  Stock price prediction has been the focus of a large amount of research but
an acceptable solution has so far escaped academics. Recent advances in deep
learning have motivated researchers to apply neural networks to stock
prediction. In this paper, we propose a convolution-based neural network model
for predicting the future value of the S&P 500 index. The proposed model is
capable of predicting the next-day direction of the index based on the previous
values of the index. Experiments show that our model outperforms a number of
benchmarks achieving an accuracy rate of over 55%.
