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An Electronic Market-Maker

Author(s)
Chan, Nicholas Tung; Shelton, Christian
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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.
Date issued
2001-04-17
URI
http://hdl.handle.net/1721.1/7220
Other identifiers
AIM-2001-005
CBCL-195
Series/Report no.
AIM-2001-005CBCL-195

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  • AI Memos (1959 - 2004)
  • CBCL Memos (1993 - 2004)

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