Are LLM s Rational Investors? A Study on the Financial Bias in LLM s (2025)

Abstract: Large language models (LLMs) excel in natural language generation but also exhibit biases, particularly in gender, race, and religion, which can be amplified with widespread use. However, research on biases in specific domains, such as finance, remains limited. To address this gap, we conducted a comprehensive evaluation of 23 leading LLMs and found varying degrees of financial bias, including more pronounced biases in financial-specific LLMs (FinLLMs). In response, we propose the Financial Bias Indicators (FBI) framework, which includes components like the Bias Unveiler, Bias Detective, Bias Tracker, and Bias Antidote, designed to identify, detect, analyze, and mitigate financial biases. Our analysis explores the root causes of these biases and introduces a debiasing method based on financial causal knowledge, alongside three other debiasing techniques. For the most biased model, we successfully reduced bias by 68% according to key metrics. This study advances our understanding of LLM biases in finance and highlights the need for greater scrutiny in their application within this critical domain.

## introduction large language models have become pivotal in natural language generation tasks such as automated conversations and content creation. however, they still exhibit significant biases. when these models are widely deployed, such biases can lead to serious consequences, including racial discrimination. while existing research has extensively examined the general biases like gender @xcite , religion @xcite , and race @xcite , studies focusing on biases within specific domains remain relatively * contribute equally to this work. the first author: yuhang zhou (yuhangzhou22@m.fudan.edu.cn), yuchen ni (2230733@tongji.edu.cn) † the corresponding authors: yixin cao (yxcao@fudan.edu.cn), guangnan ye (yegn@fudan.edu.cn) scarce. to address this gap, we select the finance domain, which plays a critical role in societal development, as our research focus. leveraging insights from behavioral finance, we specifically investigated and analyzed the presence of biases in llms within the context of financial investment. recent research @xcite has shown these financial biases may lead to misunderstandings of market dynamics, adversely affecting investors and potentially causing financial market turbulence. accurately assessing and addressing these biases will positively impact the financial sector by optimizing investment decisions and promoting stable market development. however, in the realm of finllms, research has predominantly concentrated on enhancing model performance through continued pre-training or fine-tuning in domain specific corpus, e.g., bloomberggpt @xcite and silversight @xcite . in the era of llms, many methods for detecting bias have also emerged @xcite , @xcite , but directly transferring these methods to the financial domain will encounter the follow-ing four challenges: q1: how to redefine financial biases in the era of llms and achieve sufficient coverage? q2: how to design metrics and data to evaluate bias in llms? q3: how to find the mechanisms interpretability of financial bias in llms? q4: how to effectively mitigate financial bias in llms? to work towards this goal, we propose the financial bias indicators (fbi) framework to comprehensively assess financial rationality in llms. the fbi framework consists of four components: bias unveiler, bias detective, bias tracker, and bias antidote, covering the redefinition, detection, cause analysis, and mitigation of financial biases. our research is based on the theory of behavioral finance @xcite , to test financial rationality, which suggests that investors have multiple biases and deviate from rationality, providing a comprehensive perspective for research. according to this theory, financial biases fall into two main types: belief bias and risk-preference bias, with some examples illustrated in figure 1 . belief bias occurs when decisions are influenced by pre-existing beliefs rather than facts, as shown on the left, which should be eliminated to ensure fairness and accuracy. in contrast, risk-preference bias reflects a model's inherent risk tendencies, such as loss aversion, as shown on the right. this bias doesn't need elimination but should be detected and understood, as it reveals how the model approaches risk. our findings emphasize a haunting reality: all llms currently exhibit varying degrees of financial bias, making it difficult to truly apply them to financial markets. we also brought some findings, such as: 1) finllm may generate greater financial bias; 2) in the same model family, the degree of bias decreases as the model increases, but overall it does not comply with the scaling law @xcite ; 3) the financial bias is influenced by the financial cycle of the model training corpus; 4) the source of llm bias mainly depends on its level of attention to some biased information such as entities. while four mitigation methods show promise, the persistent biases in llms highlight the necessity for further explore the fundamental reasons, and improve llm robustness, fairness, and rationality in financial domain. our key contributions are summarized as follows: ## fbi: a framework for assessing llms financial bias we propose the fbi framework illustrated in figure 2 . this framework is divided into four parts belief bias poses greater harm to individuals and economy, as it stems from the model's misinterpretation of information, unlike risk-preference bias, which reflects inherent decision-making tendencies. thus, we redefined and tested both biases but focused on analyzing and mitigating belief bias. our experiments involved 23 leading llms, including general models and finllms, such as the llama @xcite and gpt (openai, 2023) families (details in appendix d). this setup enables in-depth financial bias analysis. due to space constraints, all detailed results are provided in the appendix, while the main text presents representative cases with analysis. ## bias unveiler: redefine financial bias in order to redefine financial bias detection for llms and ensure the comprehensive coverage, we categorize biases in llms within financial contexts into belief bias and risk-preference bias based on the definitions from behavioral finance @xcite , and define six relevant biases and describe on their meanings on llms. ## bias detective: design evaluation standard after redefining financial bias in llms, we further design quantitative metrics for financial bias and constructed evaluating data from sources like news and company-investor q&a interactions. these metrics were applied to evaluate 23 large models. ## bias tracker: explore mechanisms interpretability the previous text proves that all llms exhibit varying degrees of financial bias. to further explore the sources of these bias, we analyzed two potential causes: model capability and biased information attention. this approach helps determine whether the financial bias stems from the model's insufficient ability or excessive focus on biased entities, enabling more effective bias mitigation strategies. ## discussion our study using the fbi framework aids in identifying and reducing financial bias in llms, we observe that the source of model bias mainly depends on its level of attention to biased information, and discuss the findings from three aspects. ## related work due to factors such as training data and sociocultural influences, various biases are widely present in llm systems @xcite . previous studies have focused on biases related to race @xcite , gender @xcite , politics @xcite , and geography @xcite . @xcite explored social stereotypes and inequalities, using the social contact debiasing method to instruct these models with unbiased responses to prompts; @xcite proposed a framework aimed at quantifying and reducing gender bias in llm; @xcite projects multiple biases onto geographic space to detect geographic biases. however, there is still a lack of biased research on finance, which limits the development of llm in financial applications. ## conclusion our research proposes the fbi framework, a method for defining, detecting, analyzing, and mitigating the financial bias of llms in the intricate field of financial analysis, validating the capabilities and limitations of llms in financial contexts, offering reliable insights for their application in the finance sector. we rigorously examined 23 leading llms and revealed substantial differences in their financial bias. the results indicate that this bias exists in every llm, and the degree of bias in fin-llm may be more pronounced, but it can be miti-gated through slow thinking or causal knowledge enhancement. as llms evolve towards mitigating financial biases, they will help prevent market price fluctuations and bubbles, which is crucial for their reliable use in the financial domain.