Convergence of trading strategies in continuous double auction markets with boundedly-rational networked traders.

Musial-Gabrys, K., McBurney, P. and Zhang, J., 2017. Convergence of trading strategies in continuous double auction markets with boundedly-rational networked traders. Review of Quantitative Finance and Accounting. (In Press)

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DOI: 10.1007/s11156-017-0631-3

Abstract

This paper considers the convergence of trading strategies among artificial traders connected to one another in a social network and trading in a continuous double auction financial marketplace. Convergence is studied by means of an agent-based simu- lation model called the Social Network Artificial stoCk marKet model. Six different canonical network topologies (including no-network) are used to represent the possible connections between artificial traders. Traders learn from the trading experiences of their connected neighbours by means of reinforcement learning. The results show that the proportions of traders using particular trading strategies are eventually stable. Which strategies dominate in these stable states depends to some extent on the particular network topology of trader connections and the types of traders.

Item Type:Article
ISSN:1573-7179
Uncontrolled Keywords:Market microstructure; Agent-based modeling; Social networks; Investment decisions; Automated trading; Continuous double auctions; Reinforcement learning
Group:Faculty of Science & Technology
ID Code:28943
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:19 Apr 2017 14:58
Last Modified:19 Apr 2017 14:58

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