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Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective

Author

Listed:
  • Johann Lussange

    (LNC2 - Laboratoire de Neurosciences Cognitives & Computationnelles - DEC - Département d'Etudes Cognitives - ENS Paris - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INSERM - Institut National de la Santé et de la Recherche Médicale)

  • Boris Gutkin

    (LNC2 - Laboratoire de Neurosciences Cognitives & Computationnelles - DEC - Département d'Etudes Cognitives - ENS Paris - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INSERM - Institut National de la Santé et de la Recherche Médicale)

Abstract

Recent technological developments have changed the fundamental ways stock markets function, bringing regulatory instances to assess the benefits of these developments. In parallel, the ongoing machine learning revolution and its multiple applications to trading can now be used to design a next generation of financial models, and thereby explore the systemic complexity of financial stock markets in new ways. We here follow on a previous groundwork, where we designed and calibrated a novel agent-based model stock market simulator, where each agent autonomously learns to trade by reinforcement learning. In this Paper, we now study the predictions of this model from a regulator's perspective. In particular, we focus on how the market quality is impacted by smaller order book tick sizes, increasingly larger metaorders, and higher trading frequencies, respectively. Under our model assumptions, we find that the market quality benefits from the latter, but not from the other two trends.

Suggested Citation

  • Johann Lussange & Boris Gutkin, 2023. "Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective," Working Papers hal-04273903, HAL.
    • Handle: RePEc:hal:wpaper:hal-04273903
    DOI: 10.48550/arXiv.2302.04184
    Note: View the original document on HAL open archive server: https://hal.science/hal-04273903
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