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Information or noise: What does algorithmic trading incorporate into the stock prices?

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  • Zhou, Hao
  • Elliott, Robert J.
  • Kalev, Petko S.

Abstract

This study investigates the role of algorithmic trading in the price discovery process. We estimate a state space framework which decomposes stock prices into permanent price series and transient pricing errors. Algorithmic traders contribute more to the permanent price processes and less to the transient pricing errors compared to other traders overall, during periods of high individual stock volatility, and on market-wide stress days. Algorithmic order flows are more associated with future stock returns compared to non-algorithmic order flows. Our results suggest that algorithmic traders incorporate price-relevant information and mitigate transitory price pressures.

Suggested Citation

  • Zhou, Hao & Elliott, Robert J. & Kalev, Petko S., 2019. "Information or noise: What does algorithmic trading incorporate into the stock prices?," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 27-39.
    • Handle: RePEc:eee:finana:v:63:y:2019:i:c:p:27-39
    DOI: 10.1016/j.irfa.2019.02.006
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    More about this item

    Keywords

    Algorithmic trading; Order flows; Price discovery; Pricing errors;
    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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