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Using deep learning to predict energy stock risk spillover based on co-investor attention

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  • Si, Jingjian
  • Gao, Xiangyun
  • Zhou, Jinsheng

Abstract

The risk spillover of the energy stock market has become a research hot spot in the field of energy finance. Investors and regulators can avoid systemic risks through the risk spillover relationship among listed companies. Therefore, predicting the risk spillover of the energy stock market is necessary. This study proposes a new forecasting framework to predict the risk spillover among different listed energy companies by leveraging the common concerns of investors for such companies. Results show that the deep neural network with targeted loss function constructed in this study demonstrates higher prediction accuracy.

Suggested Citation

  • Si, Jingjian & Gao, Xiangyun & Zhou, Jinsheng, 2025. "Using deep learning to predict energy stock risk spillover based on co-investor attention," Finance Research Letters, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:finlet:v:74:y:2025:i:c:s1544612325000248
    DOI: 10.1016/j.frl.2025.106759
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    References listed on IDEAS

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