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

Yan Peng, Jason Shachat, Lijia Wei, Sarah Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Using double auction market experiments with both human and agent traders, we demonstrate that agent traders prioritizing 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 non competitive 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.
Original languageEnglish
JournalExperimental Economics
DOIs
Publication statusPublished - 8 Dec 2023

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