Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs

• URL: http://arxiv.org/abs/2402.12713v2
• Date: Mon, 1 Jul 2024 15:42:15 GMT
• Title: Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs
• Authors: Yuhang Zhou, Yuchen Ni, Yunhui Gan, Zhangyue Yin, Xiang Liu, Jian Zhang, Sen Liu, Xipeng Qiu, Guangnan Ye, Hongfeng Chai,
• Abstract summary: Large Language Models (LLMs) are increasingly adopted in financial analysis for interpreting complex market data and trends. Financial Bias Indicators (FBI) is a framework with components like Bias Unveiler, Bias Detective, Bias Tracker, and Bias Antidote. We evaluate 23 leading LLMs and propose a de-biasing method based on financial causal knowledge.
• Score: 44.53203911878139
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly adopted in financial analysis for interpreting complex market data and trends. However, their use is challenged by intrinsic biases (e.g., risk-preference bias) and a superficial understanding of market intricacies, necessitating a thorough assessment of their financial insight. To address these issues, we introduce Financial Bias Indicators (FBI), a framework with components like Bias Unveiler, Bias Detective, Bias Tracker, and Bias Antidote to identify, detect, analyze, and eliminate irrational biases in LLMs. By combining behavioral finance principles with bias examination, we evaluate 23 leading LLMs and propose a de-biasing method based on financial causal knowledge. Results show varying degrees of financial irrationality among models, influenced by their design and training. Models trained specifically on financial datasets may exhibit more irrationality, and even larger financial language models (FinLLMs) can show more bias than smaller, general models. We utilize four prompt-based methods incorporating causal debiasing, effectively reducing financial biases in these models. This work enhances the understanding of LLMs' bias in financial applications, laying the foundation for developing more reliable and rational financial analysis tools.

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