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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.archives-ouvertes.fr/hal-01705087
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    References listed on IDEAS

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    1. 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.
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    6. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
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    9. 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.
    10. Brad M. Barber & Yi-Tsung Lee & Yu-Jane Liu & Terrance Odean, 2009. "Just How Much Do Individual Investors Lose by Trading?," Review of Financial Studies, Society for Financial Studies, vol. 22(2), pages 609-632, February.
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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.
    2. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction Of Investor Interest: a Supervised Clustering Approach," Working Papers hal-02276055, HAL.

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