An Electronic Market-Maker

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7220

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DC Field	Value	Language
dc.creator	Chan, Nicholas Tung	-
dc.creator	Shelton, Christian	-
dc.date	2004-10-20T20:50:09Z	-
dc.date	2004-10-20T20:50:09Z	-
dc.date	2001-04-17	-
dc.date.accessioned	2013-10-09T02:48:35Z	-
dc.date.available	2013-10-09T02:48:35Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-2001-005	-
dc.identifier	CBCL-195	-
dc.identifier	http://hdl.handle.net/1721.1/7220	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	This 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.	-
dc.format	2620276 bytes	-
dc.format	480221 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-2001-005	-
dc.relation	CBCL-195	-
dc.title	An Electronic Market-Maker	-
Appears in Collections:	MIT Items

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