Large language models (LLMs), as a new generation of recommendation engines, possess powerful summarization and data analysis capabilities, surpassing traditional recommendation systems in both scope and performance. One promising application is investment recommendation. In this paper, we reveal a novel product bias in LLM investment recommendation, where LLMs exhibit systematic preferences for specific products. Such preferences can subtly influence user investment decisions, potentially leading to inflated valuations of products and financial bubbles, posing risks to both individual investors and market stability. To comprehensively study the product bias, we develop an automated pipeline to create a dataset of 567,000 samples across five asset classes (stocks, mutual funds, cryptocurrencies, savings, and portfolios). With this dataset, we present the bf first study on product bias in LLM investment recommendations. Our findings reveal that LLMs exhibit clear product preferences, such as certain stocks (e.g., `AAPL' from Apple and `MSFT' from Microsoft). Notably, this bias persists even after applying debiasing techniques. We urge AI researchers to take heed of the product bias in LLM investment recommendations and its implications, ensuring fairness and security in the digital space and market.

本研究揭示了大型语言模型（LLMs）在投资推荐中存在的产品偏差，即LLMs对特定产品表现出系统性的偏好。这种偏好可能影响用户的投资决策，导致产品估值膨胀并可能引发金融泡沫，从而对投资者及市场稳定构成风险。我们开发了一个自动化流程，创建了覆盖五类资产（股票、共同基金、加密货币、储蓄和投资组合）的567,000个样本的数据集，首次系统研究了这一产品偏差。

揭示大型语言模型投资推荐中的产品偏差