Market Making via Reinforcement Learning

Spooner, Thomas, Fearnley, John, Savani, Rahul ORCID: 0000-0003-1262-7831 and Koukorinis, Andreas (2018) Market Making via Reinforcement Learning In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1.

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Abstract

Market making is a fundamental trading problem in which an agent provides liquidity by continually offering to buy and sell a security. The problem is challenging due to inventory risk, the risk of accumulating an unfavourable position and ultimately losing money. In this paper, we develop a high-fidelity simulation of limit order book markets, and use it to design a market making agent using temporal-difference reinforcement learning. We use a linear combination of tile codings as a value function approximator, and design a custom reward function that controls inventory risk. We demonstrate the effectiveness of our approach by showing that our agent outperforms both simple benchmark strategies and a recent online learning approach from the literature.

Item Type:	Conference Item (Unspecified)
Uncontrolled Keywords:	35 Commerce, Management, Tourism and Services, 3502 Banking, Finance and Investment, 46 Information and Computing Sciences, 4602 Artificial Intelligence, 4611 Machine Learning
Depositing User:	Symplectic Admin
Date Deposited:	24 Jan 2019 12:34
Last Modified:	23 May 2026 01:31
DOI:	10.65109/yyqw8667
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3020822
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