Traditional stock price prediction methods are mostly based on the time series of a single stock,ignoring the complex interrelationships between stocks.In response to this issue,the article proposes a stock price prediction method based on busi-ness content to construct stock correlation relationships from the perspective of building a more effective stock portfolio.The model consists of three components:the association relationship construction component,the temporal feature extraction compo-nent and the association capture component.The association relationship construction component uses improved TF-IDF to ex-tract the similarity of business content keywords in the annual reports of listed companies to construct stock correlation relation-ships.The temporal feature extraction component uses LSTM to extract temporal features of stock trading data.The association capture component utilizes GCN to capture high-dimensional features of stock interactions,and finally outputs the predicted stock price through the fully connected layer.The experimental results in the Chinese A-share market indicate that this model has the smallest error,the better fit,and can more effectively predict stock prices compared to single stocks and industry relation-ship based prediction methods.It is a stock price prediction model that captures the mutual influence between stocks more fully.
关键词
股票价格预测/业务内容/股票关联关系/词频-逆向文件频率/长短期记忆神经网络/图卷积神经网络
Key words
stock price forecast/business content/stock related relationships/term frequency-inverse document frequency/long and short term memory neural network/graph convolution neural network
维普
万方数据