Equity order flow is persistent in the sense that buy orders tend to be
followed by buy orders and sell orders tend to be followed by sell orders. For
equity order flow this persistence is extremely long-ranged, with positive
correlations spanning thousands of orders, over time intervals of up to several
days. Such persistence in supply and demand is economically important because
it influences the market impact as a function of both time and size and because
it indicates that the market is in a sense out of equilibrium. Persistence can
be caused by two types of behavior: (1) Order splitting, in which a single
investor repeatedly places an order of the same sign, or (2) herding, in which
different investors place orders of the same sign. We develop a method to
decompose the autocorrelation function into splitting and herding components
and apply this to order flow data from the London Stock Exchange containing
exchange membership identifiers. Members typically act as brokers for other
investors, so that it is not clear whether patterns we observe in brokerage
data also reflect patterns in the behavior of single investors. To address this
problem we develop models for the distortion caused by brokerage and
demonstrate that persistence in order flow is overwhelmingly due to order
splitting by single investors. At longer time scales we observe that different
investors' behavior is anti-correlated. We show that this is due to differences
in the response to price-changing vs. non-price-changing market orders.
%0 Journal Article
%1 Toth2015Why
%A Tóth, Bence
%A Palit, Imon
%A Lillo, Fabrizio
%A Farmer, J. Doyne
%D 2015
%J Journal of Economic Dynamics and Control
%K financial-markets econophysics
%P 218--239
%R 10.1016/j.jedc.2014.10.007
%T Why is equity order flow so persistent?
%U http://dx.doi.org/10.1016/j.jedc.2014.10.007
%V 51
%X Equity order flow is persistent in the sense that buy orders tend to be
followed by buy orders and sell orders tend to be followed by sell orders. For
equity order flow this persistence is extremely long-ranged, with positive
correlations spanning thousands of orders, over time intervals of up to several
days. Such persistence in supply and demand is economically important because
it influences the market impact as a function of both time and size and because
it indicates that the market is in a sense out of equilibrium. Persistence can
be caused by two types of behavior: (1) Order splitting, in which a single
investor repeatedly places an order of the same sign, or (2) herding, in which
different investors place orders of the same sign. We develop a method to
decompose the autocorrelation function into splitting and herding components
and apply this to order flow data from the London Stock Exchange containing
exchange membership identifiers. Members typically act as brokers for other
investors, so that it is not clear whether patterns we observe in brokerage
data also reflect patterns in the behavior of single investors. To address this
problem we develop models for the distortion caused by brokerage and
demonstrate that persistence in order flow is overwhelmingly due to order
splitting by single investors. At longer time scales we observe that different
investors' behavior is anti-correlated. We show that this is due to differences
in the response to price-changing vs. non-price-changing market orders.
@article{Toth2015Why,
abstract = {{Equity order flow is persistent in the sense that buy orders tend to be
followed by buy orders and sell orders tend to be followed by sell orders. For
equity order flow this persistence is extremely long-ranged, with positive
correlations spanning thousands of orders, over time intervals of up to several
days. Such persistence in supply and demand is economically important because
it influences the market impact as a function of both time and size and because
it indicates that the market is in a sense out of equilibrium. Persistence can
be caused by two types of behavior: (1) Order splitting, in which a single
investor repeatedly places an order of the same sign, or (2) herding, in which
different investors place orders of the same sign. We develop a method to
decompose the autocorrelation function into splitting and herding components
and apply this to order flow data from the London Stock Exchange containing
exchange membership identifiers. Members typically act as brokers for other
investors, so that it is not clear whether patterns we observe in brokerage
data also reflect patterns in the behavior of single investors. To address this
problem we develop models for the distortion caused by brokerage and
demonstrate that persistence in order flow is overwhelmingly due to order
splitting by single investors. At longer time scales we observe that different
investors' behavior is anti-correlated. We show that this is due to differences
in the response to price-changing vs. non-price-changing market orders.}},
added-at = {2019-06-10T14:53:09.000+0200},
archiveprefix = {arXiv},
author = {T\'{o}th, Bence and Palit, Imon and Lillo, Fabrizio and Farmer, J. Doyne},
biburl = {https://www.bibsonomy.org/bibtex/25049b3daeb0df67e9dfc73e8c514b193/nonancourt},
citeulike-article-id = {9636972},
citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.jedc.2014.10.007},
citeulike-linkout-1 = {http://arxiv.org/abs/1108.1632},
citeulike-linkout-2 = {http://arxiv.org/pdf/1108.1632},
day = 8,
doi = {10.1016/j.jedc.2014.10.007},
eprint = {1108.1632},
interhash = {8697471a82d36b0297f42a76465cedd8},
intrahash = {5049b3daeb0df67e9dfc73e8c514b193},
issn = {0165-1889},
journal = {Journal of Economic Dynamics and Control},
keywords = {financial-markets econophysics},
month = feb,
pages = {218--239},
posted-at = {2011-12-06 10:44:22},
priority = {2},
timestamp = {2019-08-01T15:39:33.000+0200},
title = {{Why is equity order flow so persistent?}},
url = {http://dx.doi.org/10.1016/j.jedc.2014.10.007},
volume = 51,
year = 2015
}