The Good, the Bad, and the Ugly: How algorithmic traders impact institutional trading costs

NBER Big Data and Securities Markets Conference

48 Pages Posted: 26 Jul 2016 Last revised: 15 Dec 2020

See all articles by Tālis J. Putniņš

Tālis J. Putniņš

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

Joseph Barbara

Australian Securities and Investments Commission (ASIC)

Date Written: December 15, 2020

Abstract

We show that behind the aggregate effects of algorithmic and high-frequency traders (AT/HFT) is substantial heterogeneity in how individual algorithms impact institutional trading costs. Using unique trader-identified regulatory data, we find that the cluster of “harmful” algorithmic traders doubles institutional trading costs. “Beneficial” algorithmic traders offset much of this increase. We find no evidence that fast traders (HFTs) are more harmful. Traders that hold inventory overnight are more likely to benefit institutional investors by providing more sustained liquidity. The heterogeneity 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, The Good, the Bad, and the Ugly: How algorithmic traders impact institutional trading costs (December 15, 2020). NBER Big Data and Securities Markets Conference, 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)

Digital Finance CRC ( email )

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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