Speed traps: algorithmic trader performance under alternative market balances and structures

Yan Peng (), Jason Shachat, Lijia Wei () and S. Sarah Zhang
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Yan Peng: Xiamen University
Lijia Wei: Wuhan University

Experimental Economics, 2024, vol. 27, issue 2, No 3, 325-350

Abstract: Abstract Using double auction market experiments with both human and agent traders, we demonstrate that agent traders prioritising low latency often generate, sometimes perversely so, diminished earnings in a variety of market structures and configurations. With respect to the benefit of low latency, we only find superior performance of fast-Zero Intelligence Plus (ZIP) buyers to human buyers in balanced markets with the same number of human and fast-ZIP buyers and sellers. However, in markets with a preponderance of agents on one side of the market and a noncompetitive market structure, such as monopolies and duopolies, fast-ZIP agents fall into a speed trap. In such speed traps, fast-ZIP agents capture minimal surplus and, in some cases, experience near first-degree price discrimination. In contrast, the trader performance of slow-ZIP agents is comparable to that of human counterparts, or even better in certain market conditions.

Keywords: Trading agents; Speed; Algorithmic trading; Laboratory experiment (search for similar items in EconPapers)
JEL-codes: C78 C92 D40 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10683-023-09816-8

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