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The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.

Algorithmic Trading and Market Quality: International Evidence

Open access
Boehmer, Ekkehart, Fong, Kingsley, Wu, Juan (Julie)·2021·Journal of Financial and Quantitative Analysis

Abstract

We study the effect of algorithmic trading (AT) on market quality between 2001 and 2011 in 42 equity markets around the world. We use an exchange colocation service that increases AT as an exogenous instrument to draw causal inferences about AT on market quality. On average, AT improves liquidity and informational efficiency but increases short-term volatility. Importantly, AT also lowers execution shortfalls for buy-side institutional investors. Our results are surprisingly consistent across markets and thus across a wide range of AT environments. We further document that the beneficial effect of AT is stronger in large stocks than in small stocks.

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Metadata

Authors
Boehmer, E., Fong, K., & Wu, J. (.
Venue
Journal of Financial and Quantitative Analysis
Published
2020-10-13
Volume
56
Issue
8
Pages
2659-2688
ISSN
  • 0022-1090 (ISSN-L)
  • 1756-6916
OpenAlex
W3122734080
Cited by
322
References
34
FWCI
25.09
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Algorithmic Trading and Market Quality: International Evidence. https://app.cochat.ai/papers/10.1017/s0022109020000782
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Boehmer, E., Fong, K., & Wu, J. (. (2021). Algorithmic Trading and Market Quality: International Evidence. Journal of Financial and Quantitative Analysis. https://doi.org/10.1017/s0022109020000782

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.