Other recent work documents systematic biases in LLM-based financial analysis, including foreign bias in cross-border predictions (Cao, Wang, and Xiang, 2025) and sector and size biases in investment recommendations (Choi, Lopez-Lira, and Lee, 2025). Building on this emerging literature, four potential channels are especially relevant for investment practitioners:
1. Size bias: Large firms receive more analyst coverage and media attention, therefore LLMs have more textual information about them, which can translate into more confident and often more optimistic forecasts. Smaller firms, by contrast, may be treated conservatively simply because less information exists in the training data.
2. Sector bias: Technology and financial stocks dominate business news and online discussions. If AI models internalize this optimism, they may systematically assign higher expected returns or more favorable recommendations to these sectors, regardless of valuation or cycle risk.
3. Volume bias: Highly liquid stocks generate more trading commentary, news flow, and price discussion. AI models may implicitly prefer these names because they appear more frequently in training data.
4. Attention bias: Stocks with strong social media presence or high search activity tend to attract disproportionate investor attention. AI models trained on internet content may inherit this hype effect, reinforcing popularity rather than fundamentals.
These biases matter because they can distort both idea generation and risk allocation. If AI tools overweight familiar names, investors may unknowingly reduce diversification and overlook under-researched opportunities.