Carbon price prediction models based on online news information analytics
Tài liệu tham khảo
Bai, S., Kolter, J.Z., Koltun, V., 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv Prepr. arXiv1803.01271.
Ballings, 2015, Evaluating multiple classifiers for stock price direction prediction, Expert Syst. Appl., 42, 7046, 10.1016/j.eswa.2015.05.013
Batten, 2021, Does weather, or energy prices, affect carbon prices?, Energy Econ., 96, 10.1016/j.eneco.2020.105016
Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324
Broadstock, 2019, Social-media and intraday stock returns: the pricing power of sentiment, Financ. Res. Lett., 30, 116, 10.1016/j.frl.2019.03.030
Colladon, 2020, Forecasting election results by studying brand importance in online news, Int. J. Forecast., 36, 414, 10.1016/j.ijforecast.2019.05.013
Fan, 2017, Dynamics of China's carbon prices in the pilot trading phase, Appl. Energy, 208, 1452, 10.1016/j.apenergy.2017.09.007
Hao, 2020, Modelling of carbon price in two real carbon trading markets, J. Clean. Prod., 244, 10.1016/j.jclepro.2019.118556
Hintermann, 2010, Allowance price drivers in the first phase of the EU ETS, J. Environ. Econ. Manag., 59, 43, 10.1016/j.jeem.2009.07.002
Huang, 2021, A hybrid model for carbon price forecasting using GARCH and long short-term memory network, Appl. Energy, 285, 10.1016/j.apenergy.2021.116485
Huang, 2020, Carbon price forecasting with optimization prediction method based on unstructured combination, Sci. Total Environ., 725, 10.1016/j.scitotenv.2020.138350
Ji, 2021, Price drivers in the carbon emissions trading scheme: evidence from Chinese emissions trading scheme pilots, J. Clean. Prod., 278, 10.1016/j.jclepro.2020.123469
Keppler, 2010, Causalities between CO2, electricity, and other energy variables during phase I and phase II of the EU ETS, Energy Policy, 38, 3329, 10.1016/j.enpol.2010.02.004
LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Li, 2015, How does Google search affect trader positions and crude oil prices?, Econ. Model., 49, 162, 10.1016/j.econmod.2015.04.005
Li, 2019, Text-based crude oil price forecasting: a deep learning approach, Int. J. Forecast., 35, 1548, 10.1016/j.ijforecast.2018.07.006
Liu, 2021, Carbon option price forecasting based on modified fractional Brownian motion optimized by GARCH model in carbon emission trading, North Am. J. Econ. Financ., 55, 101307, 10.1016/j.najef.2020.101307
Lu, 2020, Carbon trading volume and price forecasting in China using multiple machine learning models, J. Clean. Prod., 249, 10.1016/j.jclepro.2019.119386
Seifert, 2008, Dynamic behavior of CO2 spot prices, J. Environ. Econ. Manag., 56, 180, 10.1016/j.jeem.2008.03.003
Sun, 2020, A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network, Energy, 207, 10.1016/j.energy.2020.118294
Sun, 2020, A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network, J. Clean. Prod., 243, 10.1016/j.jclepro.2019.118671
Sun, 2021, Carbon price prediction based on modified wavelet least square support vector machine, Sci. Total Environ., 754, 10.1016/j.scitotenv.2020.142052
Wen, 2022, What drive carbon price dynamics in China?, Int. Rev. Financ. Anal., 79, 10.1016/j.irfa.2021.101999
Wen, 2020, Asymmetric relationship between carbon emission trading market and stock market: evidences from China, Energy Econ., 91, 10.1016/j.eneco.2020.104850
Xu, 2020, Carbon price forecasting with complex network and extreme learning machine, Phys. A Stat. Mech. Appl., 545, 10.1016/j.physa.2019.122830
Ye, 2021, Influences of sentiment from news articles on EU carbon prices, Energy Econ., 101, 10.1016/j.eneco.2021.105393
Zhang, 2018, A hybrid model using signal processing technology, econometric models and neural network for carbon spot price forecasting, J. Clean. Prod., 204, 958, 10.1016/j.jclepro.2018.09.071
Zhao, 2021, Extreme event shocks and dynamic volatility interactions: the stock, commodity, and carbon markets in China, Financ. Res. Lett.
Zhu, 2018, A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting, Energy Econ., 70, 143, 10.1016/j.eneco.2017.12.030
Zhu, 2019, Carbon price forecasting with variational mode decomposition and optimal combined model, Phys. A Stat. Mech. Appl., 519, 140, 10.1016/j.physa.2018.12.017