Is the shift from keyword-based search engines to artificial intelligence (AI) as implemented with large language models (LLM) affecting typical investor behavior? In the May 2026 revision of their paper entitled "The Double-Edged Mind: How LLMs Expand Stock Market Participation Yet Strengthen Confirmation-Seeking", Cara Damm, Kevin Bauer, Florian Hett and Loriana Pelizzon address this question via an online experiment that randomly assigns participants to groups of volunteers who have access to keyword-based search engines, an LLM-based chatbot (unlabeled Gemini 2.0 Flash) or no information filtering tools. They design the experiment with two stages:
- Stage 1: With access only to the name of the investment, participants initially choose a standard exchange-traded fund (ETF), a matched Environmental-Social-Governance (ESG) ETF or risk-free cash.
- Stage 2: They revisit their decisions after receiving access to their respective assigned information filtering tools.
At the end, participants who chose the cash alternative keep their initial investments, while those who chose an ETF receive their initial investments adjusted by a fund return. Using responses from 374 participants in the experiment, they find that:
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