Prediction of Stock Market Prices of Using Recurrent Neural Network—Long Short-Term Memory

Abstract

The prediction of stock market prices is a complex and tedious task that usually requires human assistance to perform predictions. Because of the highly interdependent nature of the stock prices, traditional batch processing methods cannot be utilized effectively for the analysis of the stock market. Hence, this research work focuses on the prediction of the closing price of a stock using long short-term memory (LSTM) by taking the open price, high price and low price on a specific day as the features for prediction. Stocks of five top technology companies for two years have been analyzed. It also discusses how the performance of the LSTM model varies with different optimizers and suggests the best optimizer for the stock price prediction.