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Generating realistic metaorders from public data

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  • Guillaume Maitrier
  • Gr'egoire Loeper
  • Jean-Philippe Bouchaud

Abstract

This paper introduces a novel algorithm for generating realistic metaorders from public trade data, addressing a longstanding challenge in price impact research that has traditionally relied on proprietary datasets. Our method effectively recovers all established stylized facts of metaorders impact, such as the Square Root Law, the concave profile during metaorder execution, and the post-execution decay. This algorithm not only overcomes the dependence on proprietary data, a major barrier to research reproducibility, but also enables the creation of larger and more robust datasets that may increase the quality of empirical studies. Our findings strongly suggest that average realized short-term price impact is not due to information revelation (as in the Kyle framework) but has a mechanical origin which could explain the universality of the Square Root Law.

Suggested Citation

  • Guillaume Maitrier & Gr'egoire Loeper & Jean-Philippe Bouchaud, 2025. "Generating realistic metaorders from public data," Papers 2503.18199, arXiv.org, revised Apr 2025.
    • Handle: RePEc:arx:papers:2503.18199
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    References listed on IDEAS

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    1. Louis Saddier & Matteo Marsili, 2023. "A Bayesian theory of market impact," Papers 2303.08867, arXiv.org, revised May 2024.
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