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Influences of sentiment from news articles on EU carbon prices

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  • Ye, Jing
  • Xue, Minggao

Abstract

In this paper, we calculate the carbon tone index that reflects sentiment in news articles through a self-built dictionary. We study the effect of carbon tone index on carbon price return in the period from September 19, 2017 to October 9, 2020. In addition, we employ the Latent Dirichlet Allocation (LDA) method to explore the differential influences of different topic carbon tone indexes on carbon prices. The market confidence boosted by the approved Market Stability Reserve (MSR) policy led to a continuous increase in volume in 2018. Using two subsample periods divided by the implementation of MSR, we explore the problem whether the increased high volume changed the speed of information absorption in carbon market. Quantile regression with control variables (coal, oil, natural gas, electricity and stock prices) is used to test the robustness of the estimated results. The empirical results show that carbon tone index is closely associated with changes in carbon prices and the efficiency of carbon market is improved after MSR. Finally, we use all carbon tone indexes at the 10% significance level in eight predictive models and show the economic value of the optimal predictive model. In summary, carbon tone index has a strong predictive power for carbon prices.

Suggested Citation

  • Ye, Jing & Xue, Minggao, 2021. "Influences of sentiment from news articles on EU carbon prices," Energy Economics, Elsevier, vol. 101(C).
    • Handle: RePEc:eee:eneeco:v:101:y:2021:i:c:s0140988321002929
    DOI: 10.1016/j.eneco.2021.105393
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    5. Zhang, Fang & Xia, Yan, 2022. "Carbon price prediction models based on online news information analytics," Finance Research Letters, Elsevier, vol. 46(PA).
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    8. Xu Gong & Mengjie Li & Keqin Guan & Chuanwang Sun, 2023. "Climate change attention and carbon futures return prediction," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(9), pages 1261-1288, September.
    9. Xu Gong & Keqin Guan & Qiyang Chen, 2022. "The role of textual analysis in oil futures price forecasting based on machine learning approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1987-2017, October.
    10. Man, Yuanyuan & Zhang, Sunpei & He, Yongda, 2024. "Dynamic risk spillover and hedging efficacy of China’s carbon-energy-finance markets: Economic policy uncertainty and investor sentiment non-linear causal effects," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 1397-1416.
    11. Hartvig, Áron Dénes & Pap, Áron & Pálos, Péter, 2023. "EU Climate Change News Index: Forecasting EU ETS prices with online news," Finance Research Letters, Elsevier, vol. 54(C).
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