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

TL;DR

This paper introduces GraphCNNpred, a graph neural network-based deep learning system that predicts stock market indices with improved accuracy and a high Sharpe ratio, demonstrating its effectiveness across multiple indices.

Contribution

The paper presents a novel graph neural network model for stock prediction that leverages diverse data sources, outperforming baseline algorithms in accuracy.

Findings

Prediction accuracy improved by 4% to 15% over baselines

Achieved a Sharpe ratio over 3 in trading simulation

Effective across multiple stock indices

Abstract

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.