural Neural Synthesis via Boundary Dynamics: Formal Verification in Lean 4 and Bare-Metal Gauntlet Validation](https://mlsanity.com/paper/2609.33066/) (2026)

## Abstract (arXiv)

Automated Market Makers (AMMs) are both liquidity sources and investment vehicles for market participants. This paper analyzes the risks and returns of liquidity provision (LP) investments in AMMs. In a continuous-time model, we show that LP returns decompose into a beta-like component reflecting market risk exposure, and an alpha-like component reflecting microstructural forces: accrued fees minus losses to arbitrageurs. Applying our decomposition to the Uniswap v2 ETH-USDC pool, we find that over 99.991% of LP return variance is driven by beta exposure to market risk