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Can Large Language Models forecast carbon price movements? Evidence from Chinese carbon markets

Author

Listed:
  • Chen, Rui
  • Jiang, Haiqi
  • Guo, Tingyu
  • Fan, Chenyou

Abstract

This paper investigates the impact of Large Language Models (LLMs) on forecasting Chinese carbon prices. We introduce a novel two-stage forecasting framework integrating a Time-Series Model (TSM) and Large Language Models. Initially, we use historical data on Chinese Emission Allowance prices to train the TSM for preliminary predictions. LLMs then refine these predictions, which process a sequence of past and corresponding future prices as a chain of thought. Additionally, we utilize the LLM to analyze and categorize the sentiment of news headlines, generating market sentiment labels that enhance the LLM’s predictive accuracy. Our findings indicate that LLMs can improve TSM forecasts by 28–38 % across different regional markets. Furthermore, incorporating news sentiment labels into the LLM contributes an additional reduction in forecasting deviations, ranging from 3–4 %.

Suggested Citation

  • Chen, Rui & Jiang, Haiqi & Guo, Tingyu & Fan, Chenyou, 2025. "Can Large Language Models forecast carbon price movements? Evidence from Chinese carbon markets," Research in International Business and Finance, Elsevier, vol. 77(PB).
    • Handle: RePEc:eee:riibaf:v:77:y:2025:i:pb:s0275531925002077
    DOI: 10.1016/j.ribaf.2025.102951
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    1. Sean Cao Robert H. Smith & Wei Jiang & Baozhong Yang J. Mack Robinson & Alan L Zhang & Tarun Ramadorai, 2023. "How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI," The Review of Financial Studies, Society for Financial Studies, vol. 36(9), pages 3603-3642.
    2. Tim Loughran & Bill Mcdonald, 2014. "Measuring Readability in Financial Disclosures," Journal of Finance, American Finance Association, vol. 69(4), pages 1643-1671, August.
    3. Paul Glasserman & Caden Lin, 2023. "Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis," Papers 2309.17322, arXiv.org.
    4. Gerard Hoberg & Gordon Phillips, 2016. "Text-Based Network Industries and Endogenous Product Differentiation," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1423-1465.
    5. Ren, Xiaohang & Duan, Kun & Tao, Lizhu & Shi, Yukun & Yan, Cheng, 2022. "Carbon prices forecasting in quantiles," Energy Economics, Elsevier, vol. 108(C).
    6. Bangzhu Zhu, 2012. "A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network," Energies, MDPI, vol. 5(2), pages 1-16, February.
    7. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    8. Li, Jingmiao & Liu, Dehong, 2023. "Carbon price forecasting based on secondary decomposition and feature screening," Energy, Elsevier, vol. 278(PA).
    9. Shijie Wu & Ozan Irsoy & Steven Lu & Vadim Dabravolski & Mark Dredze & Sebastian Gehrmann & Prabhanjan Kambadur & David Rosenberg & Gideon Mann, 2023. "BloombergGPT: A Large Language Model for Finance," Papers 2303.17564, arXiv.org, revised Dec 2023.
    10. Duan, Kun & Wang, Rui & Chen, Shun & Ge, Lei, 2023. "Exploring the predictability of attention mechanism with LSTM: Evidence from EU carbon futures prices," Research in International Business and Finance, Elsevier, vol. 66(C).
    11. Jianguo Zhou & Qiqi Wang, 2021. "Forecasting Carbon Price with Secondary Decomposition Algorithm and Optimized Extreme Learning Machine," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    12. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    13. Michael Gofman & Zhao Jin, 2024. "Artificial Intelligence, Education, and Entrepreneurship," Journal of Finance, American Finance Association, vol. 79(1), pages 631-667, February.
    14. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 54(4), pages 1187-1230, September.
    15. Qianqian Xie & Weiguang Han & Yanzhao Lai & Min Peng & Jimin Huang, 2023. "The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges," Papers 2304.05351, arXiv.org, revised Apr 2023.
    16. Tianqi Pang & Kehui Tan & Chenyou Fan, 2023. "Carbon Price Forecasting with Quantile Regression and Feature Selection," Papers 2305.03224, arXiv.org.
    17. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
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    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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