Why is equity order flow so persistent?

Abstract

Order flow in equity markets is remarkably persistent in the sense that order signs (to buy or sell) are positively autocorrelated out to time lags of tens of thousands of orders, corresponding to many days. Two possible explanations are herding, corresponding to positive correlation in the behavior of different investors, or order splitting, corresponding to positive autocorrelation in the behavior of single investors. We investigate this using order flow data from the London Stock Exchange for which we have membership identifiers. By formulating models for herding and order splitting, as well as models for brokerage choice, we are able to overcome the distortion introduced by brokerage. On timescales of less than a few hours the persistence of order flow is overwhelmingly due to splitting rather than herding. We also study the properties of brokerage order flow and show that it is remarkably consistent both cross-sectionally and longitudinally.

The impact of latency sensitive trading on high frequency arbitrage opportunities

“…Brogaard et al (2014b) find following infrastructure upgrades on the LSE, the associated increase in HFT activity does not affect institutional trader costs, while Van Kervel and Menkveld (2016) document that institutional transaction costs increase (decrease) when HFTs trade in the same (opposite) direction as institutional investors who execute a package of trades through order-splitting strategies on the Nasdaq OMX Sweden. Conversely, Toth et al (2015) find order splitting does not appear to change with the rise of algorithmic trading on the LSE.…”

Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data

“…This, along with the possibility to obtain event data from exchanges, yields huge amounts of data, which has created new opportunities for data processing. This enables market analysis on a completely new level on many interesting questions (see, for example Toth et al, 2015;Chiarella et al, 2015), but has also brought unique challenges for both theory and computational methods (Cont, 2011). In the recent literature, both tractable models and data-driven approach-that is, machine learning-have been introduced to predict price movements with LOB data (Cont et al, 2010;Cont, 2011;Cont and De Larrard, 2012;Kercheval and Zhang, 2015;Ntakaris et al, 2018;Tsantekidis et al, 2017b,a;Passalis et al, 2017;Dixon, 2018;Tran et al, 2018;Sirignano and Cont, 2018).…”

Large Large-Trader Activity Weakens the Long Memory of Limit Order Markets

“…Many references find that C α (τ ) ∝ aτ −b where b < 1, in which case the integral of C α (τ ) is infinite (see e.g. Lillo et al [2005], Toth et al [2015]) (we omit the α index for a and b in order to avoid too heavy notations). This is indeed a good approximation for very long time series.…”