Active Liquidity On Chain: Evidence from PropAMMs Across Chains

2026-09-29

AI Summary

This study addresses the vulnerability of traditional automated market makers (AMMs) to adverse selection and arbitrage-induced erosion of passive liquidity by presenting the first cross-chain, longitudinal empirical investigation of proactive AMMs (PropAMMs) on blockchains such as Solana. Through on-chain data decoding, longitudinal econometric analysis, and microstructure modeling, we systematically examine their proactive quoting logic, risk-pricing mechanisms, and continuous repricing strategies. Our findings demonstrate that PropAMMs generate excess returns through differentiated risk management and strategic execution, significantly outperforming conventional AMM paradigms. By effectively reducing retail trading costs while enhancing market maker profitability, this work establishes a novel paradigm for decentralized liquidity provision.

Abstract

The liquidity providers of most typical automated market makers (AMMs) are passive and known to suffer from adverse selection. AMMs traditionally rely on trades executed on them to sync the price with external markets (and a notion of fair value thereof). As a result, when an external fair value moves, arbitrageurs trade on AMMs to pick off their stale quotes. A recent approach termed proprietary automated market makers (propAMMs) emerged as a response in 2024: these on-chain programs instead have their singular operator quote from its own inventory, repricing without a trade via efficiently provided price updates. By 2026, propAMMs accounted for more than half of SOL/USDC volume on Solana. We present the first year-long longitudinal measurement of propAMMs on Solana, Base and Monad, and decode the on-chain logic of the dominant propAMM on Base. We find that two seconds after a fill, propAMMs earn 0.37 bps on Solana and 1.19 bps on Base, while AMMs lose 0.22 and 0.62 bps. We empirically quantify and classify their edge as stemming from four factors: propAMMs continuously reprice rather than waiting for a trade, they charge for the source-dependent risk each counterparty might bring, they avoid cross-venue arbitrage from other on-chain markets (such as other AMMs), and they spoof by filling trades at a worse price than they quote. Finally, we show that on our proxy for retail flow (i.e., fills that arrive when the reference price is not moving) propAMMs collect 0.26 bps on Solana where AMMs collect 2.59 bps, indicating that they offer better prices for short-term uninformed flow.

Problem

• automated market makers
• adverse selection
• proprietary AMMs
• liquidity provision
• on-chain trading

Innovation

• PropAMMs
• Automated Market Makers
• Adverse Selection
• On-chain Measurement
• Repricing