Google Stock Prices Prediction Using Deep Learning

Stock prices are driven by corporate earnings or profit expectations. If a trader thinks that the company's earnings are high or will rise further, they will raise the price of the stock. One way for shareholders to get a return on their investment is to buy low stocks and sell them at high prices. If the company performs poorly and the value of the stock declines, the shareholder will lose some or all of his investment at the time of sale. Therefore, accurate stock price information is important. In this work, we proposed a google stock price prediction model using Recurrent Neural Network (RNN). Previous works on Google stack prediction have used some important techniques and models. Such as deep learning models like Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) has been used for google stock movement prediction. Stock movement prediction on social media data by introducing Stock-Net, Artificial Neural Network (ANN) has also been used with an accuracy score of 0.58. Most of the proposed solutions have limited accuracy. In this paper, we have used Kaggle data of google stock price from the year 2012 to 2016. To predict the stock price of the first two months of 2017 based on the last two months of 2016. For this purpose, we used the Recurrent Neural Network (RNN) as a deep learning model and obtained an accuracy of 87.32%.