GraphCNNpred: A stock market indices prediction using a Graph based deep learning system

The application of deep learning techniques for predicting stock market prices is a prominent and widely researched topic in the field of data science. To effectively predict market trends, it is essential to utilize a diversified dataset. In this paper, we introduce a graph neural network-based convolutional neural network (CNN) model that can be applied to diverse data sources to extract features and predict trends for indices like S&P 500, NASDAQ, DJI, NYSE, and RUSSELL. The experiments show that the proposed models improve the prediction performance for all indices over the baseline algorithms by about 4% to 15%, in terms of F-measure. A trading simulation based on these predictions achieved a Sharpe ratio above 3.