2020/03/31 by Guillermo Angeris, Tarun Chitra · 128 citations
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Asset (computer security) #Auction Theory and Applications #Computer science #Computer security #Constant (computer programming) #Database #Database transaction #Economics #Financial Markets and Investment Strategies #Function (biology) #Logarithm #Mathematical economics #Mathematical optimization #Mathematics #Oracle #Set (abstract data type) #Sports Analytics and Performance #Value (mathematics) #math.OC #q-fin.TR
paper · pdf · doi:10.1145/3419614.3423251
arxiv created 2020/06/26 · openalex publication_date 2020/10/21 · arxiv updated 2021/01/13 · openalex created_date 2022/07/28 · openalex updated_date 2026/08/05
Automated market makers, first popularized by Hanson's logarithmic market scoring rule (or LMSR) for prediction markets, have become important building blocks, called 'primitives,' for decentralized finance. A particularly useful primitive is the ability to measure the price of an asset, a problem often known as the pricing oracle problem. In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers (or CFMMs) which includes existing popular market makers such as Uniswap, Balancer, and Curve, whose yearly transaction volume totals to billions of dollars. We give sufficient conditions such that, under fairly general assumptions, agents who interact with these constant function market makers are incentivized to correctly report the price of an asset and that they can do so in a computationally efficient way. We also derive several other useful properties that were previously not known. These include lower bounds on the total value of assets held by CFMMs and lower bounds guaranteeing that no agent can, by any set of trades, drain the reserves of assets held by a given CFMM.