Convergence of trading strategies in continuous double auction markets with boundedly-rational networked traders
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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.
KeywordsMarket microstructure Agent-based modeling Social networks Investment decisions Automated trading Continuous double auctions Reinforcement learning
JEL ClassificationC73 D44 D47 G02 G11
We thank Ramo Gençay, Erik Theissen, Todd P. White, Dimitrios Koutmos, and other participants at the 2nd International Workshop on “Financial Markets and Nonlinear Dynamics” (FMND) coorganized by Fredj Jawadi in Paris on 4 and 5 June 2015, and the 24th Annual Conference on Pacific Basin Finance, Economics, Accounting, and Management organized by Cheng-Few Lee in Taiwan on 11 and 12 June 2016, and two anonymous referees for their comments and suggestions.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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