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China’s Energy Stock Price Index Prediction Based on VECM–BiLSTM Model

Author

Listed:
  • Bingchun Liu

    (School of Management, Tianjin University of Technology, Tianjin 300382, China)

  • Xia Zhang

    (School of Management, Tianjin University of Technology, Tianjin 300382, China)

  • Yuan Gao

    (Xinhua Electric Power Development Investment Co., Ltd., CNNC, Tianjin 300382, China)

  • Minghui Xu

    (China Energy Engineering Group Tianjin Electric Power Design Institute Co., Ltd., Tianjin 300382, China)

  • Xiaobo Wang

    (Office of State-Owned Assets and Campus Economic Management, Tianjin University of Technology, Tianjin 300382, China)

Abstract

The energy stock price index maps the development trends in China’s energy market to a certain extent, and accurate forecasting of China’s energy market index can effectively guide the government to regulate energy policies to cope with external risks. The vector error correction model (VECM) analyzes the relationship between each indicator and the output, provides an external explanation for the way the indicator influences the output indicator, and uses this to filter the input indicators. The forecast results of the China energy stock price index for 2022–2024 showed an upward trend, and the model evaluation parameters MAE, MAPE, and RMSE were 0.2422, 3.5704% and 0.3529, respectively, with higher forecasting efficiency than other comparative models. Finally, the impact of different indicators on the Chinese energy market was analyzed through scenario setting. The results show that oscillations in the real commodity price factor (RCPF) and the global economic conditions index (GECON) cause fluctuations in the price indices of the Chinese energy market and that the Chinese energy market evolves in the same manner as the changes in two international stock indices: the MSCI World Index and FTSE 100 Index.

Suggested Citation

  • Bingchun Liu & Xia Zhang & Yuan Gao & Minghui Xu & Xiaobo Wang, 2025. "China’s Energy Stock Price Index Prediction Based on VECM–BiLSTM Model," Energies, MDPI, vol. 18(5), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1242-:d:1604619
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    References listed on IDEAS

    as
    1. Zhou, Wei & Chen, Yan & Chen, Jin, 2022. "Risk spread in multiple energy markets: Extreme volatility spillover network analysis before and during the COVID-19 pandemic," Energy, Elsevier, vol. 256(C).
    2. Urolagin, Siddhaling & Sharma, Nikhil & Datta, Tapan Kumar, 2021. "A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting," Energy, Elsevier, vol. 231(C).
    3. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
    4. Jia, Zhijie & Wen, Shiyan & Lin, Boqiang, 2021. "The effects and reacts of COVID-19 pandemic and international oil price on energy, economy, and environment in China," Applied Energy, Elsevier, vol. 302(C).
    5. Wu, Chunying & Wang, Jianzhou & Hao, Yan, 2022. "Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm," Resources Policy, Elsevier, vol. 77(C).
    6. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).
    7. Zhang, Xiaoyu & Zhou, Jinlan & Du, Xiaodong, 2022. "Impact of oil price uncertainty shocks on China’s macro-economy," Resources Policy, Elsevier, vol. 79(C).
    8. Inoue, Atsushi & Kilian, Lutz, 2022. "Joint Bayesian inference about impulse responses in VAR models," Journal of Econometrics, Elsevier, vol. 231(2), pages 457-476.
    9. Ju, Keyi & Su, Bin & Zhou, Dequn & Wu, Junmin, 2017. "Does energy-price regulation benefit China's economy and environment? Evidence from energy-price distortions," Energy Policy, Elsevier, vol. 105(C), pages 108-119.
    10. Sadeghi, Abdorasoul & Roudari, Soheil, 2022. "Heterogeneous effects of oil structure and oil shocks on stock prices in different regimes: Evidence from oil-exporting and oil-importing countries," Resources Policy, Elsevier, vol. 76(C).
    11. Wang, Lu & Wu, Jiangbin & Cao, Yang & Hong, Yanran, 2022. "Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?," Energy Economics, Elsevier, vol. 111(C).
    12. Xiong, Xiaoping & Qing, Guohua, 2023. "A hybrid day-ahead electricity price forecasting framework based on time series," Energy, Elsevier, vol. 264(C).
    13. Chiu, Fan-Ping & Hsu, Chia-Sheng & Ho, Alan & Chen, Chi-Chung, 2016. "Modeling the price relationships between crude oil, energy crops and biofuels," Energy, Elsevier, vol. 109(C), pages 845-857.
    14. Ashfaq, Saleha & Tang, Yong & Maqbool, Rashid, 2019. "Volatility spillover impact of world oil prices on leading Asian energy exporting and importing economies’ stock returns," Energy, Elsevier, vol. 188(C).
    15. Atems, Bebonchu & Mette, Jehu & Lin, Guoyu & Madraki, Golshan, 2023. "Estimating and forecasting the impact of nonrenewable energy prices on US renewable energy consumption," Energy Policy, Elsevier, vol. 173(C).
    16. Gong, Xu & Shi, Rong & Xu, Jun & Lin, Boqiang, 2021. "Analyzing spillover effects between carbon and fossil energy markets from a time-varying perspective," Applied Energy, Elsevier, vol. 285(C).
    17. Li, Jingpeng & Umar, Muhammad & Huo, Jiale, 2023. "The spillover effect between Chinese crude oil futures market and Chinese green energy stock market," Energy Economics, Elsevier, vol. 119(C).
    18. Singh, Sanjeet & Bansal, Pooja & Hosen, Mosharrof & Bansal, Sanjeev K., 2023. "Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM," Resources Policy, Elsevier, vol. 80(C).
    19. Chen, Zhan-Ming & Chen, Pei-Lin & Ma, Zeming & Xu, Shiyun & Hayat, Tasawar & Alsaedi, Ahmed, 2019. "Inflationary and distributional effects of fossil energy price fluctuation on the Chinese economy," Energy, Elsevier, vol. 187(C).
    20. Fan, Dongyan & Sun, Hai & Yao, Jun & Zhang, Kai & Yan, Xia & Sun, Zhixue, 2021. "Well production forecasting based on ARIMA-LSTM model considering manual operations," Energy, Elsevier, vol. 220(C).
    21. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
    22. Zeng, Yu-Rong & Zeng, Yi & Choi, Beomjin & Wang, Lin, 2017. "Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network," Energy, Elsevier, vol. 127(C), pages 381-396.
    23. Jiang, He & Hu, Weiqiang & Xiao, Ling & Dong, Yao, 2022. "A decomposition ensemble based deep learning approach for crude oil price forecasting," Resources Policy, Elsevier, vol. 78(C).
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