dc.contributor.author | Chan, Nicholas Tung | en_US |
dc.contributor.author | Shelton, Christian | en_US |
dc.date.accessioned | 2004-10-20T20:50:09Z | |
dc.date.available | 2004-10-20T20:50:09Z | |
dc.date.issued | 2001-04-17 | en_US |
dc.identifier.other | AIM-2001-005 | en_US |
dc.identifier.other | CBCL-195 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/7220 | |
dc.description.abstract | 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. | en_US |
dc.format.extent | 2620276 bytes | |
dc.format.extent | 480221 bytes | |
dc.format.mimetype | application/postscript | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.relation.ispartofseries | AIM-2001-005 | en_US |
dc.relation.ispartofseries | CBCL-195 | en_US |
dc.title | An Electronic Market-Maker | en_US |