STOCK MARKET PRICE PREDICTION USING LONG-SHORT TERM MEMORY (LSTM)

Abstract

Stock index prices predicting is a tough task and, because of various reasons relating to many technological and non - tech reasons, share price knowledge is an extremely difficult, unpredictable and dynamic environment. In parallel to deep learning techniques, a variety of academic experiments from different disciplines to resolve this topic and machine learning techniques are one of the many technologies used. Many machine learning techniques in this field were able to produce acceptable outcomes while it was used in this type of predictions. This project studies stock market price prediction using LSTM model which is applied on Stock index prices historical data along with indications analysis which will be used to achieve more accurate results. In this study, data sets of historical prices of common stock of Agilent Technology, Alcoa Corporation Common Stock and American Airlines Group Common Stock were gathered to achieve this objective, and several tests were carried out using LSTM, the findings were evaluated using RMSE and RMSPE values that guarantee better performance for the LSTM method used.