From Data to Dollars: Exposing Product Bias in LLM Investment Recommendation
Abstract: 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 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.
Paper Type: Long
Research Area: Ethics, Bias, and Fairness
Research Area Keywords: model bias evaluation, model bias mitigation
Contribution Types: Data analysis
Languages Studied: English
Submission Number: 8380
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