Analysis of Stock Market Prediction Models Using Deep Learning

In era of Internet and Big Data where investors are open to share sentiments or opinion about share price for discussion which leads to generate massive amount of unstructured data. This unstructured data further affects the direction of stock price on the basis of investor's emotions and sentiments. In recent years, Deep Learning techniques are extensively explored to predict stock fluctuation using historical data, technical indicators and sentiment analysis. This paper inspects the recent literature in the area of Deep Learning Neural Network, Sentiment Analysis, Data Mining, Fuzzy logic and Machine Learning systems to predict the stock market movement. Due to its nonlinear approach, stock market prediction cannot be relying on traditional methods including fundament analysis and technical analysis. In this paper, we give close eye on summarization enhancements, challenges, future scope and categorized research papers published in this field since 2011. Many recent proposed algorithm's methodologies are studied and presented briefly in this paper. Although discussion was based on predictor techniques, trading strategies and evaluation matrices.