{"ID":2825036,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00836","arxiv_id":"2601.00836","title":"ESG Beliefs of Large Language Models: Evidence and Impact","abstract":"We examine whether large language models (LLMs) hold systematic beliefs about environmental, social, and governance (ESG) issues and how these beliefs compare with-and potentially influence-those of human market participants. Based on established surveys originally administered to professional and retail investors, we show that major LLMs exhibit a strong pro-ESG orientation. Compared with human investors, LLMs assign greater financial relevance for ESG performance, expect larger return premia for high-ESG firms, and display a stronger willingness to sacrifice financial returns for ESG improvements. These preferences are highly uniform and values-driven, in contrast to heterogeneous human views. Using a large dataset of analyst reports, we further show that sell-side analysts become significantly more optimistic about high-ESG firms after adopting LLMs for research. Our findings reveal that LLMs embed distinct, coherent ESG beliefs and that these beliefs can shape human judgments, highlighting a new channel through which AI adoption may influence financial markets.","short_abstract":"We examine whether large language models (LLMs) hold systematic beliefs about environmental, social, and governance (ESG) issues and how these beliefs compare with-and potentially influence-those of human market participants. Based on established surveys originally administered to professional and retail investors, we...","url_abs":"https://arxiv.org/abs/2601.00836","url_pdf":"https://arxiv.org/pdf/2601.00836v1","authors":"[\"Tong Li\",\"Luping Yu\"]","published":"2025-12-26T15:49:25Z","proceeding":"physics.soc-ph","tasks":"[\"physics.soc-ph\",\"cs.CY\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}