Information or noise: what does algorithmic trading incorporate into the stock prices?
Date
2019
Authors
Zhou, H.
Elliott, R.J.
Kalev, P.S.
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Journal article
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International Review of Financial Analysis, 2019; 63:27-39
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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.
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Copyright Crown Copyright © 2019 Published by Elsevier