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

Junhuan Zhang*, Peter McBurney, Katarzyna Musial

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

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 simulation 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.

Original languageEnglish
Pages (from-to)1-52
Number of pages52
JournalReview of Quantitative Finance and Accounting
DOIs
Publication statusE-pub ahead of print - 24 Mar 2017

Keywords

  • Agent-based modeling
  • Automated trading
  • Continuous double auctions
  • Investment decisions
  • Market microstructure
  • Reinforcement learning
  • Social networks

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