Knowledge graph and deep learning combined with a stock price prediction network focusing on related stocks and mutation points

Abstract

Due to the interaction of many factors in the stock market, stock price prediction has always been a challenging problem in the field of machine learning. In particular, the mutation factors of the stock market often have a great impact on subsequent predictions. The existing prediction models seldom consider the impacts of other stocks in the stock market and mutation points on the prediction accuracy of target stocks. Therefore, this paper presents a new knowledge graph and deep learning method combined with a stock price prediction network focusing on related stocks and mutation points. First, the target stock price features are obtained through the ConvLSTM network. Second, the knowledge graph is used to mine the hidden relationships between stocks to find the stocks relevant to the target stock to obtain the market information vector and the market information features through the ConvLSTM network. Then, we find the mutation points according to the price change range, construct the mutation point distance weight matrix according to the distance from each trading day to the mutation points, and obtain the mutation point information features through the graph convolutional network (GCN). Finally, the features of market information, mutation point information and target stock price are fused to jointly predict the future stock price. The experimental results on the A share of Shenzhen from 2010 to 2019 show that the algorithm has good robustness and that the prediction accuracy is effectively improved.