In this paper, the self‐monitoring learning model FinBERT is used to identify text emotions, and the sliding time window time‐lagged cross‐correlation (WTLCC) method is utilized to screen Baidu Index keywords for the Shanghai Stock Exchange Index and 18 A‐share listed companies. There are five different types of indicators constructed: news media sentiment, public attention, investor sentiment, investor sentiment disagreement, and media sentiment disagreement. To accurately describe the structure of sentimental contagion, this paper combines graph neural network to learn and output the sentimental contagion graph, and then constructs multivariable time series forecasting with graph neural networks (MTGNN) volatility forecasting model, which can extract the spatial–temporal dependence of variables in pairs. The results show that MTGNN model possesses the highest forecasting accuracy, which performs 30.30% lower on average across four evaluation indicators for Shanghai Stock Exchange Index than temporal pattern attention–long short‐term memory model, which ranks second. For all of the models considered in this paper, adding sentimental contagion mechanism can significantly improve the volatility forecasting accuracy. The error of MTGNN is reduced the most, with a 15.21% average reduction for the Shanghai Stock Exchange Index. The contagion relationship among media reports, investor sentiment, and attention can help provide new ideas for enhancing the precision of volatility forecasting from the public opinion environment in the financial market.

中文翻译：

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基于MTGNN模型结合媒体报道、投资者情绪和关注度的股市波动率预测

本文利用自监控学习模型FinBERT识别文本情感，利用滑动时间窗时滞互相关（WTLCC）方法筛选上证指数和18个A的百度指数关键词。参股上市公司。构建了五种不同类型的指标：新闻媒体情绪、公众关注度、投资者情绪、投资者情绪分歧和媒体情绪分歧。为了准确描述情感传染的结构，本文结合图神经网络学习并输出情感传染图，然后构建多变量时间序列预测图神经网络（MTGNN）波动性预测模型，可以提取时空依赖性成对的变量。结果表明，MTGNN模型具有最高的预测精度，在上证指数的四个评价指标上，其预测精度平均比排名第二的时间模式注意力-长短期记忆模型低30.30%。对于本文考虑的所有模型，添加情感传染机制可以显着提高波动率预测的准确性。MTGNN误差降低幅度最大，上证指数平均降低15.21%。媒体报道、投资者情绪和关注度之间的传染关系，可以为提高金融市场舆论环境波动预测的精准度提供新思路。