A Deep Neural Network-Based Multisource Information Fusion Method for Stock Price Prediction of Enterprises

Most of the existing research works on stock price prediction ignore the synergistic effect of multi-source factors. Therefore, an enterprise stock price prediction method based on multi-source information fusion based on deep neural network (DNN) is proposed. Based on the condition of multi-source information fusion, the original data related to enterprise stock price are collected from multiple sources, and the structure level and model framework of the method are constructed. The feature extraction technology of DNN is used to extract the features useful for stock price prediction from the pre-processed data, and the DNN structure considering multi-source information fusion is established. Finally, feature extraction and evaluation settings are completed based on data variables. LOSS, ACCURACY and other indicators were used for analysis. The results show that compared with typical prediction methods, this method can make use of multiple information sources more comprehensively and improve the prediction accuracy. In addition, the proposed method has certain flexibility and can adapt to the characteristics and changes of different stock markets