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Speed Traps: Algorithmic Trader Performance Under Alternative Market Structures

Yan Peng, Jason Shachat, Lijia Wei and S. Sarah Zhang
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Yan Peng: School of Economics and Management, Wuhan University

Working Papers from Chapman University, Economic Science Institute

Abstract: Using laboratory experiments, we illustrate that trading algorithms that prioritize low latency pose certain pitfalls in a variety of market structures and configurations. In hybrid double auctions markets with human traders and trading agents, we find superior performance of trading agents to human traders in balanced markets with the same number of human and Zero Intelligence Plus (ZIP) buyers and sellers only, thus providing a partial replication of Das et al. (2001). However, in unbalanced markets and extreme market structures, such as monopolies and duopolies, fast ZIP agents fall into a speed trap and both human participants and slow ZIP agents outperform fast ZIP agents. For human traders, faster reaction time significantly improves trading performance, while Theory of Mind can be detrimental for human buyers, but beneficial for human sellers.

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: 2020
New Economics Papers: this item is included in nep-exp, nep-mst and nep-ore
References: Add references at CitEc
Citations: View citations in EconPapers (3)

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https://digitalcommons.chapman.edu/esi_working_papers/334/

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Persistent link: https://EconPapers.repec.org/RePEc:chu:wpaper:20-39

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