| Título: | An Electronic Market-Maker |
| Autores: |
Chan, Nicholas Tung Shelton, Christian |
| Fecha: |
2004-10-20 2004-10-20 2001-04-17 |
| Publicador: | MIT |
| Fuente: |
 |
| Tipo: | |
| Tema: | |
| Descripción: | 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. |
| Idioma: | Inglés |
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