Deep Learning for Temporal Stock Prediction: A Comparison

Deep learning has emerging and numerous applications for most areas, including finance, physics and medical science, etc. The deep-based models achieve satisfying performance on those tasks. In this paper, we aim to provide a summary of deep learning-based temporal stock prediction. Specifically, we first categorize the models into three aspects, including CNN-based models, RNN-based models, and hybrid models, by combining CNN and RNN. Then, we detail the preliminary knowledge for deep-based models, including the components of CNN and RNN. Furthermore, we provide an in-depth review of those methods. Finally, we provide a perspective discussion on the stock prediction tasks for further research. We hope our method can be useful for future research and provide a brief introduction to beginners.