揭示大模型在投资分析中的固有偏见，发现其偏好科技股和逆势操作。
Your AI, Not Your View: The Bias of LLMs in Investment Analysis
- 设计实验框架，通过正反论据测试模型隐含投资倾向。
- 多数模型倾向科技股、大盘股及逆势策略，且易陷入确认偏误。
- 适合关注AI投研风险的金融从业者与模型评估研究人员。
在金融领域，大语言模型（LLMs）常因预训练知识与实时市场数据之间的差异而产生知识冲突。这类冲突在实际投资服务中尤为严重，模型的内在偏见可能与机构目标不一致，导致不可靠的建议。尽管存在此风险，大模型的固有投资偏见仍缺乏深入研究。本文提出一种实验框架，用于探究此类冲突场景下的涌现行为，并对基于大模型的投资分析偏见进行量化分析。通过设定平衡与不平衡论据的假设情景，我们提取模型的潜在偏见并测量其持续性。分析聚焦于行业、市值和动量因素，结果表明各模型表现出独特的偏见特征。多数模型普遍存在偏好科技股、大盘股及逆势策略的倾向，且这些基础偏见常演变为确认偏误，使模型在面对越来越多反证时仍坚持初始判断。公开的排行榜可在此处获取：https://linqalpha.com/leaderboard
原文摘要 · Abstract (English)
In finance, Large Language Models (LLMs) face frequent knowledge conflicts arising from discrepancies between their pre-trained parametric knowledge and real-time market data. These conflicts are especially problematic in real-world investment services, where a model's inherent biases can misalign with institutional objectives, leading to unreliable recommendations. Despite this risk, the intrinsic investment biases of LLMs remain underexplored. We propose an experimental framework to investigate emergent behaviors in such conflict scenarios, offering a quantitative analysis of bias in LLM-based investment analysis. Using hypothetical scenarios with balanced and imbalanced arguments, we extract the latent biases of models and measure their persistence. Our analysis, centered on sector, size, and momentum, reveals distinct, model-specific biases. Across most models, a tendency to prefer technology stocks, large-cap stocks, and contrarian strategies is observed. These foundational biases often escalate into confirmation bias, causing models to cling to initial judgments even when faced with increasing counter-evidence. A public leaderboard benchmarking bias across a broader set of models is available at https://linqalpha.com/leaderboard
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