# Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets

arXiv: https://arxiv.org/abs/2210.08569 · HTML: https://mlsanity.com/paper/2210.08569/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 75 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [When to Trust Your Model: Model-Based Policy Optimization](https://mlsanity.com/paper/1906.08253/) (2019)
- [Risk-sensitive Reinforcement Learning](https://mlsanity.com/paper/1311.2097/) (2013)
- [Hyperbolic Discounting and Learning over Multiple Horizons](https://mlsanity.com/paper/1902.06865/) (2019)
- [Market Making via Reinforcement Learning](https://mlsanity.com/paper/1804.04216/) (2018)
- [Learning the Preferences of Ignorant, Inconsistent Agents](https://mlsanity.com/paper/1512.05832/) (2015)
- [Get Real: Realism Metrics for Robust Limit Order Book Market Simulations](https://mlsanity.com/paper/1912.04941/) (2019)
- [Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior](https://mlsanity.com/paper/1805.08010/) (2018)
- [ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets](https://mlsanity.com/paper/2110.14771/) (2021)
- [ABIDES: Towards High-Fidelity Market Simulation for AI Research](https://mlsanity.com/paper/1904.12066/) (2019)
- [Learning to simulate realistic limit order book markets from data as a World Agent](https://mlsanity.com/paper/2210.09897/) (2022)
- [Human irrationality: both bad and good for reward inference](https://mlsanity.com/paper/2111.06956/) (2021)
- [How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?](https://mlsanity.com/paper/1906.12010/) (2019)
- [Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response](https://mlsanity.com/paper/1906.02312/) (2019)
- [Profit equitably: An investigation of market maker's impact on equitable outcomes](https://mlsanity.com/paper/2111.00094/) (2021)

## Built on by (most cited first, 2 of 2)

- [LLM-driven Imitation of Subrational Behavior : Illusion or Reality?](https://mlsanity.com/paper/2402.08755/) (2024)
- [K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs](https://mlsanity.com/paper/2302.11996/) (2023)

## Abstract (arXiv)

Human decision-making in real-life deviates significantly from the optimal decisions made by fully rational agents, primarily due to computational limitations or psychological biases. While existing studies in behavioral finance have discovered various aspects of human sub-rationality, there lacks a comprehensive framework to transfer these findings into an adaptive human model applicable across diverse financial market scenarios. In this study, we introduce a flexible model that incorporates five different aspects of human sub-rationality using reinforcement learning. Our model is trained using a high-fidelity multi-agent market simulator, which overcomes limitations associated with the scarcity of labeled data of individual investors. We evaluate the behavior of sub-rational human investors using hand-crafted market scenarios and SHAP value analysis, showing that our model accurately reproduces the observations in the previous studies and reveals insights of the driving factors of human behavior. Finally, we explore the impact of sub-rationality on the investor's Profit and Loss (PnL) and market quality. Our experiments reveal 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.