Mandate without Managers: Automated Market Makers as Verifiable Portfolio Products

Battle Count: 70

Highly relevant to quantitative trading and portfolio management. The paper provides a rigorous mathematical framework for understanding how AMM invariants encode portfolio mandates, characterizes the no-arbitrage mis-weighting band analytically, and demonstrates empirically that G3M portfolios can Pareto-dominate traditional funds in both returns and tracking error. Directly applicable to: (1) designing automated rebalancing strategies, (2) understanding LVR as execution cost of mandate maintenance, (3) evaluating DeFi-based portfolio products against traditional funds, (4) fee parameter calibration for band-rebalancing strategies. The convex optimization framework and KKT characterization are directly transferable to quantitative portfolio construction.

The theoretical framework (G3M invariant, fee structure, no-arbitrage bounds) is mathematically well-defined and implementable. The fee decomposition (proportional + residual swap) and path-independence properties are clearly specified. However, practical implementation requires: (1) DeFi smart contract development for multi-asset G3M pools, (2) integration with tokenized asset infrastructure, (3) real-time price oracle feeds, (4) regulatory compliance for portfolio products, (5) the backtesting framework requires Bloomberg data access and careful handling of NAV vs. market value benchmarks. The mathematical proofs are complete but the simulation code is not provided.

The paper provides complete mathematical formulations (invariant, fee structure, KKT conditions, proofs in appendix) and specifies data sources (Bloomberg) and time periods. However, no code repository is provided, Bloomberg data is proprietary, and simulation parameters (arbitrage frequency, reinvestment rules) are described but not fully parameterized for exact replication. The fee range [0%, 10%] and daily arbitrage assumption are stated.