Stock Market Prediction computes the process of predicting the future value of the financial stock of the company based on the historical data of the stock. The successfully predicted stock future price will maximize the gain of investors. Stock market prediction utilizes in various fields such as trading, finance, statistics, and computer science. Predicting stock market performance is a very large and profitable area where the stock decision predictors employed deep learning to produce successful predictions. Stock market prediction using deep learning with a large amount of data provide high accuracy.
Multi-Layer Perceptron, Recurrent Neural Network, Long Short Term Memory, Convolutional Neural Network, Deep Belief Network, Autoencoders, and Restricted Boltzmann machine are the deep learning algorithms for predicting the price of the stock market. Long Short Term Memory (LSTM) is a widely used deep learning model for predicting the stock market price. Future directions of stock market prediction using deep learning are combination ensemble learning and deep learning models, new deep learning models for stock market prediction, stock market prediction with multiple data sources, cross-market analysis, algorithmic trading, hybrid model based stock market prediction, and self-attention neural network to predict the stock market.