Why Is Equity Order Flow so Persistent

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.

[1]  Tarun Chordia,et al.  Order imbalance and individual stock returns: theory and evidence , 2004 .

[2]  Nick Chater,et al.  Herding in humans , 2009, Trends in Cognitive Sciences.

[3]  P. Brown,et al.  The interaction between order imbalance and stock price , 1997 .

[4]  Richard Roll,et al.  Orderimbalance, Liquidity and Market Returns , 2001 .

[5]  Effects of order flow imbalance on short-horizon contrarian strategies in the Australian equity market , 2006 .

[6]  Market impact and trading protocols of hidden orders in stock markets , 2009 .

[7]  Esteban Moro,et al.  Scaling laws of strategic behavior and size heterogeneity in agent dynamics. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Jim Gatheral No-dynamic-arbitrage and market impact , 2009 .

[9]  S. Bikhchandani,et al.  You have printed the following article : A Theory of Fads , Fashion , Custom , and Cultural Change as Informational Cascades , 2007 .

[10]  Jean-Philippe Bouchaud,et al.  The price impact of order book events: market orders, limit orders and cancellations , 2009, 0904.0900.

[11]  Fabrizio Lillo,et al.  Market efficiency and the long-memory of supply and demand: is price impact variable and permanent or fixed and temporary? , 2006, physics/0602015.

[12]  C. Granger,et al.  A long memory property of stock market returns and a new model , 1993 .

[13]  Reka Albert,et al.  Mean-field theory for scale-free random networks , 1999 .

[14]  Chester Spatt,et al.  An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse , 1995 .

[15]  Jean-Philippe Bouchaud,et al.  Random walks, liquidity molasses and critical response in financial markets , 2004, cond-mat/0406224.

[16]  N. Taylor Competition on the London Stock Exchange , 2002 .

[17]  C. Holden,et al.  A comprehensive test of order choice theory: recent evidence from the NYSE , 2003 .

[18]  Models for the Impact of All Order Book Events , 2011, 1107.3364.

[19]  Richard Roll,et al.  Liquidity and Market Efficiency , 2008 .

[20]  J. Bouchaud,et al.  Fluctuations and Response in Financial Markets: The Subtle Nature of 'Random' Price Changes , 2003, cond-mat/0307332.

[21]  D. Hirshleifer,et al.  Security Analysis and Trading Patterns When Some Investors Receive Information Before Others , 1994 .

[22]  Austin Gerig,et al.  A Theory for Market Impact: How Order Flow Affects Stock Price , 2008, 0804.3818.

[23]  Yi-Tsung Lee,et al.  Order Imbalances and Market Efficiency: Evidence from the Taiwan Stock Exchange , 2001, Journal of Financial and Quantitative Analysis.

[24]  Ryuichi Yamamoto Order Aggressiveness, Pre-Trade Transparency, and Long Memory in an Order-Driven Market , 2011 .

[25]  Fabrizio Lillo,et al.  Trading activity and price impact in parallel markets: SETS vs. off-book market at the London Stock Exchange , 2011, 1102.0687.

[26]  F. Lillo,et al.  How efficiency shapes market impact , 2011, 1102.5457.

[27]  D. Scharfstein,et al.  Herd Behavior and Investment , 1990 .

[28]  R. Wermers,et al.  Mutual Fund Herding and the Impact on Stock Prices , 1998 .

[29]  F. Lillo,et al.  The Long Memory of the Efficient Market , 2003, cond-mat/0311053.

[30]  Tarun Chordia,et al.  Order Imbalance and Individual Stock Returns , 2002 .

[31]  S. Viswanathan,et al.  Do Inventories Matter in Dealership Markets? Evidence from the London Stock Exchange , 1998 .

[32]  Blake LeBaron,et al.  Order-splitting and long-memory in an order-driven market , 2010 .

[33]  A. Kyle Continuous Auctions and Insider Trading , 1985 .

[34]  Louis K.C. Chan,et al.  Institutional trades and intraday stock price behavior , 1991 .

[35]  A. Banerjee,et al.  A Simple Model of Herd Behavior , 1992 .

[36]  Gilles Teyssière,et al.  Microeconomic Models for Long Memory in the Volatility of Financial Time Series , 2001 .

[37]  J. Bouchaud,et al.  Herd Behavior and Aggregate Fluctuations in Financial Markets , 1997 .

[38]  Richard K. Lyons,et al.  A simultaneous trade model of the foreign exchange hot potato , 1997 .

[39]  Ekkehart Boehmer,et al.  Order Flow and Prices , 2008 .

[40]  Jan Beran,et al.  Statistics for long-memory processes , 1994 .

[41]  André Orléan Bayesian interactions and collective dynamics of opinion: Herd behavior and mimetic contagion , 1995 .

[42]  A. Banerjee,et al.  The Economics of Rumours , 1993 .

[43]  Spyros Skouras,et al.  Markets Change Every Day: Evidence from the Memory of Trade Direction , 2011 .

[44]  Esteban Moro,et al.  Market impact and trading profile of hidden orders in stock markets. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[45]  Jean-Philippe Bouchaud,et al.  Anomalous Price Impact and the Critical Nature of Liquidity in Financial Markets , 2011, 1105.1694.

[46]  C. Holden,et al.  Order Dynamics: Recent Evidence from the NYSE , 2007 .

[47]  Blake LeBaron,et al.  The Impact of Imitation on Long Memory in an Order-Driven Market , 2008 .

[48]  Avanidhar Subrahmanyam,et al.  Evidence on the Speed of Convergence to Market Efficiency , 2001 .

[49]  F. Lillo,et al.  A Theory for Long-Memory in Supply and Demand , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[50]  Gur Huberman,et al.  Price Manipulation and Quasi-Arbitrage , 2004 .

[51]  B. LeBaron,et al.  Long-memory in an order-driven market , 2007 .

[52]  Juhani T. Linnainmaa,et al.  Lack of Anonymity and the Inference from Order Flow , 2011 .

[53]  Giulia Iori,et al.  A Microsimulation of Traders Activity in the Stock Market: The Role of Heterogeneity, Agents' Interactions and Trade Frictions , 1999, adap-org/9905005.

[54]  G. Iori,et al.  The role of communication and imitation in limit order markets , 2009 .

[55]  F. Lillo,et al.  The adaptive nature of liquidity taking in limit order books , 2014, 1403.0842.

[56]  J. Bouchaud,et al.  How Markets Slowly Digest Changes in Supply and Demand , 2008, 0809.0822.

[57]  Louis K.C. Chan,et al.  The Behavior of Stock Prices Around Institutional Trades , 1993 .

[58]  W. Y. Yeo Serial Correlation in the Limit Order Flow: Causes and Impact , 2008 .

[59]  H. Simon,et al.  ON A CLASS OF SKEW DISTRIBUTION FUNCTIONS , 1955 .

[60]  Jón Dańıelsson,et al.  Liquidity determination in an order-driven market , 2012 .

[61]  Fabrizio Lillo,et al.  How does the market react to your order flow? , 2011, 1104.0587.

[62]  Josef Lakonishok,et al.  The impact of institutional trading on stock prices * , 1991 .