Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs
This work addresses the issue of unreliable financial analysis tools for users in finance by providing a method to mitigate biases, though it is incremental as it builds on existing bias detection and behavioral finance principles.
The study tackled the problem of financial biases in Large Language Models (LLMs) used for financial analysis by introducing a framework called Financial Bias Indicators (FBI) to detect and reduce these biases, showing that models trained on financial datasets can exhibit more irrationality and that prompt-based methods effectively reduced biases in 23 evaluated LLMs.
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.