Market Making without Regret

Abstract

We consider a sequential decision-making setting where, at every round t, the learner (a market maker) posts a bid price B_t and an ask price A_t to an incoming trader (the taker) with a private valuation for 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. Letting M_t be the market price (observed only at the end of round t), the maker’s utility is M_t-B_t if the maker bought the asset, it is A_t-M_t if they sold it, and it is 0 if no trade occurred. 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 market prices and takers’ valuations. The difficulty in the analysis stems from a unique relationship between the reward and feedback functions that allows learning algorithms to trade off reward for information in a continuous way.