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

TL;DR

This paper uses a multi-agent reinforcement learning model to analyze how different regulatory changes, like order book tick sizes and trading frequencies, affect stock market quality.

Contribution

It introduces a novel agent-based model with reinforcement learning agents to study systemic impacts of regulatory variations on market quality.

Findings

Higher trading frequencies improve market quality.

Smaller order book tick sizes do not necessarily enhance market quality.

Larger metaorders negatively impact market quality.

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.