  The prediction of stock and foreign exchange (Forex) had always been a hot
and profitable area of study. Deep learning application had proven to yields
better accuracy and return in the field of financial prediction and
forecasting. In this survey we selected papers from the DBLP database for
comparison and analysis. We classified papers according to different deep
learning methods, which included: Convolutional neural network (CNN), Long
Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network
(RNN), Reinforcement Learning, and other deep learning methods such as HAN,
NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable,
model, and results of each article. The survey presented the results through
the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe
ratio, and return rate. We identified that recent models that combined LSTM
with other methods, for example, DNN, are widely researched. Reinforcement
learning and other deep learning method yielded great returns and performances.
We conclude that in recent years the trend of using deep-learning based method
for financial modeling is exponentially rising.
