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

Jingjian Si, Xiangyun Gao and Jinsheng Zhou

Finance Research Letters, 2025, vol. 74, issue C

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.

Keywords: Investor attention; Risk spillover; Energy finance; Deep learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612325000248
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:74:y:2025:i:c:s1544612325000248

DOI: 10.1016/j.frl.2025.106759

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().