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Bojić, L., Zagovora, O., Zelenkauskaite, A., Vuković, V., Čabarkapa, M., Veseljević Jerković, S. et al. (2025). Comparing Large Language Models and Human Annotators in Latent Content Analysis of Sentiment, Political Leaning, Emotional Intensity and Sarcasm. Scientific Reports, 15, Article No. 11477.
https://doi.org/10.1038/s41598-025-96508-3

has been cited by the following article:

• TITLE: Climate Change: Factor Investing and Portfolio Management—Evidence from AI-Classified Sentiments

  AUTHORS: Yehuda Davis, Henry He Huang, Haiting Sun

  KEYWORDS: Climate Risk, ChatGPT, AI Models, Portfolio Optimization

  JOURNAL NAME: Journal of Financial Risk Management, Vol.15 No.4, October 16, 2026

  ABSTRACT: This paper examines whether AI-classified climate-change sentiment extracted from financial news contains incremental information for return prediction and portfolio optimization. We construct a novel climate-news sentiment measure using a large language model (LLM) to classify daily climate-related sentiment in Bloomberg and Reuters news articles. We then incorporate this measure into a rolling-window Fama-French five-factor forecasting model to generate predicted returns for a portfolio of ten exchange-traded funds (ETFs) spanning equities, fixed income, commodities, and foreign exchange. Using these predicted returns, we form daily portfolio weights that maximize the Sharpe ratio and compare their performance to both a portfolio constructed without the climate sentiment measure and to an equal-weight baseline portfolio. Over our sample period, the portfolio that incorporates climate-news sentiment achieves substantially higher returns and stronger risk-adjusted performance across multiple metrics. Our results indicate that climate-related news contains incremental information useful for ETF return forecasts and dynamic portfolio allocation. Additionally, we illustrate the ability of LLMs to extract economically meaningful information from financial news articles. Our findings contribute to the literature on climate-related information in financial markets and demonstrate the potential of AI-driven sentiment measures in portfolio management.