Heterogeneity in How Algorithmic Traders Impact Institutional Trading Costs

44 Pages Posted: 26 Jul 2016 Last revised: 27 Jan 2020

See all articles by Tālis J. Putniņš

Tālis J. Putniņš

University of Technology Sydney (UTS); Stockholm School of Economics, Riga

Joseph Barbara

Australian Securities and Investments Commission (ASIC)

Date Written: July 24, 2017

Abstract

We show that behind the aggregate effects of algorithmic and high-frequency trading (AT/HFT) lies rich heterogeneity in the effects of individual traders/algorithms. Using unique regulatory data, we find that the most harmful traders double the costs of executing institutional parent orders. Beneficial traders offset much of this increase. HFTs are no more likely to increase institutional trading costs than non-HFTs. We identify other characteristics that distinguish harmful and beneficial traders. The paper explains why AT/HFT appear detrimental to some investors despite being beneficial or benign in aggregate.

Keywords: Algorithmic Trading, High-Frequency Trading, Liquidity, Transaction Costs, Implementation Shortfall, Predatory Trading

JEL Classification: G14

Suggested Citation

Putnins, Talis J. and Barbara, Joseph, Heterogeneity in How Algorithmic Traders Impact Institutional Trading Costs (July 24, 2017). CIFR Paper No. 113/2016/Project F005. Available at SSRN: https://ssrn.com/abstract=2813870 or http://dx.doi.org/10.2139/ssrn.2813870

Talis J. Putnins (Contact Author)

University of Technology Sydney (UTS) ( email )

PO Box 123
Broadway
Sydney
Australia
+61 2 9514 3088 (Phone)

Stockholm School of Economics, Riga ( email )

Strelnieku iela 4a
Riga, LV 1010
Latvia
+371 67015841 (Phone)

Joseph Barbara

Australian Securities and Investments Commission (ASIC) ( email )

Sydney
Australia

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