Limited or Biased: Modeling Subrational Human Investors in Financial Markets

Abstract

Human decision-making in real-life deviates significantly from optimal decisions made by fully rational agents, primarily due to computational limitations or psychological biases. While existing studies in psychology and economics have discovered various types of human limitations and biases, there lacks a comprehensive framework to transfer these findings into models of subrational investors in financial markets. In this study, we introduce a unified framework that use reinforcement learning (RL) to incorporate five different aspects of human subrationality including bounded rationality, myopic behavior, prospect-biased behavior, optimistic and pessimistic behaviors. Unlike the data-driven approaches, our model is trained based on a high-fidelity multi-agent market simulator, which is not limited by the availability of subrational investor trading data. Our framework demonstrates investment behavior that is characteristic of each type of subrationality in hand-crafted market scenarios. We evaluate the Profit and Loss (PnL) of different types of subrational investors, and use SHAP value analysis to reveal the driving factors in their decision-making process. Finally, we explore the impact of subrationality on market quality regarding liquidity, volatility, and efficiency. Our experiments indicate that bounded-rational and prospect-biased human behaviors improve liquidity but diminish price efficiency, whereas human behavior influenced by myopia, optimism, and pessimism reduces market liquidity.