Authors:
Steve Stotter
;
John Cartlidge
and
Dave Cliff
Affiliation:
University of Bristol, United Kingdom
Keyword(s):
Software Agents, Auctions, Financial Markets, Automated Trading, Computational Finance, ExPo.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Auctions and Markets
;
Economic Agent Models
Abstract:
Automated trading systems in the global financial markets are increasingly being deployed to do jobs previously done by skilled human traders: very often a human trader in the markets simply cannot tell whether the counter-party to a trade is another human, or a machine. Clearly, automated trading systems can easily be considered as “intelligent” software agents. In this paper we report on experiments with software traderagents
running the well-known “AA” and “ZIP” strategies, often used as reference benchmarks in previously published studies; here we suggest disambiguated standard implementations of these algorithms. Then, using Exchange Portal (ExPo), an open-source financial exchange simulation platform designed for real-time behavioural economic experiments involving human traders and/or trader-agents, we explore the impact of introducing a new method for assignment adaptation in ZIP. Results show that markets containing only assignmentadaptive (ASAD) agents equilibrate more quic
kly after market shocks than markets containing only “standard” ZIP agents. However, perhaps counter-intuitively, in mixed heterogeneous populations of ASAD agents and ZIP agents, ZIP agents outperform ASAD agents. Evidence suggests that the behaviour of ASAD agents act
as a new signal in the market that ZIP agents then use to beneficially alter their own behaviour, to the detriment
of the ASAD agents themselves.
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