IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v46y2022ipas1544612322001143.html
   My bibliography  Save this article

Carbon price prediction models based on online news information analytics

Author

Listed:
  • Zhang, Fang
  • Xia, Yan

Abstract

In recent years, with the verification of the carbon market's effectiveness in conserving energy and mitigating emissions, accurate carbon price prediction has attracted the interest of researchers and investors. However, carbon price forecasting is widely considered intractable due to its various non-stationary properties. A novel data-driven carbon prices forecasting approach using online news data and Google trends is applied in this paper. Word embedding algorithm is adopted to identify the text features of online carbon market news, which expressing the text information. Long Short Term Memory (LSTM) algorithm is applied to forecast carbon prices. Finally, a comparison analysis is employed, the results of which show that the proposed framework performs better than traditional statistical forecasting models with respect to predictive ability and robustness.

Suggested Citation

  • Zhang, Fang & Xia, Yan, 2022. "Carbon price prediction models based on online news information analytics," Finance Research Letters, Elsevier, vol. 46(PA).
    • Handle: RePEc:eee:finlet:v:46:y:2022:i:pa:s1544612322001143
    DOI: 10.1016/j.frl.2022.102809
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612322001143
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2022.102809?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hintermann, Beat, 2010. "Allowance price drivers in the first phase of the EU ETS," Journal of Environmental Economics and Management, Elsevier, vol. 59(1), pages 43-56, January.
    2. Keppler, Jan Horst & Mansanet-Bataller, Maria, 2010. "Causalities between CO2, electricity, and other energy variables during phase I and phase II of the EU ETS," Energy Policy, Elsevier, vol. 38(7), pages 3329-3341, July.
    3. Zhu, Jiaming & Wu, Peng & Chen, Huayou & Liu, Jinpei & Zhou, Ligang, 2019. "Carbon price forecasting with variational mode decomposition and optimal combined model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 140-158.
    4. Seifert, Jan & Uhrig-Homburg, Marliese & Wagner, Michael, 2008. "Dynamic behavior of CO2 spot prices," Journal of Environmental Economics and Management, Elsevier, vol. 56(2), pages 180-194, September.
    5. Li, Xuerong & Shang, Wei & Wang, Shouyang, 2019. "Text-based crude oil price forecasting: A deep learning approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1548-1560.
    6. Broadstock, David C. & Zhang, Dayong, 2019. "Social-media and intraday stock returns: The pricing power of sentiment," Finance Research Letters, Elsevier, vol. 30(C), pages 116-123.
    7. Li, Xin & Ma, Jian & Wang, Shouyang & Zhang, Xun, 2015. "How does Google search affect trader positions and crude oil prices?," Economic Modelling, Elsevier, vol. 49(C), pages 162-171.
    8. Sun, Wei & Huang, Chenchen, 2020. "A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network," Energy, Elsevier, vol. 207(C).
    9. Fronzetti Colladon, Andrea, 2020. "Forecasting election results by studying brand importance in online news," International Journal of Forecasting, Elsevier, vol. 36(2), pages 414-427.
    10. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    11. Ye, Jing & Xue, Minggao, 2021. "Influences of sentiment from news articles on EU carbon prices," Energy Economics, Elsevier, vol. 101(C).
    12. Wen, Fenghua & Zhao, Lili & He, Shaoyi & Yang, Guozheng, 2020. "Asymmetric relationship between carbon emission trading market and stock market: Evidences from China," Energy Economics, Elsevier, vol. 91(C).
    13. Wen, Fenghua & Zhao, Haocen & Zhao, Lili & Yin, Hua, 2022. "What drive carbon price dynamics in China?," International Review of Financial Analysis, Elsevier, vol. 79(C).
    14. repec:dau:papers:123456789/5269 is not listed on IDEAS
    15. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    16. Zhu, Bangzhu & Ye, Shunxin & Wang, Ping & He, Kaijian & Zhang, Tao & Wei, Yi-Ming, 2018. "A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting," Energy Economics, Elsevier, vol. 70(C), pages 143-157.
    17. Liu, Zhibin & Huang, Shan, 2021. "Carbon option price forecasting based on modified fractional Brownian motion optimized by GARCH model in carbon emission trading," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    18. Batten, Jonathan A. & Maddox, Grace E. & Young, Martin R., 2021. "Does weather, or energy prices, affect carbon prices?," Energy Economics, Elsevier, vol. 96(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Huang, Wenyang & Zhao, Jianyu & Wang, Xiaokang, 2024. "Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price," Energy Economics, Elsevier, vol. 132(C).
    2. Peng Ye & Yong Li & Abu Bakkar Siddik, 2023. "Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm," Energies, MDPI, vol. 16(11), pages 1-39, June.
    3. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).
    2. Po Yun & Chen Zhang & Yaqi Wu & Yu Yang, 2022. "Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network," IJERPH, MDPI, vol. 19(2), pages 1-19, January.
    3. Yumin Li & Ruiqi Yang & Xiaoman Wang & Jiaming Zhu & Nan Song, 2023. "Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
    4. Gao, Feng & Shao, Xueyan, 2022. "A novel interval decomposition ensemble model for interval carbon price forecasting," Energy, Elsevier, vol. 243(C).
    5. Huang, Wenyang & Wang, Huiwen & Qin, Haotong & Wei, Yigang & Chevallier, Julien, 2022. "Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method," Energy Economics, Elsevier, vol. 110(C).
    6. Wen Zhang & Zhibin Wu, 2022. "Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 615-632, April.
    7. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.
    8. Yi Yao & Lixin Tian & Guangxi Cao, 2022. "The Information Spillover among the Carbon Market, Energy Market, and Stock Market: A Case Study of China’s Pilot Carbon Markets," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    9. Zhu, Bangzhu & Yuan, Lili & Ye, Shunxin, 2019. "Examining the multi-timescales of European carbon market with grey relational analysis and empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 392-399.
    10. Dahlen, Niklas & Fehrenkötter, Rieke & Schreiter, Maximilian, 2024. "The new bond on the block — Designing a carbon-linked bond for sustainable investment projects," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 316-325.
    11. Li, Xin & Li, Zheng & Su, Chi-Wei & Umar, Muhammad & Shao, Xuefeng, 2022. "Exploring the asymmetric impact of economic policy uncertainty on China's carbon emissions trading market price: Do different types of uncertainty matter?," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    12. Houjian Li & Xinya Huang & Deheng Zhou & Andi Cao & Mengying Su & Yufeng Wang & Lili Guo, 2022. "Forecasting Carbon Price in China: A Multimodel Comparison," IJERPH, MDPI, vol. 19(10), pages 1-16, May.
    13. Su, Chi Wei & Wei, Shenkai & Wang, Yan & Tao, Ran, 2024. "How does climate policy uncertainty affect the carbon market?," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    14. Demiralay, Sercan & Gencer, Hatice Gaye & Bayraci, Selcuk, 2022. "Carbon credit futures as an emerging asset: Hedging, diversification and downside risks," Energy Economics, Elsevier, vol. 113(C).
    15. Liu, Shuihan & Xie, Gang & Wang, Zhengzhong & Wang, Shouyang, 2024. "A secondary decomposition-ensemble framework for interval carbon price forecasting," Applied Energy, Elsevier, vol. 359(C).
    16. Creti, Anna & Jouvet, Pierre-André & Mignon, Valérie, 2012. "Carbon price drivers: Phase I versus Phase II equilibrium?," Energy Economics, Elsevier, vol. 34(1), pages 327-334.
    17. Qin, Chaoyong & Qin, Dongling & Jiang, Qiuxian & Zhu, Bangzhu, 2024. "Forecasting carbon price with attention mechanism and bidirectional long short-term memory network," Energy, Elsevier, vol. 299(C).
    18. Zhu, Bangzhu & Huang, Liqing & Yuan, Lili & Ye, Shunxin & Wang, Ping, 2020. "Exploring the risk spillover effects between carbon market and electricity market: A bidimensional empirical mode decomposition based conditional value at risk approach," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 163-175.
    19. Eslahi, Mohammadehsan & Mazza, Paolo, 2023. "Can weather variables and electricity demand predict carbon emissions allowances prices? Evidence from the first three phases of the EU ETS," Ecological Economics, Elsevier, vol. 214(C).
    20. Ahonen, Elena & Corbet, Shaen & Goodell, John W. & Günay, Samet & Larkin, Charles, 2022. "Are carbon futures prices stable? New evidence during negative oil," Finance Research Letters, Elsevier, vol. 47(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:46:y:2022:i:pa:s1544612322001143. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.