Dynamics of Market Making Algorithms in Dealer Markets: Learning and Tacit Collusion

Cont, Rama; Xiong, Wei

doi:10.2139/ssrn.4119858

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…Chan and Shelton (2001), and more recently, Lim and Gorse (2018) and Spooner et al (2018) have developed RL-based market making approaches for limit order book markets; however, they do not explicitly model the competing market makers or study different competitive scenarios. Cont and Xiong (2023) recently study competition and collusion among a set of market makers, and use reinforcement learning as a mean to solve for the equilibrium of the game. Spooner and Savani (2020) study a discrete-time zero-sum game between a market maker and an adversary and show that adversarial reinforcement learning can help produce more robust policies.…”

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confidence: 99%

Towards multi‐agent reinforcement learning‐driven over‐the‐counter market simulations

Vadori,

Ardon,

Ganesh

et al. 2023

Mathematical Finance

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We study a game between liquidity provider (LP) and liquidity taker agents interacting in an over‐the‐counter market, for which the typical example is foreign exchange. We show how a suitable design of parameterized families of reward functions coupled with shared policy learning constitutes an efficient solution to this problem. By playing against each other, our deep‐reinforcement‐learning‐driven agents learn emergent behaviors relative to a wide spectrum of objectives encompassing profit‐and‐loss, optimal execution, and market share. In particular, we find that LPs naturally learn to balance hedging and skewing, where skewing refers to setting their buy and sell prices asymmetrically as a function of their inventory. We further introduce a novel RL‐based calibration algorithm, which we found performed well at imposing constraints on the game equilibrium. On the theoretical side, we are able to show convergence rates for our multi‐agent policy gradient algorithm under a transitivity assumption, closely related to generalized ordinal potential games.

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confidence: 99%

Towards multi‐agent reinforcement learning‐driven over‐the‐counter market simulations

Vadori,

Ardon,

Ganesh

et al. 2023

Mathematical Finance

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Deep Reinforcement Learning in Financial Markets Context: Review and Open Challenges

Boutyour,

Idrissi

2023

Studies in Computational Intelligence

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AI-driven liquidity provision in OTC financial markets

Cartea

Chang²,

Mroczka

et al. 2022

Quantitative Finance

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Providing liquidity in over-the-counter markets is a challenging under-taking, in large part because a market maker does not observe where their competitors quote, nor do they typically know how many rivals they compete with or what the trader's overall liquidity demand is. Optimal pricing strategies can be derived in theory assuming full knowledge of the competitive environment, but these results do not translate into practice where information is incomplete and asymmetric. This paper studies whether artificial intelligence, in the form of multi-armed bandit reinforcement learning algorithms, can be used by liquidity providers to dynamically set spreads using only information that is commonly available to them. We also investigate whether collusive effects can arise when competing liquidity providers all employ such algorithms. Our findings are as follows. In a single-agent setup where only one liquidity provider is optimising pricing in an otherwise static environment, all the algorithms considered are able to locate the theoretically optimal pricing policy, albeit they do so quite inefficiently when compared to a model-based approach. In a multi-agent setting where competing liquidity providers simultaneously and independently use algorithms to optimise pricing, we demonstrate that for one class of algorithms (pseudo) collusion can not arise, while for another it can theoretically arise in certain circumstances and we provide examples where it does. The scenarios where collusive effects appear, however, are fragile and sensitive to the specific configuration and exceedingly unlikely to occur in practice. Moreover, with a modest number of competitors, collusive effects that might otherwise arise in some of the most contrived scenarios are largely or entirely eliminated.

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Dynamics of Market Making Algorithms in Dealer Markets: Learning and Tacit Collusion

Cited by 4 publications

References 26 publications

Towards multi‐agent reinforcement learning‐driven over‐the‐counter market simulations

Towards multi‐agent reinforcement learning‐driven over‐the‐counter market simulations

Deep Reinforcement Learning in Financial Markets Context: Review and Open Challenges

AI-driven liquidity provision in OTC financial markets

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