Algorithmic trading in turbulent markets

Hao Zhou, Petko S. Kalev and Alex Frino

Pacific-Basin Finance Journal, 2020, vol. 62, issue C

Abstract: Does Algorithmic Trading (AT) exacerbate price swings in turbulent markets? We find that stocks with high AT experience less price drops (surges) on days when the market declines (increases) for more than 2%. This result is consistent with the view that AT minimizes price pressures and mitigates transitory pricing errors. Further analyses show that the net imbalances of AT liquidity demand and supply orders have smaller price impacts compared to non-AT net order imbalances and algorithmic traders reduce their price pressure by executing their trades based on the prevailing volume-weighted average prices.

Keywords: Algorithmic trading; Order imbalance; Turbulent markets; Volume-weighted average price; Price swing (search for similar items in EconPapers)
JEL-codes: G12 G14 G19 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0927538X20302201
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:62:y:2020:i:c:s0927538x20302201

DOI: 10.1016/j.pacfin.2020.101358

Access Statistics for this article

Pacific-Basin Finance Journal is currently edited by K. Chan and S. Ghon Rhee

More articles in Pacific-Basin Finance Journal from Elsevier
Bibliographic data for series maintained by Catherine Liu ().