| 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 |
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