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We study a novel automated market maker design: the function maximizing AMM (FM-AMM). Our central assumption is that trades are batched before execution. Because of competition between arbitrageurs, the FM-AMM eliminates arbitrage profits (or LVR) and sandwich attacks, currently the two main problems in decentralized finance and blockchain design more broadly. We then consider 11 token pairs and use Binance price data to simulate the lower bound to the return of providing liquidity to an FM-AMM. Such a lower bound is, for the most part, slightly higher than the empirical returns of providing liquidity on Uniswap v3 (currently the dominant AMM).

We analyze blocks proposed for inclusion in the Ethereum blockchain during 8 minutes on December 3rd, 2024. Our dataset comprises 38 winning blocks, 15,097 proposed blocks, 10,793 unique transactions, and 2,380,014 transaction-block pairings. We find that exclusive transactions--transactions present only in blocks proposed by a single builder--account for 85% of the fees paid by all transactions included in winning blocks. We also find that a surprisingly large number of user transactions are delayed: although proposed during a bidding cycle, they are not included in the corresponding winning block. Many such delayed transactions are exclusive to a losing builder. We also identify two arbitrage bots trading between decentralized (DEX) and centralized exchanges (CEX). By examining their bidding dynamics, we estimate that the implied price at which these bots trade USDC/WETH and USDT/WETH on CEXes is between 3.4 and 4.2 basis points better than the contemporaneous price reported on Binance.

We analyze 15,097 blocks proposed for inclusion in Ethereum's blockchain over an 8-minute window on December 3, 2024, during which 38 blocks were added to the chain. We classify transactions as exclusive -- present only in blocks from a single builder -- or private -- absent from the public mempool but included in blocks from multiple builders. We find that exclusive transactions account for 84% of the total fees paid by transactions in winning blocks. Furthermore, we show that exclusivity cannot be fully explained by exclusive relationships between senders and builders: about 7% of all exclusive transactions included on-chain, by value, come from senders who route exclusively to a single builder. Analyzing transaction logs shows that some exclusive transactions are duplicates or variations of the same strategy, but even accounting for that, the share of the total fees paid by transactions in winning blocks is at least 77.2%. Taken together, our findings highlight that exclusive transactions are the dominant source of builder revenues.

This paper presents the results of a comprehensive empirical study of losses to arbitrageurs (following the formalization of loss-versus-rebalancing by [Milionis et al., 2022]) incurred by liquidity providers on automated market makers (AMMs). We show that those losses exceed the fees earned by liquidity providers across many of the largest AMM liquidity pools (on Uniswap). Remarkably, we also find that the Uniswap v2 pools are more profitable for passive LPs than their Uniswap v3 counterparts. We also investigate how arbitrage losses change with block times. As expected, arbitrage losses decrease when block production is faster. However, the rate of the decline varies significantly across different trading pairs. For instance, when comparing 100ms block times to Ethereum's current 12-second block times, the decrease in losses to arbitrageurs ranges between 20% to 70%, depending on the specific trading pair.

Blockchain trade intent auctions currently intermediate approximately USD 5 billion monthly. Due to production complementarities, the auction is combinatorial: when multiple trade intents from different traders are auctioned off simultaneously, a bidder (here called solver) can generate additional efficiencies by winning a batch of multiple trade intents. However, sharing these additional efficiencies between traders is problematic: because of market frictions and fees (solvers' private information), the auctioneer does not know how much each trader would have received had its trade been auctioned off individually. We formalize this problem and study the most commonly used auction formats: batch auctions and multiple simultaneous auctions. We also propose a novel fair combinatorial auction that combines batch auction and multiple simultaneous auctions: solvers submit individual-trade bids and batched bids, but batched bids are considered only if they are better for all traders relative to the outcome of the simultaneous auctions constructed using the individual-trade bids. We find a trade-off between the fairness guarantees provided in equilibrium by the auction (i.e., the minimum each trader can expect to receive) and the expected value of the assets returned to the traders. Also, the amount that each trader receives in the equilibrium of the fair combinatorial auction may be higher or lower than what they receive in the equilibrium of the simultaneous auctions used as a benchmark for fairness.