Convergence of trading strategies in continuous double auction markets with boundedly-rational networked traders
Authored by Junhuan Zhang, Peter McBurney, Katarzyna Musial
Date Published: 2018
DOI: 10.1007/s11156-017-0631-3
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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.
Tags
Social networks
Agent-based modeling
behavior
Dynamics
market microstructure
Model
Prediction
Zero-intelligence
Stock-market
Reinforcement
learning
Investment
decisions
Automated trading
Continuous double auctions
Neural-network