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The market nanostructure origin of asset price time reversal asymmetry

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  • Marcus Cordi

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

  • Damien Challet

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

  • Serge Kassibrakis

Abstract

We introduce a framework to infer lead-lag networks between the states of elements of complex systems, determined at different timescales. As such networks encode the causal structure of a system, infering lead-lag networks for many pairs of timescales provides a global picture of the mutual influence between timescales. We apply our method to two trader-resolved FX data sets and document strong and complex asymmetric influence of timescales on the structure of lead-lag networks. Expectedly, this asymmetry extends to trader activity: for institutional clients in our dataset, past activity on timescales longer than 3 hours is more correlated with future activity at shorter timescales than the opposite (Zumbach effect), while a reverse Zumbach effect is found for past timescales shorter than 3 hours; retail clients have a totally different, and much more intricate, structure of asymmetric timescale influence. The causality structures are clearly caused by markedly different behaviors of the two types of traders. Hence, market nano-structure, i.e., market dynamics at the individual trader level, provides an unprecedented insight into the causality structure of financial markets, which is much more complex than previously thought.

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  • Marcus Cordi & Damien Challet & Serge Kassibrakis, 2018. "The market nanostructure origin of asset price time reversal asymmetry," Post-Print hal-01966419, HAL.
    • Handle: RePEc:hal:journl:hal-01966419
    DOI: 10.2139/ssrn.3309170
    Note: View the original document on HAL open archive server: https://hal.science/hal-01966419
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    4. Wojciech Wisniewski & Yuri Kalnishkan & David Lindsay & Si^an Lindsay, 2024. "Temporal distribution of clusters of investors and their application in prediction with expert advice," Papers 2406.19403, arXiv.org.

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