Author:
Marco De Luca
Affiliation:
University of Bristol, United Kingdom
Keyword(s):
Agent-based Computational Economics, Continuous Double Auction, Experimental Economics, Trading Agents.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Auctions and Markets
;
Economic Agent Models
Abstract:
In the past decade there has been a rapid growth of the use of adaptive automated trading systems, commonly referred to in the finance industry as ``robot traders'': AI applications replacing highly-paid human traders in the global financial markets. The academic roots of this industry-changing deployment of AI technologies can be traced back to research published by a team of researchers at IBM at IJCAI 2001, which was subsequently replicated and extended by De Luca and Cliff at IJCAI 2011 and ICAART 2011. Here, we focus on the order management policy enforced by Open Exchange (OpEx), the open source algorithmic trading system designed by De Luca, for both human and robot traders: while humans are allowed to manage multiple orders simultaneously, robots only deal with one order at the time. We hypothesise that such unbalance may have strongly influenced the victory of human traders over robot traders, reported in
past studies by De Luca et al., and by Cartlidge and Cliff. We employ
ed OpEx to implement a multiple-order policy for robots as well as humans, and ran several human vs. robot trading experiments. Using aggregated market metrics and time analysis, we reached two important conclusions. First, we demonstrated that, in mixed human-robot markets, robots dealing multiple simultaneous orders consistently outperform robots dealing one order at a time. And second, we showed that while human traders outperform single-order robot traders under specific circumstances, multiple-order robot traders are never outperformed by human traders. We thus conclude that the performance of robot traders in a human-robot mixed market is strongly influenced by the order management policy they employ.
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