Volatility forecasting for stock market incorporating media reports, investors' sentiment, and attention based on MTGNN model

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.