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Algorithmic Trading and Market Quality: International Evidence

Published online by Cambridge University Press:  13 October 2020

Ekkehart Boehmer ,
Kingsley Fong Open the ORCID record for Kingsley Fong [Opens in a new window]  and
Juan (Julie) Wu
Ekkehart Boehmer*
Affiliation:
Singapore Management University Lee Kong Chian School of Business
Kingsley Fong
Affiliation:
University of New South Wales Business [email protected]
Juan (Julie) Wu
Affiliation:
University of Nebraska–Lincoln College of [email protected]
*
[email protected] (corresponding author)
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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.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 56 , Issue 8 , December 2021 , pp. 2659 - 2688
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

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Footnotes

We thank Jennifer Conrad (the editor), Michael Goldstein, Joel Hasbrouck, Terry Hendershott, Vincent van Kervel (the referee), Elvira Sojli, and participants at the 2012 Institute Louis Bachelier Market Microstructure Conference, ATC/ECB Workshop on High Frequency Trading, 2016 Bank of England meeting, 2013 AFA meeting, 2015 FMA meeting, 2015 Workshop on the Mathematics of High Frequency Financial Markets (UCLA), Bocconi University, EDHEC Business School, EDHEC Risk Institute London, ESSEC, HKU, HKUST, HSE Moscow, Humboldt Universitaet Berlin, Imperial College, the Monetary Authority of Singapore, National University of Singapore, NTU, SAIF at Shanghai Jiao Tong University, SMU (Dallas), SMU (Singapore), Tinbergen Institute Amsterdam, Texas Christian University, Texas Tech University, Universitaet St. Gallen, University of Houston, University of Iowa, University of Nebraska-Lincoln, University of Rhode Island, University of Wyoming, and Warwick Business School for their helpful comments.

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