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

arXiv:2403.19781v1 Announce Type: cross
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 …

abstract agent arxiv behavior benefit consequences cs.lg cs.ma decisions dynamic facts however investors market markets q-fin.tr regulators reinforcement reinforcement learning simulation simulator them type