Stock Market Prediction Using Deep Learning

World's economy is driven by the stock market. Investors want to gain a reasonable profit by putting their valuable wealth in suitable stocks thus residing in a secure and win-win situation. Stock market movement is a critical concern which decides the profit or loss for the customers. Fundamental behind market movement is identified as time series. Thus, time series prediction could insist investors to design a suitable strategy during the investments to overcome the risk of erroneous investments. Therefore, a LSTM based model which works with the principle of time series has been adopted in this research work to predict stock prices. Furthermore, recurrent oriented Short-Term Long Memory (LSTM) algorithm has been developed and is employed for predicting the stock price of a company based on the historical prices available. And, next 30 days stocks were predicted. The proposed algorithm is verified with the Apple stock data (AAPL). The obtained results are analyzed through training RMSE (root mean squared error) and the test RMSE. Compared to the related stock prediction approaches, the proposed LSTM based algorithm performs better than its counterparts and shows definite accuracy in predicting the stock prices.