An Electronic Market-Maker (2001)

by Nicholas T. Chan and Christian R. Shelton

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 envrionment, 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.

Download Information

Nicholas T. Chan and Christian R. Shelton (2001). "An Electronic Market-Maker." Seventh International Conference of the Society for Computational Economics.

Bibtex citation

@inproceedings{ChaShe01workshop,
   author = "Nicholas T. Chan and Christian R. Shelton",
   title = "An Electronic Market-Maker",
   booktitle = "Seventh International Conference of the Society for Computational Economics",
   booktitleabbr = "{SCE}",
   year = 2001,
}