Deep Neural Networks for Stock Price Prediction

With the economic development of countries in recent years, the influence of the stock market on the global economy has increased. The economic trends of stocks are influenced by many factors, such as information on the financial results of companies, news reports, social media forums, and so on. Stock predictions have also become a popular topic. To date, there has been a great deal of research done by professionals, most of which has used methods related to deep learning. Computer deep learning methods such as Generative Adversarial Networks (GAN) [1], Convolutional Neural Networks (CNN), Deep Reinforcement Learning (DRL), and LSTM (a recurrent neural network) [2] are all highly efficient, adaptable, and portable. However, while many researchers have addressed many of the problems of stock prediction through a variety of deep learning models, such as the barriers to predicting stocks based on the characteristics of the raw data, ignoring the correlation between similar stocks across the stock market [3], and the inefficiency of information. However, a detailed summary and consideration of these areas are still lacking at the moment. This paper examines and summarizes stock prediction algorithms based on deep learning methods and their development process and in the latter part of the paper analyses the future trends of stock prediction algorithms.