Authors:
Marco De Luca
and
Dave Cliff
Affiliation:
University of Bristol, United Kingdom
Keyword(s):
Algorithmic trading, Continuous double auction, Experimental economics, Trading agents.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Economic Agent Models
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
In 2001, a team of researchers at IBM published a paper in IJCAI which reported on the first experiments that systematically studied the interactions of human traders and software-agent traders in electronic marketplaces running the continuous double auction (CDA) mechanism. IBM found that two software-agent strategies, known as GD and ZIP, consistently outperformed human traders. IBM's results received international press coverage, probably because the CDA is the mechanism that is used in the main electronic trading systems that make up the global financial markets. In 2002, Tesauro & Bredin published details of an extension to GD, which they named GDX, for which they wrote: "We suggest that this algorithm may offer the best performance of any published CDA bidding strategy". To the best of our knowledge, GDX has never been tested against human traders under experimental conditions. In this paper, we report on the first such test: we present detailed analysis of the results from our
own replications of IBM's human vs. ZIP experiments and from our world-first experiments that test humans vs. GDX. Our overall findings are that, both when competing against ZIP in pure agent vs. agent experiments and when competing against human traders, GDX's performance is significantly better than the performance of ZIP.
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