Carbon price prediction models based on online news information analytics

Finance Research Letters - Tập 46 - Trang 102809 - 2022
Fang Zhang1, Yan Xia2,3
1School of Economics, Capital University of Economics and Business, Beijing, China
2Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China
3School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, 100049, China

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