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

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

International Review of Financial Analysis, 2019, vol. 63, issue C, 27-39

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.

Keywords: Algorithmic trading; Order flows; Price discovery; Pricing errors (search for similar items in EconPapers)
JEL-codes: G12 G14 G19 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:63:y:2019:i:c:p:27-39

DOI: 10.1016/j.irfa.2019.02.006

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