A knowledge graph–GCN–community detection integrated model for large-scale stock price prediction.

Owing to uncertainty in the stock market, stock price prediction has always been a challenging research hotspot. In recent years, many stock prediction methods have used stock price series and technical indicators as inputs and the time series algorithm to predict, but they often ignore the influence of deeper factors such as the situation of the stock company and current situation of the stock industry. In addition, most of them predict based on small-scale stock datasets with limited characteristics and have certain defects such as bias, poor stability of prediction results, and lack of statistical significance tests on experimental results. To solve these problems, we propose a new method for stock price prediction based on knowledge graph (KG) and graph convolution neural network (GCN) models. First, stock KG is constructed, and the semantic relationships between stocks are described in the form of triples. Second, the correlations between stocks are quantified by fully utilizing their explicit/implicit relationships in the KG. Third, K-means, community detection (CD), and GCNs are merged to obtain accurate clustering results for similar stocks. Finally, the historical prices of similar stocks are used as the input characteristics of the time series models to predict stock price trends. We collect 4684 A-share market stocks in China from 2013 to 2019 and predicted the stock price trends for 762 of them. The experimental results and significance test show that the proposed method achieve the best accuracy, precision, and F1-measure on large-scale stock datasets and have the best stability, proving that the overall prediction effect outperforms that by state-of-the-art methods. • A stock knowledge graph is constructed based on the shareholder, concept stock and industry of stocks. • The concept of contribution to quantify the distance between stocks is introduced, and a distance adjacency matrix is constructed. • A new method that combines community detection with graph convolution neural network is proposed. • A small dataset can lead to biased, unstable, or fluctuating predictions. Our model addresses this problem and achieves optimal accuracy, precision, and F1 on a large-scale stock dataset. [ABSTRACT FROM AUTHOR]