  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.
