  Financial markets have a vital role in the development of modern society.
They allow the deployment of economic resources. Changes in stock prices
reflect changes in the market. In this study, we focus on predicting stock
prices by deep learning model. This is a challenge task, because there is much
noise and uncertainty in information that is related to stock prices. So this
work uses sparse autoencoders with one-dimension (1-D) residual convolutional
networks which is a deep learning model, to de-noise the data. Long-short term
memory (LSTM) is then used to predict the stock price. The prices, indices and
macroeconomic variables in past are the features used to predict the next day's
price. Experiment results show that 1-D residual convolutional networks can
de-noise data and extract deep features better than a model that combines
wavelet transforms (WT) and stacked autoencoders (SAEs). In addition, we
compare the performances of model with two different forecast targets of stock
price: absolute stock price and price rate of change. The results show that
predicting stock price through price rate of change is better than predicting
absolute prices directly.
