  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.
