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Algorithmic trading in turbulent markets

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  • Zhou, Hao
  • Kalev, Petko S.
  • Frino, Alex

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.

Suggested Citation

  • Zhou, Hao & Kalev, Petko S. & Frino, Alex, 2020. "Algorithmic trading in turbulent markets," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:pacfin:v:62:y:2020:i:c:s0927538x20302201
    DOI: 10.1016/j.pacfin.2020.101358
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    1. Arumugam, Devika & Krishna Prasanna, P., 2021. "Commonality and contrarian trading among algorithmic traders," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).

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    More about this item

    Keywords

    Algorithmic trading; Order imbalance; Turbulent markets; Volume-weighted average price; Price swing;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G19 - Financial Economics - - General Financial Markets - - - Other

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