Reinforcement Learning Approaches to Optimal Market Making

Market making is the process whereby a market participant, called a market maker, simultaneously and repeatedly posts limit orders on both sides of the limit order book of a security in order to both provide liquidity and generate profit. Optimal market making entails dynamic adjustment of bid and a...

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Autores principales:	Bruno Gašperov, Stjepan Begušić, Petra Posedel Šimović, Zvonko Kostanjčar
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep reinforcement learning reinforcement learning finance market making machine learning deep learning Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/86c142702878470a833bb38d0424c979
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spelling	oai:doaj.org-article:86c142702878470a833bb38d0424c9792021-11-11T18:15:21ZReinforcement Learning Approaches to Optimal Market Making10.3390/math92126892227-7390https://doaj.org/article/86c142702878470a833bb38d0424c9792021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2689https://doaj.org/toc/2227-7390Market making is the process whereby a market participant, called a market maker, simultaneously and repeatedly posts limit orders on both sides of the limit order book of a security in order to both provide liquidity and generate profit. Optimal market making entails dynamic adjustment of bid and ask prices in response to the market maker’s current inventory level and market conditions with the goal of maximizing a risk-adjusted return measure. This problem is naturally framed as a Markov decision process, a discrete-time stochastic (inventory) control process. Reinforcement learning, a class of techniques based on learning from observations and used for solving Markov decision processes, lends itself particularly well to it. Recent years have seen a very strong uptick in the popularity of such techniques in the field, fueled in part by a series of successes of deep reinforcement learning in other domains. The primary goal of this paper is to provide a comprehensive and up-to-date overview of the current state-of-the-art applications of (deep) reinforcement learning focused on optimal market making. The analysis indicated that reinforcement learning techniques provide superior performance in terms of the risk-adjusted return over more standard market making strategies, typically derived from analytical models.Bruno GašperovStjepan BegušićPetra Posedel ŠimovićZvonko KostanjčarMDPI AGarticledeep reinforcement learningreinforcement learningfinancemarket makingmachine learningdeep learningMathematicsQA1-939ENMathematics, Vol 9, Iss 2689, p 2689 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep reinforcement learning reinforcement learning finance market making machine learning deep learning Mathematics QA1-939
spellingShingle	deep reinforcement learning reinforcement learning finance market making machine learning deep learning Mathematics QA1-939 Bruno Gašperov Stjepan Begušić Petra Posedel Šimović Zvonko Kostanjčar Reinforcement Learning Approaches to Optimal Market Making
description	Market making is the process whereby a market participant, called a market maker, simultaneously and repeatedly posts limit orders on both sides of the limit order book of a security in order to both provide liquidity and generate profit. Optimal market making entails dynamic adjustment of bid and ask prices in response to the market maker’s current inventory level and market conditions with the goal of maximizing a risk-adjusted return measure. This problem is naturally framed as a Markov decision process, a discrete-time stochastic (inventory) control process. Reinforcement learning, a class of techniques based on learning from observations and used for solving Markov decision processes, lends itself particularly well to it. Recent years have seen a very strong uptick in the popularity of such techniques in the field, fueled in part by a series of successes of deep reinforcement learning in other domains. The primary goal of this paper is to provide a comprehensive and up-to-date overview of the current state-of-the-art applications of (deep) reinforcement learning focused on optimal market making. The analysis indicated that reinforcement learning techniques provide superior performance in terms of the risk-adjusted return over more standard market making strategies, typically derived from analytical models.
format	article
author	Bruno Gašperov Stjepan Begušić Petra Posedel Šimović Zvonko Kostanjčar
author_facet	Bruno Gašperov Stjepan Begušić Petra Posedel Šimović Zvonko Kostanjčar
author_sort	Bruno Gašperov
title	Reinforcement Learning Approaches to Optimal Market Making
title_short	Reinforcement Learning Approaches to Optimal Market Making
title_full	Reinforcement Learning Approaches to Optimal Market Making
title_fullStr	Reinforcement Learning Approaches to Optimal Market Making
title_full_unstemmed	Reinforcement Learning Approaches to Optimal Market Making
title_sort	reinforcement learning approaches to optimal market making
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/86c142702878470a833bb38d0424c979
work_keys_str_mv	AT brunogasperov reinforcementlearningapproachestooptimalmarketmaking AT stjepanbegusic reinforcementlearningapproachestooptimalmarketmaking AT petraposedelsimovic reinforcementlearningapproachestooptimalmarketmaking AT zvonkokostanjcar reinforcementlearningapproachestooptimalmarketmaking
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Reinforcement Learning Approaches to Optimal Market Making

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