Few existing literatures used the text information of the public opinions as the input index for volatility forecasting. This paper uses the text comment information of stockholders to construct a text sentiment factor that integrates the influence of comments and then combines other transaction information on volatility forecasting based on high-frequency finance data with the deep learning model long short-term memory (LSTM). The study finds that under the framework of the LSTM model, the forecasting accuracy for the volatility with the sentiment index is better than that of the LSTM model without the sentiment index and 10 traditional econometric models under the six loss functions. When compared with the traditional econometric model for multistep forecasting, the LSTM model is robust. With the addition of the public opinion index, the accuracy of LSTM is improved by 9.3%, 4.7%, 6.2%, 9.2%, 7.9%, and 16.9%, respectively, under the six evaluation criteria. The research in this article provides a more accurate, robust, and sustainable method for volatility forecasting in the context of big data.

中文翻译：

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高频数据下基于文本挖掘和深度学习的股票波动预测

现有文献很少将舆情文本信息作为波动率预测的输入指标。本文利用股东的文本评论信息构建了一个文本情感因子，整合评论的影响，然后结合其他交易信息基于高频金融数据与深度学习模型长短期记忆（LSTM）进行波动率预测. 研究发现，在LSTM模型的框架下，在6个损失函数下，有情绪指数的波动率预测精度优于没有情绪指数的LSTM模型和10种传统计量经济模型。与用于多步预测的传统计量经济学模型相比，LSTM 模型是稳健的。加上舆情指数，LSTM的准确率在六项评价标准下分别提高了9.3%、4.7%、6.2%、9.2%、7.9%和16.9%。本文的研究为大数据背景下的波动率预测提供了一种更准确、稳健和可持续的方法。