arXiv 13 Jan 2026 · Machine Learning
arXiv:2601.08247 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rasa Kanapickienė, Deimantė Vasiliauskaitė, G. Keliuotytė-Staniulėni… (2024) A comprehensive review of behavioral biases in financial decision-making: from classical finance to behavioral finance perspecti… | 0.843 | 3 | 3 | 100% |
| 2 | Alara Uyvar Kara (2025) The role of cognitive biases in financial decision-making | 0.843 | 3 | 3 | 100% |
| 3 | Tidor-Vlad Pricope (2021) Deep reinforcement learning in quantitative algorithmic trading: A review | 0.843 | 3 | 3 | 100% |
| 4 | Safiye Turgay and Abdülkadir Aydın (2025) Improving decision making under uncertainty with data analytics: Bayesian networks, reinforcement learning, and risk perception… | 0.843 | 3 | 3 | 100% |
| 5 | Maochun Xu, Zixun Lan, Zheng Tao, Jiawei Du, and Zongao Ye (2023) Deep reinforcement learning for quantitative trading | 0.843 | 3 | 3 | 100% |
| 6 | Bingqing Wang (2023) The impact of anchoring bias on financial decision-making: Exploring cognitive biases in decision-making processes | 0.644 | 2 | 2 | 100% |
| 7 | Mohd Afjal (2024) Evolving trends, limitations, and ethical considerations in ai-driven conversational interfaces: assessing chatgpt's impact on h… | 0.405 | 1 | 1 | 100% |
| 8 | Sounak Banerjee, Daphne Cornelisse, Deepak E. Gopinath, Emily Sumner… (2025) Estimating cognitive biases with attention-aware inverse planning | 0.405 | 1 | 1 | 100% |
| 9 | Patrick Cheridito, Jean-Loup Dupret, and Zhexin Wu (2025) Abides-marl: A multi-agent reinforcement learning environment for endogenous price formation and execution in a limit order book | 0.405 | 1 | 1 | 100% |
| 10 | Sara Garg (2025) Artificial intelligence in strategic business decision-making: Challenges, biases, and ethical considerations in microfinance in… | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 15 scored citations.