No-Regret Revenue Maximization in Sequential Market Making Under Inventory Constraints

Abstract:Market makers keep trading efficient by setting bid and ask prices for buyers and sellers. In sequential market making, trades happen independently based on price thresholds, requiring the market maker to maintain balanced, low-risk inventory over T rounds. This paper studies how to maximize revenue with unknown buyer and seller valuations using regret minimization. When the market maker only sees trade success or failure, an algorithm achieves regret and inventory bounds proportional to T to the power of 2/3. When full order book data is available for failed trades, regret and inventory limits improve significantly to the square root of T. We also establish a fundamental trade-off via a lower bound, by proving that tighter round-by-round inventory limits strictly force higher minimum regret.

Machine Learning (cs.LG)

arXiv:2610.24562 [cs.LG]

(or arXiv:2610.24562v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.24562 Focus to learn more arXiv-issued DOI via DataCite

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