An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market - Long, Jiawei; Chen, Zhaopeng; He, Weibing; Wu, Taiyu; Ren, Jiangtao (Applied soft computing, 2020)
Many studies have been carried out on stock price trend prediction, but most of them focused on the public market data and did not utilize the trading behaviors owing to the unavailability of real transaction records data. In fact, trading behaviors can better reflect the market movements, and the fusion of trading information and market information can further improve the prediction accuracy. In this paper, we propose a deep neural network model using the desensitized transaction records and public market information to predict stock price trend. Considering the correlation between stocks, our method utilizes the knowledge graph and graph embeddings techniques to select the relevant stocks of the target for constructing the market and trading information. Given the considerable number of investors and the complexity of transaction records data, the investors are clustered to reduce the dimensions of the trading feature matrices, and then the matrices are fed into the convolutional neural network to unearth the investment patterns. Eventually, the attention-based bidirectional long short-term memory network can predict the stock price trends for financial decision support. The experiments on the price movement direction and trend prediction show that our method achieves the best performance in comparison with other prediction baselines. •A framework fusing trading and market information is proposed to stock prediction.•Technical indicators on relevant stocks are combined in forecasting price trends.•The relevant stocks of the target stock are selected by knowledge graph methods.•The trading features on stocks of trader clusters ensure the robustness.•The proposed model outperforms other baseline models on real world dataset.