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Climate change attention and carbon futures return prediction

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  • Xu Gong
  • Mengjie Li
  • Keqin Guan
  • Chuanwang Sun

Abstract

This study explores the predictive effect of climate change attention on carbon futures returns. Using climate‐related Google Trends and news, we construct five dimensions of the public climate attention index and media climate attention index. After feature selection, we incorporate the optimized combination with lagged order into the machine learning model to predict EU Emission Allowance futures returns. Our empirical results show that the forecasting models with climate attention outperform the corresponding benchmark models, indicating that climate attention does provide predictive information for carbon futures returns. In addition, we carry out trading simulations to investigate the economic performance of the forecast results. It turns out that the market strategies based on the prediction models with climate attention can deliver more benefits than the counterpart market strategies. More specifically, the cumulative returns reach 140% during the out‐of‐sample period, much higher than 79% of the cumulative returns of the buy‐and‐hold strategy.

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  • 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.
  • Handle: RePEc:wly:jfutmk:v:43:y:2023:i:9:p:1261-1288
    DOI: 10.1002/fut.22443
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    Cited by:

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    2. Zhikai Zhang & Yaojie Zhang & Yudong Wang & Qunwei Wang, 2024. "The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(4), pages 557-584, April.
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    5. Xinchen Liu & Xuanwei Ning & Chengliang Wu & Yang Zhang, 2024. "Evolutionary Trends in Carbon Market Risk Research," Energies, MDPI, vol. 17(18), pages 1-28, September.

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