Abstract
Market Making (also known as liquidity providing service) is a well-known trading problem studied in multiple disciplines including Finance, Economics and Artificial Intelligence. This paper examines the impact of Market Spread over the market maker’s (or liquidity provider’s) convergence ability through testing the hypothesis that “Knowledge of market spread while learning leads to faster convergence to an optimal and less volatile market making policy”. Reinforcement Learning was used to mimic the behaviour of a liquidity provider with Limit Order Book using historical Trade and Quote data of five equities, as the trading environment. An empirical study of results obtained from experiments (comparing our reward function with benchmark) shows significant improvement in the magnitude of returns obtained by a market maker with knowledge of market spread compared to a market maker without such knowledge, which proves our stated hypothesis.
Original language | English |
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Title of host publication | Machine Learning, Optimization, and Data Science - 5th International Conference, LOD 2019, Proceedings |
Editors | Giuseppe Nicosia, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, Vincenzo Sciacca |
Chapter | 13 |
Pages | 143-153 |
Number of pages | 11 |
Volume | 11943 |
ISBN (Electronic) | 978-3-030-37599-7 |
DOIs | |
Publication status | Published online - 3 Jan 2020 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11943 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Keywords
- Market making
- Market spread
- Reinforcement learning
- Reward function
Fingerprint
Dive into the research topics of 'Effect of Market Spread Over Reinforcement Learning Based Market Maker'. Together they form a unique fingerprint.Student theses
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Methods for reinforcement learning policy improvement for a single market maker
Haider, A. (Author), Hawe, G. (Supervisor), Wang, H. (Supervisor) & Scotney, B. (Supervisor), Apr 2023Student thesis: Doctoral Thesis
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