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

ArXiv ID: 2403.19781 “View on arXiv”

Authors: Unknown

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.

Keywords: agent-based simulation, reinforcement learning, market impact, flash crash, stylized facts, General (Market Simulation)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced mathematical concepts like Markov Decision Processes (MDP) and the Proximal Policy Optimization (PPO) algorithm, evidenced by detailed formal definitions and equations, which drives the high math complexity score. However, the empirical rigor is limited as the study is framed within a simulation environment using synthetic agents rather than backtesting on real financial data, with no mention of live deployment or statistical validation against market outcomes.

  flowchart TD
    A["Research Goal:<br>Develop an RL-based Agent-Based<br>Market Simulator to capture<br>dynamic market behavior &<br>realistic stylized facts"] --> B

    subgraph B ["Methodology"]
        direction TB
        B1["Define RL Agent<br>Architecture & Parameters"] --> B2["Train RL Agents<br>in Market Environment"] --> B3["Deploy Trained Agents<br>into Agent-Based Model"]
    end

    B --> C

    subgraph C ["Data & Inputs"]
        C1["Market Data for<br>Environment State"]
        C2["Reward Functions &<br>Trading Rules"]
    end

    C --> D["Computational Process:<br>Multi-Agent Simulation<br>with RL Decision Logic"]

    D --> E

    subgraph E ["Key Findings/Outcomes"]
        direction TB
        E1["Simulated market exhibits<br>realistic stylized facts<br>e.g., volatility clustering, fat tails"]
        E2["RL agents adapt dynamically<br>to external shocks e.g., Flash Crash"]
        E3["Framework offers effective<br>tool for anticipating policy<br>& regulatory impacts"]
    end