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Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making

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  • Liu He

Abstract

Financial markets are influenced by human behavior that deviates from rationality due to cognitive biases. Traditional reinforcement learning (RL) models for financial decision-making assume rational agents, potentially overlooking the impact of psychological factors. This study integrates cognitive biases into RL frameworks for financial trading, hypothesizing that such models can exhibit human-like trading behavior and achieve better risk-adjusted returns than standard RL agents. We introduce biases, such as overconfidence and loss aversion, into reward structures and decision-making processes and evaluate their performance in simulated and real-world trading environments. Despite its inconclusive or negative results, this study provides insights into the challenges of incorporating human-like biases into RL, offering valuable lessons for developing robust financial AI systems.

Suggested Citation

  • Liu He, 2026. "Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making," Papers 2601.08247, arXiv.org.
    • Handle: RePEc:arx:papers:2601.08247
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    File URL: http://arxiv.org/pdf/2601.08247
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    References listed on IDEAS

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    1. Tidor-Vlad Pricope, 2021. "Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review," Papers 2106.00123, arXiv.org.
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