🤖 AI Summary
This work proposes modeling automated market makers (AMMs) as programmable portfolio execution mechanisms capable of strictly and verifiably implementing predefined asset weights without active management intervention. Building upon the geometric mean market maker (G3M) invariant, the approach constructs target-weight portfolios augmented with multi-asset dynamic fees and achieves band-based rebalancing driven by arbitrage incentives. The mechanism enables on-chain verification of portfolio compliance, formally establishing AMMs for the first time as verifiable investment products. Simulations demonstrate that, relying solely on arbitrage-driven trading flows and with fees set within reasonable ranges, G3M-based portfolios outperform real-world benchmarks—including VBIAX, EQL, and EDOW—in both annualized returns and tracking error.
📝 Abstract
Automated market makers (AMMs) are typically interpreted and evaluated as decentralized exchanges. Herein, we take the perspective envisioned by Balancer that an AMM can also be viewed as a portfolio technology that programmatically enforces an economic mandate. In particular, we follow the geometric mean market maker (G3M) invariant employed by that protocol in order to enforce a target-weighted portfolio. We introduce a multi-asset fee structure to the G3M under which competitive arbitrage implements a band-rebalancing strategy with mis-weighting bounded ex ante, allowing compliance with the mandate to be verified directly from the pool's observable holdings. We then compare simulated G3M portfolios against the realized performance of VBIAX, EQL, and EDOW on annualized returns and tracking error against the portfolio mandate. Across these historical case studies, and using arbitrage-only order flow, the G3M is found to outperform the incumbent funds in both metrics for certain fee ranges.