Do Algorithmic Traders Improve Liquidity When Information Asymmetry is High?

Archana Jain (), Chinmay Jain and Revansiddha Basavaraj Khanapure ()
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Archana Jain: Saunders College of Business, Rochester Institute of Technology, Rochester, NY 14623, USA
Chinmay Jain: SUNY Geneseo, Geneseo, NY 14454, USA3Ontario Tech University, Oshawa, Ontario, L1H 7K4 Canada
Revansiddha Basavaraj Khanapure: Jindal School of Management, University of Texas at Dallas, 800 W Campbell Rd, JSOM 14.218, Richardson, TX 75080, USA

Quarterly Journal of Finance (QJF), 2021, vol. 11, issue 01, 1-32

Abstract: Hendershott et al. (2011, Does Algorithmic Trading Improve Liquidity? Journal of Finance 66, 1–33) show that algorithmic traders improve liquidity in equity markets. An equally important and unanswered question is whether they improve liquidity when information asymmetry is high. We use days surrounding earnings announcement as a period of high information asymmetry. First, we follow Hendershott et al. (2011, Does Algorithmic Trading Improve Liquidity? Journal of Finance 66, 1–33) to use introduction of NYSE autoquote as a natural experiment. We find that increased algorithmic trading (AT) as a result of NYSE autoquote does not improve liquidity around earnings announcements. Next, we use trade-to-order volume % and cancel rate as a proxy for algorithmic trading and find that abnormal spreads surrounding the days of earnings announcement are significantly higher for stocks with higher AT. Our findings indicate that algorithmic traders reduces their role of liquidity provision in markets when information asymmetry is high. These findings shed further light on the role of liquidity provision by algorithmic traders in the financial markets.

Keywords: Algorithmic trading; liquidity; earnings announcement; information asymmetry (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1142/S2010139220500159

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