Pools as Portfolios: Observed arbitrage efficiency & LVR analysis of dynamic weight AMMs

2026-02-25

AI Summary

Traditional centralized exchange (CEX)-style asset rebalancing is costly and infrequent in decentralized environments, hindering efficient index or smart beta strategies. This work proposes an algorithmic rebalancing mechanism based on a dynamic-weight trading function market maker (TFMM), modeling the rebalancing process as a Dutch reverse auction wherein arbitrageurs drive prices toward target weights. For the first time, this approach is validated on real on-chain environments and shown to surpass CEX-level efficiency. Leveraging the QuantAMM protocol, block-level arbitrage analysis, and an LVR/RVR evaluation framework, the method achieves high execution efficiency on both Ethereum Mainnet and Base. Notably, on Layer 2 networks, low data costs and optimized routing enable performance that meets or exceeds ideal rebalancing benchmarks—even when individual arbitrage profits approach zero.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Algorithm Configuration
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Dynamic-weight AMMs (aka Temporal Function Market Makers, TFMMs) implement algorithmic asset allocation, analogous to index or smart beta funds, by continuously updating pools' weights. A strategy updates target weights over time, and arbitrageurs trade the pool back toward those weights. This creates a sequence of small, predictable mispricings that grow until taken, effectively executing rebalances as a series of Dutch reverse auctions. Prior theoretical and simulation work (Willetts & Harrington, 2024) predicted that this mechanism could outperform CEX-style rebalancing. We test that claim on two live pools on the QuantAMM protocol, one on Ethereum mainnet and one on Base, across two short rebalancing windows six months apart (July 2025 and January 2026). We perform block-level arbitrage analysis, and then measure long term outcomes using Loss-vs-Rebalancing (LVR) and Rebalancing-vs-Rebalancing (RVR) benchmarks. On mainnet, rebalancing becomes markedly more efficient over time (more frequent arbitrage trades with lower value extracted per trade), reaching performance comparable to or better than CEX-based models. On Base, rebalancing persists even when per-trade extraction is near (or below) zero, consistent with routing-driven execution, and achieves efficiencies that meet or exceed standard "perfect rebalancing" LVR baselines. These results demonstrate dynamic-weight AMMs as a competitive execution layer for tokenised funds, with superior performance on L2s where routing and lower data costs compress arbitrage spreads.

Problem

• Dynamic-weight AMMs
• arbitrage efficiency
• Loss-vs-Rebalancing
• rebalancing performance
• tokenised funds

Innovation

• Dynamic-weight AMMs
• Loss-vs-Rebalancing (LVR)
• arbitrage efficiency
• tokenised funds
• L2 execution