How Does Algorithmic Trading Improve Market Quality?

48 Pages Posted: 1 Apr 2015 Last revised: 6 Nov 2018

Date Written: October 10, 2015

Abstract

We use a comprehensive panel of NYSE order book data to show that the liquidity and quoting efficiency improvements associated with algorithmic trading (AT) are attributable to enhanced monitoring by liquidity providers. We find that variation in liquidity provider monitoring uniquely explains quoting behaviors around idiosyncratic versus multi-asset price jumps and small- versus large-stock price jumps. In addition, we find monitoring outperforms measures of overall AT activity in explaining stock-level decreases in liquidity costs, and that residual variation in AT is associated with increased spreads. Importantly, our results indicate that there are diminishing returns to market function from subsequent technological advancements, thus providing a novel explanation for why spreads have not continued to fall since 2007 despite sustained increases in algorithmic trading.

Keywords: High-Frequency Market Making, Algorithmic Trading, Adverse Selection, Monitoring Costs

JEL Classification: G10, G12, G14

Suggested Citation

Lyle, Matthew R. and Naughton, James P., How Does Algorithmic Trading Improve Market Quality? (October 10, 2015). Available at SSRN: https://ssrn.com/abstract=2587730 or http://dx.doi.org/10.2139/ssrn.2587730

Matthew R. Lyle

Northwestern University - Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

James P. Naughton (Contact Author)

University of Virginia, Darden School of Business ( email )

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

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