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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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    Cited by:

    1. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.
    2. 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).
    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. 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.
    5. Marcus Cordi & Serge Kassibrakis & Damien Challet, 2018. "The market nanostructure origin of asset price time reversal asymmetry," Working Papers hal-01966419, HAL.
    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. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.

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    Keywords

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