GraphCNNpred: A stock market indices prediction using a Graph based deep learning system
This work addresses stock market prediction for investors and traders, offering incremental improvements over existing methods.
The paper tackled stock market prediction by developing a graph neural network-based CNN model that uses diverse data to predict indices like S&P 500, improving prediction performance by 4% to 15% in F-measure over baselines and achieving a Sharpe ratio over 3 in trading simulations.
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 give a graph neural network based convolutional neural network (CNN) model, that can be applied on diverse source of data, in the attempt to extract features to predict the trends of indices of \text{S}\&\text{P} 500, NASDAQ, DJI, NYSE, and RUSSEL. The experiments show that the associated models improve the performance of prediction in all indices over the baseline algorithms by about $4\% \text{ to } 15\%$, in terms of F-measure. A trading simulation is generated from predictions and gained a Sharpe ratio of over 3.