  Existing surveys on stock market prediction often focus on traditional
machine learning methods instead of deep learning methods. This motivates us to
provide a structured and comprehensive overview of the research on stock market
prediction. We present four elaborated subtasks of stock market prediction and
propose a novel taxonomy to summarize the state-of-the-art models based on deep
neural networks. In addition, we also provide detailed statistics on the
datasets and evaluation metrics commonly used in the stock market. Finally, we
point out several future directions by sharing some new perspectives on stock
market prediction.
