The Price Impact of Order Book Events

By Rama Cont, A. Kukanov, Sasha Stoikov. 2010

431 citations recorded by the source corpus.

We study the price impact of order book events - limit orders, market orders and cancelations - using the NYSE TAQ data for 50 U.S. stocks. We show that, over short time intervals, price changes are mainly driven by the order flow imbalance, defined as the imbalance between supply and demand at the best bid and ask prices. Our study reveals a linear relation between order flow imbalance and price changes, with a slope inversely proportional to the market depth. These results are shown to be robust to seasonality effects, and stable across time scales and across stocks. We argue that this linear price impact model, together with a scaling argument, implies the empirically observed "square-root" relation between price changes and trading volume. However, the relation between price changes and trade volume is found to be noisy and less robust than the one based on order flow imbalance.

Subfields: Economics and Econometrics, Finance

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Citations in the corpus

Hawkes model for price and trades high-frequency dynamics (2013, background): “…in high-frequency finance from the famous ‘zero-intelligence’ order-book models Smith et al. (2003), Farmer et al. (2005) to models for trade Hewlett (2006), Large (2007), Bauwens and Hautsch (2009) or book events Muni Toke (2011), Embrechts et al. (2011), Cont et al. (2014) irregular arrivals.”
Hawkes Processes in Finance (2015, background): “[19] indicate that a substantial part of the dynamics of prices can be accounted for by the evolution best bid and best ask only.”
Price Dynamics in a Markovian Limit Order Market (2011, methodology): “Instead of keeping track of arrival, cancelation and execution of orders at all price levels (as in Cont et al. (2010b), Smith et al. (2003)), we treat the queue sizes after a price change as stationary variables drawn from a certain distribution f on 2 which represents, in a statistical sense, the…”
Sequence classification of the limit order book using recurrent neural networks (2017, background): “In contrast, several other studies state that such limit orders are informative(Parlour, 1998; Bloomﬁeld et al., 2005; Cao et al., 2009; Zheng et al., 2013; Kearns and Nevmyvaka, 2013; Cont et al., 2014; D. and Pollak, 2014).”
Enhancing trading strategies with order book signals (2018, methodology): “More recently, with access to better quality data, Cont et al. (2013) employ trade and quote data from Nasdaq to perform a statistical study of the price impact of order book events. They show that price changes are driven by order flow imbalances. The impact of volume imbalance on price changes and”
Optimal order placement in limit order markets (2012, background): “However, empirical studies show that market impact differences between limit and market orders are small (Mastromatteo et al. 2014, Cont et al. 2014).”
Fire sales, indirect contagion and systemic stress testing (2017, neutral): “Common specifications are the linear model [Kyle, 1985, Bertsimas and Lo, 1998], [Almgren and Chriss, 2000, Obizhaeva, 2012, Cont et al., 2014] Ψµ(q) = q Dµ with Dµ = c ADVµ σµ (25) where ADVµ is the average daily trading volume (in EUR), σµ the daily volatility (in”
A High Frequency Trade Execution Model for Supervised Learning (2017, neutral): “The complex relation between order book dynamics and price movements has been the focus of econometric and stochastic modeling (see Engle and Russell (1998); Cont et al. (2010b, 2014); Cont and de Larrard (2013); Chavez-Casillas and Figueroa-Lopez (2017) and references therein).”
Limit order books (2010, methodology): “Cont et al. (2011) noted that their ordinary least-squares regressions provided a strong fit across all stocks, despite the nuances of how the individual stocks were traded.”
Statistical Modeling of High-Frequency Financial Data (2011, methodology): “This focus on trades leaves out the information in quotes, which provide a more detailed picture of price formation [27, 23, 20].”
Testing Stylized Facts of Bitcoin Limit Order Books (2019, background): “Cont et al. (2014) empirically demonstrate that short-term returns are mainly driven by order flow imbalances.”
Order Book Dynamics in Liquid Markets: Limit Theorems and Diffusion Approximations (2012, methodology): “More recently, Cont, Stoikov and Talreja Cont et al. (2010b) have studied a Markovian queueing model of a limit order book, in which arrivals of market orders and limit orders at each price level are modeled as independent Poisson processes.”