Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Economic Dynamics and Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Economic Dynamics and Control, 120, 2020 DOI: 10.1016/j.jedc.2020.103992
Accepted author manuscript, 2.07 MB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - The effects of trade size and market depth on immediate price impact in a limit order book market
AU - Pham, Manh
AU - Anderson, Heather
AU - Lajbcygier, Paul
AU - Duong, Huu Nhan
N1 - This is the author’s version of a work that was accepted for publication in Journal of Economic Dynamics and Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Economic Dynamics and Control, 120, 2020 DOI: 10.1016/j.jedc.2020.103992
PY - 2020/11/1
Y1 - 2020/11/1
N2 - We compare trade size to the prevailing market depth at the best level in the limit order book to detect and account for zero impact trades in an immediate price impact model. Our model also incorporates standard trade attributes (trade size, market capitalization and volatility) in a dynamic setting. The incorporation of market depth information reduces the mean absolute/squared forecast error of an immediate price impact prediction by about 60%. After controlling for trade attributes, market depth, price impact dynamics and intra-and inter- day periodicities (in order of relative importance) all improve the prediction of a trade's price impact. We demonstrate the value of our model by showing that splitting a big order into a series of smaller trades results in a reduction of between 60% and 82% of the immediate price impact cost of the big order. We also find that our depth indicator helps with the prediction of order flow and permanent price impact.
AB - We compare trade size to the prevailing market depth at the best level in the limit order book to detect and account for zero impact trades in an immediate price impact model. Our model also incorporates standard trade attributes (trade size, market capitalization and volatility) in a dynamic setting. The incorporation of market depth information reduces the mean absolute/squared forecast error of an immediate price impact prediction by about 60%. After controlling for trade attributes, market depth, price impact dynamics and intra-and inter- day periodicities (in order of relative importance) all improve the prediction of a trade's price impact. We demonstrate the value of our model by showing that splitting a big order into a series of smaller trades results in a reduction of between 60% and 82% of the immediate price impact cost of the big order. We also find that our depth indicator helps with the prediction of order flow and permanent price impact.
KW - Immediate Price Impact
KW - Market Depth
KW - Order Flow
KW - Forecasts
U2 - 10.1016/j.jedc.2020.103992
DO - 10.1016/j.jedc.2020.103992
M3 - Journal article
VL - 120
JO - Journal of Economic Dynamics and Control
JF - Journal of Economic Dynamics and Control
SN - 0165-1889
M1 - 103992
ER -