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

Zhang, J; McBurney, P; Musial, K

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

Zhang, J McBurney, P Musial, K

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Publication Type:: Journal Article
Citation:: Review of Quantitative Finance and Accounting, 2018, 50 (1), pp. 301 - 352
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	McBurney, P	en_US
dc.contributor.author	Musial, K https://orcid.org/0000-0001-6038-7647	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Review of Quantitative Finance and Accounting, 2018, 50 (1), pp. 301 - 352	en_US
dc.identifier.issn	0924-865X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129296
dc.description.abstract	© 2017, Springer Science+Business Media New York. 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.	en_US
dc.relation.ispartof	Review of Quantitative Finance and Accounting	en_US
dc.relation.isbasedon	10.1007/s11156-017-0631-3	en_US
dc.subject.classification	Finance	en_US
dc.title	Convergence of trading strategies in continuous double auction markets with boundedly-rational networked traders	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	50	en_US
utslib.for	010205 Financial Mathematics	en_US
utslib.for	150201 Finance	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	50	en_US

Abstract:

© 2017, Springer Science+Business Media New York. 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/129296