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Statistically validated leadlag networks and inventory prediction in the foreign exchange market

Author

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  • Damien Challet

    (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec)

  • Rémy Chicheportiche

    (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec)

  • Mehdi Lallouache

    (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec)

  • Serge Kassibrakis

Abstract

We introduce a method to infer lead-lag networks of agents' actions in complex systems. These networks open the way to both microscopic and macroscopic states prediction in such systems. We apply this method to trader-resolved data in the foreign exchange market. We show that these networks are remarkably persistent, which explains why and how order flow prediction is possible from trader-resolved data. In addition, if traders' actions depend on past prices, the evolution of the average price paid by traders may also be predictable. Using random forests, we verify that the predictability of both the sign of order flow and the direction of average transaction price is strong for retail investors at an hourly time scale, which is of great relevance to brokers and order matching engines. Finally, we argue that the existence of trader lead-lag networks explains in a self-referential way why a given trader becomes active, which is in line with the fact that most trading activity has an endogenous origin.

Suggested Citation

  • Damien Challet & Rémy Chicheportiche & Mehdi Lallouache & Serge Kassibrakis, 2018. "Statistically validated leadlag networks and inventory prediction in the foreign exchange market," Post-Print hal-01705087, HAL.
  • Handle: RePEc:hal:journl:hal-01705087
    DOI: 10.1142/S0219525918500194
    Note: View the original document on HAL open archive server: https://hal.science/hal-01705087
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    1. Stephen J. Hardiman & Nicolas Bercot & Jean-Philippe Bouchaud, 2013. "Critical reflexivity in financial markets: a Hawkes process analysis," Papers 1302.1405, arXiv.org, revised Jun 2013.
    2. Chester Curme & Michele Tumminello & Rosario N. Mantegna & H. Eugene Stanley & Dror Y. Kenett, 2015. "Emergence of statistically validated financial intraday lead-lag relationships," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1375-1386, August.
    3. Mehdi Lallouache & Frédéric Abergel, 2014. "Tick size reduction and price clustering in a FX order book," Post-Print hal-01006414, HAL.
    4. Ron Kaniel & Gideon Saar & Sheridan Titman, 2008. "Individual Investor Trading and Stock Returns," Journal of Finance, American Finance Association, vol. 63(1), pages 273-310, February.
    5. Harris, Larry, 2002. "Trading and Exchanges: Market Microstructure for Practitioners," OUP Catalogue, Oxford University Press, number 9780195144703.
    6. Vladimir Filimonov & Didier Sornette, 2012. "Quantifying Reflexivity in Financial Markets: Towards a Prediction of Flash Crashes," Swiss Finance Institute Research Paper Series 12-02, Swiss Finance Institute.
    7. Michele Tumminello & Salvatore Miccichè & Fabrizio Lillo & Jyrki Piilo & Rosario N Mantegna, 2011. "Statistically Validated Networks in Bipartite Complex Systems," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    8. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
    9. 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.
    10. Florent Gallien & Serge Kassibrakis & Semyon Malamud & Filippo Passerini, 2016. "Managing Inventory with Proportional Transaction Costs," Swiss Finance Institute Research Paper Series 16-48, Swiss Finance Institute.
    11. Gillespie, Colin S., 2015. "Fitting Heavy Tailed Distributions: The poweRlaw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i02).
    12. Ilija I. Zovko & J. Doyne Farmer, 2007. "Correlations and clustering in the trading of members of the London Stock Exchange," Papers 0709.3261, arXiv.org.
    13. Jean-Philippe Bouchaud & Damien Challet, 2016. "Why have asset price properties changed so little in 200 years," Papers 1605.00634, arXiv.org.
    14. Chou, Cheng & Chu, Chia-Shang J., 2010. "Testing independence of two autocorrelated binary time series," Statistics & Probability Letters, Elsevier, vol. 80(1), pages 69-75, January.
    15. Vladimir Filimonov & Didier Sornette, 2012. "Quantifying reflexivity in financial markets: towards a prediction of flash crashes," Papers 1201.3572, arXiv.org, revised Apr 2012.
    16. Boudoukh, Jacob & Richardson, Matthew P & Whitelaw, Robert F, 1994. "A Tale of Three Schools: Insights on Autocorrelations of Short-Horizon Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 7(3), pages 539-573.
    17. Stephen Hardiman & Nicolas Bercot & Jean-Philippe Bouchaud, 2013. "Critical reflexivity in financial markets: a Hawkes process analysis," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(10), pages 1-9, October.
    18. Mark Grinblatt & Matti Keloharju, 2009. "Sensation Seeking, Overconfidence, and Trading Activity," Journal of Finance, American Finance Association, vol. 64(2), pages 549-578, April.
    19. Mehdi Lallouache & Fr'ed'eric Abergel, 2013. "Tick Size Reduction and Price Clustering in a FX Order Book," Papers 1307.5440, arXiv.org, revised Sep 2014.
    20. Eric K. Kelley & Paul C. Tetlock, 2013. "How Wise Are Crowds? Insights from Retail Orders and Stock Returns," Journal of Finance, American Finance Association, vol. 68(3), pages 1229-1265, June.
    21. Lallouache, Mehdi & Abergel, Frédéric, 2014. "Tick size reduction and price clustering in a FX order book," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 488-498.
    22. Jegadeesh, Narasimhan & Titman, Sheridan, 1995. "Overreaction, Delayed Reaction, and Contrarian Profits," The Review of Financial Studies, Society for Financial Studies, vol. 8(4), pages 973-993.
    23. David Lamper & Sam Howison & Neil Johnson, 2001. "Predictability of large future changes in a competitive evolving population," OFRC Working Papers Series 2001mf01, Oxford Financial Research Centre.
    24. Grinblatt, Mark & Keloharju, Matti, 2000. "The investment behavior and performance of various investor types: a study of Finland's unique data set," Journal of Financial Economics, Elsevier, vol. 55(1), pages 43-67, January.
    25. Brad M. Barber & Yi-Tsung Lee & Yu-Jane Liu & Terrance Odean, 2009. "Just How Much Do Individual Investors Lose by Trading?," The Review of Financial Studies, Society for Financial Studies, vol. 22(2), pages 609-632, February.
    26. L. Kullmann & J. Kertesz & K. Kaski, 2002. "Time dependent cross correlations between different stock returns: A directed network of influence," Papers cond-mat/0203256, arXiv.org, revised May 2002.
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    Cited by:

    1. Carlo Campajola & Fabrizio Lillo & Daniele Tantari, 2019. "Unveiling the relation between herding and liquidity with trader lead-lag networks," Papers 1909.10807, arXiv.org, revised Mar 2020.
    2. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.
    3. Challet, Damien & Bongiorno, Christian & Pelletier, Guillaume, 2021. "Financial factors selection with knockoffs: Fund replication, explanatory and prediction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    4. Marcus Cordi & Serge Kassibrakis & Damien Challet, 2018. "The market nanostructure origin of asset price time reversal asymmetry," Working Papers hal-01966419, HAL.
    5. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.
    6. Baltakienė, Margarita & Kanniainen, Juho & Baltakys, Kęstutis, 2021. "Identification of information networks in stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    7. Marcus Cordi & Damien Challet & Serge Kassibrakis, 2021. "The market nanostructure origin of asset price time reversal asymmetry," Quantitative Finance, Taylor & Francis Journals, vol. 21(2), pages 295-304, February.
    8. Chen, Zhang-HangJian & Wu, Wang-Long & Li, Sai-Ping & Bao, Kun & Koedijk, Kees G., 2024. "Social media information diffusion and excess stock returns co-movement," International Review of Financial Analysis, Elsevier, vol. 91(C).

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    Keywords

    lead-lag networks; trader-resolved data; foreign exchange; prediction; inventory management;
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