Business & Finance Investment ESG Sentiment from Large Language Models and Its Predictive Power for Portfolio Risk
大規模言語モデルによるビジネス・金融投資ESGセンチメントとポートフォリオリスク予測力 (AI 翻訳)
Iqra Mubeen, Samina Rauf
🤖 gxceed AI 要約
日本語
大規模言語モデル（LLM）で生成したESGセンチメントがポートフォリオリスクを予測できるか検証。正負のシグナルとボラティリティや下方リスクなどの関連を分析し、ESGセンチメントが従来の財務指標に加えて将来のリスク評価に有効であることを示した。LLMはキーワードベースよりESGのニュアンスを理解できるが、モデルバイアスや幻覚のリスクも指摘している。
English
This paper investigates whether ESG sentiment generated by LLMs can predict portfolio risk. It finds that positive ESG sentiment correlates with lower volatility and downside risk, while negative sentiment increases risk exposure. LLMs outperform keyword-based approaches in capturing ESG nuances, but challenges such as model bias and hallucination remain.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でもSSBJ対応の開示文書や統合報告書の分析にLLMを用いたESGセンチメント分析が有効である可能性を示す。投資家対応やリスク管理への応用が期待される。
In the global GX context
This study demonstrates how LLM-generated ESG sentiment can enhance portfolio risk assessment, complementing traditional financial signals. It aligns with global trends in using AI for analyzing TCFD/ISSB/CSRD disclosures and could inform ESG rating methodologies.
👥 読者別の含意
🔬研究者:Contributes to the growing literature on AI×ESG by empirically linking LLM sentiment to portfolio risk metrics, offering a methodological framework for further research.
🏢実務担当者:Portfolio managers and ESG analysts can leverage LLM sentiment as a forward-looking indicator for risk assessment and investment decisions.
📄 Abstract（原文）
The Large Language Models (LLMs) are also using sentiment analysis to generate corporate report sentiment data, articles sentiment data, earnings call sentiment data, sustainability report sentiment data, and social media sentiment data. This paper investigates if sentiment written by a large language model (LLM) can predict portfolio risk. It investigates the relationship between positive and negative signals and the risk metrics such as volatility, downside risk, drawdown exposure and portfolio stability. The results indicate that sentiment derived from LLM models can offer valuable forward-looking insights into ESG evaluations, in addition to conventional financial signals. At each of these firm levels, consistently positive ESG sentiment was associated with reduced perceived risk, increased investor confidence, and lower volatility of the portfolio, while negative sentiment was associated with increased downside exposure, reputational risk, and volatility. Another key strength the study finds is that LLMs are better able to understand nuanced language associated with the ESG when compared with the basic keyword-based approach, which will enhance the quality of sentiment measurement. Yet, there are still risks and issues, such as model bias, the risk of hallucination, the use of inconsistent ESG terminology and reliance on source document quality. Overall, LLM-generated ESG sentiment and financial data and human validation, and responsible model governance can be harnessed for use in portfolio risk assessment, the paper concludes.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://jabfs.online/index.php/journal/article/download/61/60first seen 2026-07-27 05:41:44
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。