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Why is order flow so persistent?


  • Bence Toth
  • Imon Palit
  • Fabrizio Lillo
  • J. Doyne Farmer


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.

Suggested Citation

  • Bence Toth & Imon Palit & Fabrizio Lillo & J. Doyne Farmer, 2011. "Why is order flow so persistent?," Papers 1108.1632,, revised Nov 2014.
  • Handle: RePEc:arx:papers:1108.1632

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    References listed on IDEAS

    1. Jean-Philippe Bouchaud & Yuval Gefen & Marc Potters & Matthieu Wyart, 2003. "Fluctuations and response in financial markets: the subtle nature of `random' price changes," Papers cond-mat/0307332,, revised Aug 2003.
    2. Lakonishok, Josef & Shleifer, Andrei & Vishny, Robert W., 1992. "The impact of institutional trading on stock prices," Journal of Financial Economics, Elsevier, vol. 32(1), pages 23-43, August.
    3. Jean-Philippe Bouchaud & Yuval Gefen & Marc Potters & Matthieu Wyart, 2004. "Fluctuations and response in financial markets: the subtle nature of 'random' price changes," Quantitative Finance, Taylor & Francis Journals, vol. 4(2), pages 176-190.
    4. Rama Cont & Jean-Philippe Bouchaud, 1997. "Herd behavior and aggregate fluctuations in financial markets," Science & Finance (CFM) working paper archive 500028, Science & Finance, Capital Fund Management.
    5. R. Yamamoto & B. LeBaron, 2010. "Order-splitting and long-memory in an order-driven market," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(1), pages 51-57, January.
    6. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    7. Orlean, Andre, 1995. "Bayesian interactions and collective dynamics of opinion: Herd behavior and mimetic contagion," Journal of Economic Behavior & Organization, Elsevier, vol. 28(2), pages 257-274, October.
    8. Biais, Bruno & Hillion, Pierre & Spatt, Chester, 1995. " An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse," Journal of Finance, American Finance Association, vol. 50(5), pages 1655-1689, December.
    9. Jean-Philippe Bouchaud & Julien Kockelkoren & Marc Potters, 2006. "Random walks, liquidity molasses and critical response in financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 6(2), pages 115-123.
    10. Hens, Thorsten & Schenk-Hoppe, Klaus Reiner (ed.), 2009. "Handbook of Financial Markets: Dynamics and Evolution," Elsevier Monographs, Elsevier, edition 1, number 9780123742582.
    11. F. Lillo & Szabolcs Mike & J. Doyne Farmer, 2004. "A theory for long-memory in supply and demand," Papers cond-mat/0412708,, revised Mar 2005.
    12. Lillo Fabrizio & Farmer J. Doyne, 2004. "The Long Memory of the Efficient Market," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(3), pages 1-35, September.
    13. Jean-Philippe Bouchaud & J. Doyne Farmer & Fabrizio Lillo, 2008. "How markets slowly digest changes in supply and demand," Papers 0809.0822,
    14. Abhijit V. Banerjee, 1993. "The Economics of Rumours," Review of Economic Studies, Oxford University Press, vol. 60(2), pages 309-327.
    15. Iori, Giulia, 2002. "A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interactions and trade frictions," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 269-285, October.
    16. Cont, Rama & Bouchaud, Jean-Philipe, 2000. "Herd Behavior And Aggregate Fluctuations In Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 4(02), pages 170-196, June.
    17. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    18. B. Tóth & Z. Eisler & F. Lillo & J. Kockelkoren & J.-P. Bouchaud & J.D. Farmer, 2012. "How does the market react to your order flow?," Quantitative Finance, Taylor & Francis Journals, vol. 12(7), pages 1015-1024, May.
    19. Austin Gerig, 2008. "A Theory for Market Impact: How Order Flow Affects Stock Price," Papers 0804.3818,, revised Jul 2008.
    20. Andrew Ellul, 2003. "A Comprehensive Test of Order Choice Theory:Recent Evidence from the NYSE," FMG Discussion Papers dp471, Financial Markets Group.
    21. Russ Wermers, 1999. "Mutual Fund Herding and the Impact on Stock Prices," Journal of Finance, American Finance Association, vol. 54(2), pages 581-622, April.
    22. J. Doyne Farmer & Austin Gerig & Fabrizio Lillo & Henri Waelbroeck, 2011. "How efficiency shapes market impact," Papers 1102.5457,, revised Sep 2013.
    23. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    24. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    25. Chan, Louis K. C. & Lakonishok, Josef, 1993. "Institutional trades and intraday stock price behavior," Journal of Financial Economics, Elsevier, vol. 33(2), pages 173-199, April.
    26. Chan, Louis K C & Lakonishok, Josef, 1995. " The Behavior of Stock Prices around Institutional Trades," Journal of Finance, American Finance Association, vol. 50(4), pages 1147-1174, September.
    27. Blake LeBaron & Ryuichi Yamamoto, 2008. "The Impact of Imitation on Long Memory in an Order-Driven Market," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 34(4), pages 504-517.
    28. G. Tedeschi & G. Iori & M. Gallegati, 2009. "The role of communication and imitation in limit order markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 489-497, October.
    29. Esteban Moro & Javier Vicente & Luis G. Moyano & Austin Gerig & J. Doyne Farmer & Gabriella Vaglica & Fabrizio Lillo & Rosario N. Mantegna, 2009. "Market impact and trading profile of large trading orders in stock markets," Papers 0908.0202,
    30. LeBaron, Blake & Yamamoto, Ryuichi, 2007. "Long-memory in an order-driven market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 85-89.
    31. Zoltan Eisler & Jean-Philippe Bouchaud & Julien Kockelkoren, 2011. "Models for the impact of all order book events," Papers 1107.3364,
    32. Bence Toth & Yves Lemperiere & Cyril Deremble & Joachim de Lataillade & Julien Kockelkoren & Jean-Philippe Bouchaud, 2011. "Anomalous price impact and the critical nature of liquidity in financial markets," Papers 1105.1694,, revised Nov 2011.
    33. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 107(3), pages 797-817.
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    Cited by:

    1. Aur'elien Alfonsi & Pierre Blanc, 2014. "Dynamic optimal execution in a mixed-market-impact Hawkes price model," Papers 1404.0648,, revised Jun 2015.
    2. repec:eee:econom:v:201:y:2017:i:2:p:367-383 is not listed on IDEAS
    3. Darolles, Serge & Le Fol, Gaëlle & Mero, Gulten, 2017. "Mixture of distribution hypothesis: Analyzing daily liquidity frictions and information flows," Journal of Econometrics, Elsevier, vol. 201(2), pages 367-383.

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