The price impact of order book events: market orders, limit orders and cancellations

By Zoltán Eisler, Jean-Philippe Bouchaud, J. Kockelkoren. 2009

200 citations recorded by the source corpus.

While the long-ranged correlation of market orders and their impact on prices has been relatively well studied in the literature, the corresponding studies of limit orders and cancellations are scarce. We provide here an empirical study of the cross-correlation between all these different events, and their respective impact on future price changes. We define and extract from the data the ‘bare’ impact these events would have if they were to happen in isolation. For large tick stocks, we show that a model where the bare impact of all events is permanent and non-fluctuating is in good agreement with the data. For small tick stocks, however, bare impacts must contain a history-dependent part, reflecting the internal fluctuations of the order book. We show that this effect can be accurately described by an autoregressive model of the past order flow. This framework allows us to decompose the impact of an event into three parts: an instantaneous jump component, the modification of the future rates of the different events, and the modification of the jump sizes of future events. We compare in detail the present formalism with the temporary impact model that was proposed earlier to describe the impact of market orders when other types of events are not observed. Finally, we extend the model to describe the dynamics of the bid–ask spread.

Subfields: Economics and Econometrics, Finance

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Citations in the corpus

The adaptive nature of liquidity taking in limit order books (2014, background): “We believe that liquidity providers, through the so-called stimulated refill mechanism (see [8, 19]), are also responsible in part of the restoration of efficiency.”
Statistical Modeling of High-Frequency Financial Data (2011, background): “This focus on trades leaves out the information in quotes, which provide a more detailed picture of price formation [27, 23, 20].”
Deep learning for limit order books (2016, unknown): “Eisler et al. (2012) also study the correlation between different types of limit order book events and measure their impact on price variations. Taranto et al. (2014) studies the relationship between order flow predictability, liquidity, trades, and price changes.”
Optimal order placement in limit order markets (2012, background): “lly be dierent for market and limit orders, as well as for orders sent to dierent exchanges. However, empirical studies show that market impact dierences between limit and market orders are small (Eisler et al. (2012), Mastromatteo et al. (2013)). We also note that market impact occurs over time hor”
The role of volume in order book dynamics: a multivariate Hawkes process analysis (2016, background): “That is, in the former case, the spread is almost always equal to one tick, thus resulting in fewer price changes and larger available volumes at the best quotes (Eisler et al. 2012).”
Universal scaling and nonlinearity of aggregate price impact in financial markets. (2017, unknown): “In a forthcoming paper [13], we will investigate in detail how accurately the nonlinear master curves and rescaling exponents described above can be reproduced using propagator models [29] and their generalisations [30, 31].”