  In this paper, we compare various approaches to stock price prediction using
neural networks. We analyze the performance fully connected, convolutional, and
recurrent architectures in predicting the next day value of S&P 500 index based
on its previous values. We further expand our analysis by including three
different optimization techniques: Stochastic Gradient Descent, Root Mean
Square Propagation, and Adaptive Moment Estimation. The numerical experiments
reveal that a single layer recurrent neural network with RMSprop optimizer
produces optimal results with validation and test Mean Absolute Error of 0.0150
and 0.0148 respectively.
