[Objective] This paper studies financial news representations and the supply chain characteristics of particular companies. Then it utilizes these representations and inter-company associations to improve the prediction of public opinion risks for the target company. [Methods] Firstly, we embedded the company association knowledge into financial news texts based on attention mechanism and Bi-LSTM to learn financial news representation to a specific company. Secondly, we organized the financial news sequence into a news risk transmission network based on inter-company knowledge association. Finally, we used the TGAT layer to model the temporal transmission patterns of risk information through inter-company association and aggregate the risk information to predict the financial public opinion risk of the target company. [Results] The proposed method achieved an accuracy of 0.6246 and an AUC of 0.7021 in the financial public opinion risk prediction task, outperforming the baseline methods. [Limitations] The new model only uses the statistical knowledge associations between stocks of the listed companies and does not incorporate other types of inter-company knowledge associations. [Conclusions] The proposed model can effectively learn risk information relevant to the target company from financial news and the temporal transmission characteristics of public opinion risk in inter-company associations. It demonstrates good financial risk prediction performance.
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