Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior

TL;DR

This paper introduces an agent-based market simulation using reinforcement learning agents that realistically reproduces market behaviors and stylized facts, providing a valuable tool for investors and regulators to analyze market dynamics and responses to shocks.

Contribution

It presents a novel RL-based agent framework for market simulation that captures realistic market phenomena and analyzes agent responses to external shocks.

Findings

Simulated markets exhibit realistic stylized facts.

RL agents adapt effectively to market impacts.

Insights into agent behavior during market shocks.

Abstract

Investors and regulators can greatly benefit from a realistic market simulator that enables them to anticipate the consequences of their decisions in real markets. However, traditional rule-based market simulators often fall short in accurately capturing the dynamic behavior of market participants, particularly in response to external market impact events or changes in the behavior of other participants. In this study, we explore an agent-based simulation framework employing reinforcement learning (RL) agents. We present the implementation details of these RL agents and demonstrate that the simulated market exhibits realistic stylized facts observed in real-world markets. Furthermore, we investigate the behavior of RL agents when confronted with external market impacts, such as a flash crash. Our findings shed light on the effectiveness and adaptability of RL-based agents within the simulation, offering insights into their response to significant market events.