The power of patience: a behavioural regularity in limit-order placement

By Ilija I. Zovko, J. Farmer. 2002

140 citations recorded by the source corpus.

Abstract In this paper we demonstrate a striking regularity in the way people place limit orders in financial markets, using a data set consisting of roughly two million orders from the London Stock Exchange. We define the relative limit price as the difference between the limit price and the best price available. Merging the data from 50 stocks, we demonstrate that for both buy and sell orders, the unconditional cumulative distribution of relative limit prices decays roughly as a power law with exponent approximately –1.5. This behaviour spans more than two decades, ranging from a few ticks to about 2000 ticks. Time series of relative limit prices show interesting temporal structure, characterized by an autocorrelation function that asymptotically decays as C(τ)∼τ−0.4. Furthermore, relative limit price levels are positively correlated with and are led by price volatility. This feedback may potentially contribute to clustered volatility.

Subfields: Computer Networks and Communications, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/8c39b65ff004505882911484ee33af22629776fe

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In Computer Networks and Communications

In Economics and Econometrics

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Citations in the corpus

Testing Stylized Facts of Bitcoin Limit Order Books (2019, unknown): “We may cautiously conclude that this finding is still consistent with the literature: Several authors (for example, Bouchaud et al. (2002), Zovko and Farmer (2002) and Potters and Bouchaud (2003)) found that most orders arrive at the current bid or ask price.”
The Impact of Heterogeneous Trading Rules on the Limit Order Book and Order Flows (2007, methodology): “The estimation of the Hill exponent gives values comparable with those estimated empirically by Zovko and Farmer (2002) and Potters and Bouchaud (2003).”
A Stochastic Model for Order Book Dynamics (2010, unknown): “We then extrapolate by fitting a power law function of the formˆλ (i) = k i α (suggested by Zovko and Farmer (2002) or Bouchaud et al. (2002) ).”
An empirical behavioral model of liquidity and volatility (2007, methodology): “[70] Zovko, I.”
Limit order books (2010, methodology): “For example, the LSE’s flagship order book SETS (SETS 2011) has three distinct trading phases in each trading day.”
The Price Impact of Order Book Events (2010, unknown): “The distribution of depth across price levels often has humps, gaps and is itself a separate object of study [39, 46].”
What really causes large price changes? (2003, background): “[6, 65], which has an approximate power law tail.”
Statistical theory of the continuous double auction (2002, background): “For both the London Stock Exchange and the Paris Bourse, the distribution of the limit price relative to the best bid or ask appears to decay as a power law [23, 27].”
How Markets Slowly Digest Changes in Supply and Demand (2008, background): “stocks belonging to the ftse-100, Zumbach finds ζ ≈ 0.44 (Zumbach [2004]), while for US stocks the scaling for f is less clear (Eisler and Kertecz [2006]), with apparently two regimes, one for M > 10 B$, where ζ ≈ 0.”