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An Electronic Market-Maker

dc.date.accessioned2004-10-20T20:50:09Z
dc.date.accessioned2018-11-24T10:23:25Z
dc.date.available2004-10-20T20:50:09Z
dc.date.available2018-11-24T10:23:25Z
dc.date.issued2001-04-17en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7220
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/7220
dc.description.abstractThis paper presents an adaptive learning model for market-making under the reinforcement learning framework. Reinforcement learning is a learning technique in which agents aim to maximize the long-term accumulated rewards. No knowledge of the market environment, such as the order arrival or price process, is assumed. Instead, the agent learns from real-time market experience and develops explicit market-making strategies, achieving multiple objectives including the maximizing of profits and minimization of the bid-ask spread. The simulation results show initial success in bringing learning techniques to building market-making algorithms.en_US
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dc.format.extent480221 bytes
dc.language.isoen_US
dc.titleAn Electronic Market-Makeren_US


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