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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7220| Title: | An Electronic Market-Maker |
| Issue Date: | 9-Oct-2013 |
| 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. |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
| Other Identifiers: | AIM-2001-005 CBCL-195 http://hdl.handle.net/1721.1/7220 |
| Appears in Collections: | MIT Items |
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