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Unveiling the relation between herding and liquidity with trader lead-lag networks

Author

Listed:
  • Carlo Campajola
  • Fabrizio Lillo
  • Daniele Tantari

Abstract

We propose a method to infer lead-lag networks of traders from the observation of their trade record as well as to reconstruct their state of supply and demand when they do not trade. The method relies on the Kinetic Ising model to describe how information propagates among traders, assigning a positive or negative "opinion" to all agents about whether the traded asset price will go up or down. This opinion is reflected by their trading behavior, but whenever the trader is not active in a given time window, a missing value will arise. Using a recently developed inference algorithm, we are able to reconstruct a lead-lag network and to estimate the unobserved opinions, giving a clearer picture about the state of supply and demand in the market at all times. We apply our method to a dataset of clients of a major dealer in the Foreign Exchange market at the 5 minutes time scale. We identify leading players in the market and define a herding measure based on the observed and inferred opinions. We show the causal link between herding and liquidity in the inter-dealer market used by dealers to rebalance their inventories.

Suggested Citation

  • 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.
  • Handle: RePEc:arx:papers:1909.10807
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    References listed on IDEAS

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    Cited by:

    1. Piero Mazzarisi & Silvia Zaoli & Carlo Campajola & Fabrizio Lillo, 2020. "Tail Granger causalities and where to find them: extreme risk spillovers vs. spurious linkages," Papers 2005.01160, arXiv.org, revised May 2021.
    2. Zhu, Huimin & Xiao, Xinping & Kang, Yuxiao & Kong, Dekai, 2022. "Lead-lag grey forecasting model in the new community group buying retailing," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    3. Carlo Campajola & Domenico Di Gangi & Fabrizio Lillo & Daniele Tantari, 2020. "Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model," Papers 2007.15545, arXiv.org, revised Aug 2021.
    4. Mazzarisi, Piero & Zaoli, Silvia & Campajola, Carlo & Lillo, Fabrizio, 2020. "Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    5. Yongli Li & Tianchen Wang & Baiqing Sun & Chao Liu, 2022. "Detecting the lead–lag effect in stock markets: definition, patterns, and investment strategies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-36, December.

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