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

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

URL: http://arxiv.org/abs/2403.19781v1
Date: Thu, 28 Mar 2024 19:06:50 GMT
Title: Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior
Authors: Zhiyuan Yao, Zheng Li, Matthew Thomas, Ionut Florescu,
Abstract summary: Investors and regulators can benefit from a realistic market simulator that enables them to anticipate the consequences of their decisions in real markets. Traditional rule-based market simulators often fall short in accurately capturing the dynamic behavior of market participants. In this study, we explore an agent-based simulation framework employing reinforcement learning (RL) agents.
Score: 7.8082951131908604
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
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.

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