We explore how to crawl financial forum data such as stock bars and combine them with deep learning models for sentiment analysis. In this paper, we will use the BERT model to train against the financial corpus and predict the SZSE Component Index, and find that applying the BERT model to the financial corpus through the maximum information coefficient comparison study. The obtained sentiment features will be able to reflect the fluctuations in the stock market and help to improve the prediction accuracy effectively. Meanwhile, this paper combines deep learning with financial text, in further exploring the mechanism of investor sentiment on stock market through deep learning method, which will be beneficial for national regulators and policy departments to develop more reasonable policy guidelines for maintaining the stability of stock market.
翻译:我们探索如何吸收金融论坛的数据,如股票条,并将这些数据与深入学习的情绪分析模式结合起来;在本文件中,我们将使用BERT模式,针对金融体制进行培训,预测SZSE构成指数,发现通过最大信息系数比较研究将BERT模式应用于金融体制;获得的情绪特征将能够反映股票市场的波动,并有助于有效提高预测准确性;同时,本文件将深思熟虑与金融文本结合起来,通过深思熟虑的方法,进一步探索投资者对股票市场的看法机制,这将有利于国家监管机构和政策部门制定更合理的政策准则,以维持股票市场稳定。