mlsanityData to 7 Oct 2026

Market Making without Regret

arXiv 2411.13993 ↗First posted 21 Nov 2024cs.GT cs.LG q-fin.TRcited by 4

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Abstract (arXiv)

We consider a sequential decision-making setting where, at every round t, a market maker posts a bid price Bt and an ask price At to an incoming trader (the taker) with a private valuation for one unit of some asset. If the trader's valuation is lower than the bid price, or higher than the ask price, then a trade (sell or buy) occurs. If a trade happens at round t, then letting Mt be the market price (observed only at the end of round t), the maker's utility is Mt - Bt if the maker bought the asset, and At - Mt if they sold it. We characterize the maker's regret with respect to the best fixed choice of bid and ask pairs under a variety of assumptions (adversarial, i.i.d., and their variants) on the sequence of market prices and valuations. Our upper bound analysis unveils an intriguing connection relating market making to first-price auctions and dynamic pricing. Our main technical contribution is a lower bound for the i.i.d. case with Lipschitz distributions and independence between prices and valuations. The difficulty in the analysis stems from the unique structure of the reward and feedback functions, allowing an algorithm to acquire information by graduating the "cost of exploration" in an arbitrary way.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 75 references that are in the map. First posted on arXiv 21 Nov 2024.

Builds on

25 of 75 references.

References · 25

  1. 2019Introduction to Online Convex Optimizationcited by 793
  2. 2019Introduction to Multi-Armed Banditscited by 475
  3. 2019Online Learning: A Modern Introduction Using Convex Optimizationcited by 407
  4. 2013Bandits with Knapsackscited by 163
  5. 2021Smoothed Analysis with Adaptive Adversariescited by 57
  6. 2022Smoothed Online Learning is as Easy as Statistical Learningcited by 50
  7. 2020Smoothed Analysis of Online and Differentially Private Learningcited by 47
  8. 2015Online learning in repeated auctionscited by 39
  9. 2020Optimal No-regret Learning in Repeated First-price Auctionscited by 33
  10. 2021Bilateral Trade: A Regret Minimization Perspectivecited by 29
  11. 2018Dynamic Pricing with Finitely Many Unknown Valuationscited by 23
  12. 2021Distribution-free Contextual Dynamic Pricingcited by 22
13 more
  1. 2021A Regret Analysis of Bilateral Tradecited by 19
  2. 2023No-Regret Learning in Bilateral Trade via Global Budget Balancecited by 17
  3. 2022An α-regret analysis of Adversarial Bilateral Tradecited by 16
  4. 2022Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum Statescited by 15
  5. 2023Repeated Bilateral Trade Against a Smoothed Adversarycited by 15
  6. 2023The Role of Transparency in Repeated First-Price Auctions with Unknown Valuationscited by 15
  7. 2023An Online Learning Theory of Brokeragecited by 8
  8. 2021Fast Rate Learning in Stochastic First Price Biddingcited by 7
  9. 2021Learning to Price Against a Moving Targetcited by 5
  10. 2024Feature-Based Online Bilateral Tradecited by 5
  11. 2024Fair Online Bilateral Tradecited by 4
  12. 2024A Parametric Contextual Online Learning Theory of Brokeragecited by 4
  13. 2023Adaptive maximization of social welfarecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.